System: CT

accounts

System: CT

salesperson

System: CT

relationship manager

System: PD

people

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on an unavailable 'third result' from a prior pipeline, and neither ct.client_360 nor any column-level schema is provided, so no query can be safely generated.",
  "confidence": "uncertain",
  "warnings": [
    "No column details are avail

Keep the ordinary CT user accounts whose salesperson and relationship manager are both known and are different people membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


Independent synthetic fixture values, complete membership and required field types; not production or human usefulness.

Exactly 43 CT accounts with a campaign role and a known web referral domain meet the criteria.

Generation confidence; semantic correctness is only verified by the listed checks.

Generation confidence is not an answer certificate.

The entire question interpretation has not been independently verified.

For exactly the CT accounts of any role assigned t...

accounts IS the canonical CT user table: accounts.id EQUALS user_id. Carries relationship_manager_id and latest_pipedrive_activity_at. A REFERRER is an account referenced by another account's affiliate_id (refer-and-earn referrer when referrer.role='User'; 'referrer(s)' is a CT concept, NEVER the Pipedrive cf_referrer custom field — same suppression as 'partners'). NAME EXPOSURE (ruling 2026-07-08): individual first/last name and company name MAY be SHOWN in result rows to anyone with vault access (the vault boundary IS the authorization) — they are filled server-side post-execution as a display column (client_name / first_name / last_name / company_name placeholders), NEVER read via model-generated SQL (the CT-leg gate rejects a physical read; the model projects `NULL AS <col>`). CONTACT PII stays intentionally NOT exposed: email, telephone/phone and IPs are never shown in results (they remain WHERE-only filters where already allowed).

Project `id AS user_id` (accounts.id is the user key); one row per user already.

System: CT

CT accounts

Onboarding/client dimension keyed by user_id (= accounts.id). Declared (not traded) currencies live here.

Direct user_id; one row per user_id already. Project user_id first.

System: CT

client

Metric match

No predefined metrics are configured for this connection — scored on time clarity, query complexity and column visibility instead

Query complexity

Moderate complexity: 1 JOINs, subqueries

Medium confidence (69%) — implicit time period, moderate complexity

SELECT COALESCE(m.client_count, 0) AS client_count FROM (SELECT COUNT(DISTINCT a.id) AS client_count FROM public.accounts a JOIN public.user_analytics ua ON ua.user_id = a.id WHERE a.campaign_id IS NOT NULL AND NULLIF(ua.came_from_domain, '') IS NOT NULL) m

SELECT
  COALESCE(m.client_count, 0) AS client_count
FROM (
  SELECT
    COUNT(DISTINCT a.id) AS client_count
  FROM public.accounts AS a
  JOIN public.user_analytics AS ua
    ON ua.user_id = a.id
  WHERE
    NOT a.campaign_id IS NULL AND NOT NULLIF(ua.came_from_domain, '') IS NULL
) AS m

'Known referral domain' means a non-empty came_from_domain after blank strings are excluded, treating null as unknown.

Only accounts with a campaign_id set count as assigned to a campaign.

List those 43 client accounts with their campaign and referral domain.

How do these accounts break down by campaign?

How many of these accounts have traded or generated revenue?

This is a single filtered count with no time dimension, so it is a point-in-time membership figure.

The COALESCE guarantees a zero rather than an empty result if no accounts match.

For exactly the CT accounts of any role assigned to a campaign and with a known web referral domain in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

Join accounts to user_analytics by matching user_analytics.user_id to the account's id.

Keep only accounts whose campaign_id is filled in.

Keep only rows where came_from_domain, after ignoring empty strings, has an actual value.

Count the distinct account ids from those rows.

Wrap the count so that if it's missing, show 0 instead.

Summary: Count distinct accounts linked to a campaign that have a non-empty web referral domain in their analytics, and report zero if the count comes back empty.


15 results — first client: Fictional Company 7

List every distinct ordinary CT user accounts with...

System: CT

accounts

Identity placeholder — NEVER select the physical column in SQL (the validator rejects it): project `NULL AS first_name`; the server fills real values post-execution keyed on user_id.

System: CT

first-name

Identity placeholder — NEVER select the physical column in SQL (the validator rejects it): project `NULL AS last_name`; the server fills real values post-execution keyed on user_id.

System: CT

last-name

Identity placeholder — NEVER select the physical column in SQL (the validator rejects it): project `NULL AS telephone`; the server fills real values post-execution keyed on user_id.

System: CT

telephone

System: CT

System: PD

person ID

High confidence (85%) — moderate complexity

Fictional Company 11
cfx (corporate)
+440000000011

Fictional Company 13
cfx (corporate)
+440000000013

Fictional Company 22
cfx (corporate)
+440000000022

Fictional Company 26
unknown
+440000000026

Fictional Company 33
cfx (corporate)
+440000000033

Fictional Company 39
cfx (corporate)
+440000000039

Fictional Company 44
unknown
+440000000044

Fictional Company 52
cfx (corporate)
+440000000052

SELECT a.id AS user_id, NULL AS phone FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id WHERE a.role = 'User' AND NOT a.skip_commission_calculations IS TRUE AND NOT c.completed_registration_at IS NULL AND (NULLIF(a.first_name, '') IS NULL OR NULLIF(a.last_name, '') IS NULL OR NULLIF(a.telephone, '') IS NULL) GROUP BY a.id ORDER BY a.id LIMIT 10000

SELECT
  a.id AS user_id,
  NULL AS phone
FROM public.accounts AS a
JOIN public.clients AS c
  ON c.user_id = a.id
WHERE
  a.role = 'User'
  AND NOT a.skip_commission_calculations IS TRUE
  AND NOT c.completed_registration_at IS NULL
  AND (
    NULLIF(a.first_name, '') IS NULL
    OR NULLIF(a.last_name, '') IS NULL
    OR NULLIF(a.telephone, '') IS NULL
  )
GROUP BY
  a.id
ORDER BY
  a.id
LIMIT 10000

Reading this as the registration-form funnel — started the signup wizard vs finished it (clients.completed_registration_at). If you meant activation (verified & ready to trade), say so.

activation-definition activation-definition-gold onboarding-duration churn-tier-definitions trade-existence-gold-booked

List every distinct ordinary CT user accounts with complete registration but at least one missing or blank first-name, last-name, or telephone field. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

Reads rows from the accounts table joined to clients on clients.user_id matching accounts.id, keeping only rows present in both.

Keeps only accounts where role equals 'User', skip_commission_calculations is not true, and completed_registration_at is filled in.

Keeps rows where first_name, last_name, or telephone is either null or an empty string (empty strings are treated as null via the null-if-blank check).

Removes duplicate user IDs by grouping on the account id, and returns each id as user_id with a constant null phone column.

Sorts user IDs in ascending order and returns at most 10,000 rows.

Summary: Lists account IDs (with a blank phone column) for ordinary user accounts whose registration is complete but whose first name, last name, or telephone is missing or empty, sorted ascending.


86 rows of checked IDs returned for campaign-assigned accounts with a known web referral domain, in descending user_id order

Show the same checked IDs for the CT accounts of a...

System: CT

CT accounts

System: CT

Medium confidence (58%) — implicit time period, complex query

SELECT user_id FROM (SELECT 0 AS sort_key, a.id AS user_id FROM public.accounts AS a JOIN public.user_analytics AS ua ON ua.user_id = a.id WHERE NOT a.campaign_id IS NULL AND NOT ua.came_from_domain IS NULL UNION ALL SELECT 1 AS sort_key, a.id AS user_id FROM public.accounts AS a JOIN public.user_analytics AS ua ON ua.user_id = a.id WHERE NOT a.campaign_id IS NULL AND NOT ua.came_from_domain IS NULL) AS x ORDER BY x.sort_key, CASE WHEN x.sort_key = 0 THEN user_id END ASC, CASE WHEN x.sort_key = 1 THEN user_id END DESC NULLS LAST LIMIT 10000

SELECT
  user_id
FROM (
  SELECT
    0 AS sort_key,
    a.id AS user_id
  FROM public.accounts AS a
  JOIN public.user_analytics AS ua
    ON ua.user_id = a.id
  WHERE
    NOT a.campaign_id IS NULL AND NOT ua.came_from_domain IS NULL
  UNION ALL
  SELECT
    1 AS sort_key,
    a.id AS user_id
  FROM public.accounts AS a
  JOIN public.user_analytics AS ua
    ON ua.user_id = a.id
  WHERE
    NOT a.campaign_id IS NULL AND NOT ua.came_from_domain IS NULL
) AS x
ORDER BY
  x.sort_key,
  CASE WHEN x.sort_key = 0 THEN user_id END ASC,
  CASE WHEN x.sort_key = 1 THEN user_id END DESC NULLS LAST
LIMIT 10000

Checked criteria are: campaign_id is not null and came_from_domain is not null

'Any role' was not filtered further — no role or client_type restriction was applied

Which web referral domains and campaigns do these accounts come from?

The SQL unions the same criteria twice, so each user_id is likely duplicated across the two branches — the distinct member count may be nearer half the row count

Sample rows show most accounts carry client_type cfx (corporate), with pfx (private) also appearing

Show the same checked IDs for the CT accounts of any role assigned to a campaign and with a known web referral domain from the first result, this time in descending user_id order. Keep every member and show only user_id.

Reads public.accounts joined to public.user_analytics by matching ua.user_id to a.id.

Keeps only rows where campaign_id is not null and came_from_domain is not null.

The same filtered query is repeated twice and stacked together with UNION ALL, tagged sort_key 0 for the first copy and 1 for the second.

Ordering: sort_key first, then user_id ascending for the sort_key 0 rows and descending for the sort_key 1 rows (rows where the case doesn't apply sort last).

Returns only the user_id column, capped at 10,000 rows.

Summary: Lists up to 10,000 user IDs from accounts joined to their analytics, keeping only accounts assigned to a campaign with a known referral domain, with the second copy of the same list in descending ID order.


15 ordinary CT user accounts have completed registration but at least one missing or blank name or telephone field.

For exactly the ordinary CT user accounts with com...

System: CT

accounts

System: CT

first-name

System: CT

last-name

System: CT

telephone

System: CT

client

SELECT COALESCE(COUNT(*), 0) AS client_count, NULL AS phone FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id WHERE a.role = 'User' AND NOT a.skip_commission_calculations IS TRUE AND NOT c.completed_registration_at IS NULL AND ((a.first_name IS NULL OR a.first_name = '') OR (a.last_name IS NULL OR a.last_name = '') OR (a.telephone IS NULL OR a.telephone = ''))

SELECT
  COALESCE(COUNT(*), 0) AS client_count,
  NULL AS phone
FROM public.accounts AS a
JOIN public.clients AS c
  ON c.user_id = a.id
WHERE
  a.role = 'User'
  AND NOT a.skip_commission_calculations IS TRUE
  AND NOT c.completed_registration_at IS NULL
  AND (
    (
      a.first_name IS NULL OR a.first_name = ''
    )
    OR (
      a.last_name IS NULL OR a.last_name = ''
    )
    OR (
      a.telephone IS NULL OR a.telephone = ''
    )
  )

Ordinary CT users defined as role = 'User' and not flagged to skip commission calculations.

Complete registration means completed_registration_at is set.

List the 15 accounts with their user IDs and which specific fields are missing.

Which relationship managers or clients are these incomplete-profile accounts attached to?

Each row is one account-client join, so 15 is a count of accounts, not necessarily 15 distinct humans if any user_id repeats.

The check covers null or empty-string values in first name, last name or telephone; whitespace-only entries would not be caught.

activation-definition activation-definition-gold onboarding-duration declared-currency-intent client-type-sti-classes

For exactly the ordinary CT user accounts with complete registration but at least one missing or blank first-name, last-name, or telephone field in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

Joins accounts to clients by matching the client's user_id to the account's id.

Keeps only rows where the account's role is 'User'.

Excludes accounts where skip_commission_calculations is true.

Requires that completed_registration_at is filled in (not null).

Keeps rows where first_name, last_name, or telephone is either null or an empty string.

Counts the matching rows, treating an empty result as zero; also outputs a null phone column.

Summary: Counts ordinary user accounts with complete registration that have at least one missing or blank name or telephone field, returning zero if none match.


43 accounts match the criteria of a campaign assignment plus a known web referral domain

Keep the CT accounts of any role assigned to a cam...

System: CT

CT accounts

System: CT

client

SELECT COUNT(DISTINCT a.id) AS client_count FROM public.accounts a JOIN public.user_analytics ua ON ua.user_id = a.id WHERE a.campaign_id IS NOT NULL AND ua.came_from_domain IS NOT NULL

SELECT
  COUNT(DISTINCT a.id) AS client_count
FROM public.accounts AS a
JOIN public.user_analytics AS ua
  ON ua.user_id = a.id
WHERE
  NOT a.campaign_id IS NULL AND NOT ua.came_from_domain IS NULL

client_count counts distinct account IDs meeting both filters, not distinct underlying clients

'known web referral domain' interpreted as user_analytics.came_from_domain being non-null

List those 43 accounts with their campaign and referral domain

How does this compare when the same criteria are applied to a prior period?

Single-row count, so 43 is the full result — every one of these accounts has both a campaign_id and a non-null came_from_domain.

This counts accounts with a matching analytics record; it says nothing about how recent or active those memberships are.

Keep the CT accounts of any role assigned to a campaign and with a known web referral domain membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.

Join each account to its user_analytics record by matching user_analytics.user_id to the account's id, keeping only accounts with a matching record.

Keep only accounts where campaign_id is filled in — accounts with no campaign assigned are excluded.

Keep only rows where came_from_domain is filled in — rows with a missing referral domain are excluded.

Count the distinct account ids remaining and report that number as client_count; if no rows survive, the count is zero.

Summary: Count distinct account IDs that have a campaign assigned and a known referral domain in their analytics record.


16 results — first client: Synthetic63 Client

Show the same checked IDs for the ordinary CT user...

System: CT

accounts

System: CT

first-name

System: CT

last-name

System: CT

telephone

System: CT

SELECT a.id AS user_id, NULL AS phone FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id WHERE a.role = 'User' AND NOT c.completed_registration_at IS NULL AND ((a.first_name IS NULL OR a.first_name = '') OR (a.last_name IS NULL OR a.last_name = '') OR (a.telephone IS NULL OR a.telephone = '')) GROUP BY a.id ORDER BY a.id DESC NULLS LAST LIMIT 10000

SELECT
  a.id AS user_id,
  NULL AS phone
FROM public.accounts AS a
JOIN public.clients AS c
  ON c.user_id = a.id
WHERE
  a.role = 'User'
  AND NOT c.completed_registration_at IS NULL
  AND (
    (
      a.first_name IS NULL OR a.first_name = ''
    )
    OR (
      a.last_name IS NULL OR a.last_name = ''
    )
    OR (
      a.telephone IS NULL OR a.telephone = ''
    )
  )
GROUP BY
  a.id
ORDER BY
  a.id DESC NULLS LAST
LIMIT 10000

Show the same checked IDs for the ordinary CT user accounts with complete registration but at least one missing or blank first-name, last-name, or telephone field from the first result, this time in descending user_id order. Keep every member and show only user_id.

Reads public.accounts joined to public.clients where clients.user_id matches accounts.id.

Keeps only accounts whose role is exactly 'User' and whose client row has a non-null completed_registration_at.

Requires at least one of the account's first_name, last_name, or telephone to be either null or an empty string.

Outputs the account ID as user_id and a constant null value as phone.

Groups the results by account ID and orders them by ID from highest to lowest, with any null IDs last, returning at most 10,000 rows.

Summary: Lists user account IDs (with a blank phone column) for accounts with role 'User' that have a registration completion timestamp on the joined client row and at least one missing or empty first-name, last-name, or telephone field, sorted by ID descending.


System: CT

client

Direct user_id; group quote metrics to one row per user_id before merge.

System: CT

sell currency

DECLARED (onboarding) sell currencies — the currencies the client SAID at sign-up they wanted to sell. NOT actually-traded (that is trade_bookings.sell_currency/buy_currency) and NOT a residence cohort. An ARRAY (text[]): test membership with `'<CODE>' = ANY(clients.sell_currencies)`; there is NO singular sell_currency column on clients and a scalar comparison fails validation. Declared intent needs NO trade — a client who never traded still has one — so never add a trade-existence predicate to a declared-intent cohort. A generic 'declared/intended to trade <CODE>' is EITHER side: OR this with ct.clients.buy_currencies; '<CODE> book' is the TRADED cohort, a different lens.

System: CT

declared sell currencies

System: CT

System: PD

person ID

Semantic: The query could not preserve the requested meaning or scope. Try the query again in a few moments

churn-tier-definitions declared-currency-intent activation-definition-gold client-type-sti-classes trade-existence-gold-booked

```json
{
  "sql": "",
  "explanation": "The question requires columns like a client's declared sell-currency list and each trade's sell currency (plus ct.client_360/pipedrive federation), but the provided schema lists tables with no columns and no such tables exist; only SELECTs on known schema obj

List every distinct CT client records who booked a non-deleted, non-closed trade in a sell currency absent from their declared sell currencies. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

The query failed to run. This could be due to a database issue or invalid data in your query.


System: CT

client

System: CT

sell currency

System: CT

declared sell currencies

```json
{
  "sql": "",
  "explanation": "Cannot generate: the provided schema lists table names with no columns, and the required ct.client_360 view and sell-currency fields are not available, so I cannot build the required cohort predicate or projection.",
  "confidence": "uncertain",
  "warnings":

For exactly the CT client records who booked a non-deleted, non-closed trade in a sell currency absent from their declared sell currencies in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


15 ordinary CT user accounts have completed registration but at least one missing or blank first-name, last-name, or telephone field.

Keep the ordinary CT user accounts with complete r...

System: CT

accounts

System: CT

first-name

System: CT

last-name

System: CT

telephone

System: CT

client

Moderate complexity: 1 JOINs, window functions

SELECT a.id AS user_id, COUNT(*) OVER () AS client_count, NULL AS phone FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id WHERE a.role = 'User' AND NOT a.skip_commission_calculations IS TRUE AND NOT c.completed_registration_at IS NULL AND (NULLIF(TRIM(a.first_name), '') IS NULL OR NULLIF(TRIM(a.last_name), '') IS NULL OR NULLIF(TRIM(a.telephone), '') IS NULL) ORDER BY a.id DESC NULLS LAST

SELECT
  a.id AS user_id,
  COUNT(*) OVER () AS client_count,
  NULL AS phone
FROM public.accounts AS a
JOIN public.clients AS c
  ON c.user_id = a.id
WHERE
  a.role = 'User'
  AND NOT a.skip_commission_calculations IS TRUE
  AND NOT c.completed_registration_at IS NULL
  AND (
    NULLIF(TRIM(a.first_name), '') IS NULL
    OR NULLIF(TRIM(a.last_name), '') IS NULL
    OR NULLIF(TRIM(a.telephone), '') IS NULL
  )
ORDER BY
  a.id DESC NULLS LAST

'Ordinary' means role = 'User' and excludes accounts flagged to skip commission calculations.

'Complete registration' means completed_registration_at is set, and blank means empty or whitespace-only after trimming.

Which of the 15 accounts are missing a telephone specifically versus a name field?

Which clients do these 15 user IDs belong to, listed per client?

The count of 15 is a window total repeated on each row, matching the 15 rows returned — so all listed accounts are the complete set.

Telephone is populated in several sampled rows (e.g. 11052, 11044), meaning their missing field is a first or last name rather than phone; roughly half the sample has a null phone.

Keep the ordinary CT user accounts with complete registration but at least one missing or blank first-name, last-name, or telephone field membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.

Join accounts to clients where clients.user_id matches accounts.id.

Keep only accounts whose role is 'User' and where skip_commission_calculations is not true.

Require that completed_registration_at on the client row is not null.

Keep rows where trimming first_name, last_name, or telephone and treating an empty string as null gives null — i.e. at least one of those fields is missing or blank.

For each output row show the account id as user_id, the total number of matching rows across the whole result as client_count (zero if there are none), and a phone column that is always null.

Sort by user_id from largest to smallest, putting null ids last.

Summary: List ordinary user accounts with completed registration but at least one missing or blank first-name, last-name, or telephone field, attaching the total row count to each row and a null phone column.


System: CT

client

System: CT

sell currency

System: CT

declared sell currencies

System: CT

```json
{
  "sql": "",
  "explanation": "Cannot generate: the schema context lists table names with no columns, and the required filter (trades whose sell currency is absent from the client's declared sell currencies) plus the 'first result' reference cannot be mapped to known ct.client_360 fields."

Show the same checked IDs for the CT client records who booked a non-deleted, non-closed trade in a sell currency absent from their declared sell currencies from the first result, this time in descending user_id order. Keep every member and show only user_id.


Two private clients with status rejected have broker verification timestamps: 11027 and 11063.

List every distinct private CT client records whos...

System: CT

client

The trade's PAYMENT/LIQUIDITY PARTNER — CT calls this the BROKER (live names include Corpay, Corpay Offline, Currencycloud, Clear Treasury, Moneycorp, Verto, iBan First, Central FX). Resolve the partner NAME by joining: JOIN public.broker_accounts ba ON ba.id = tb.broker_account_id JOIN public.brokers b ON b.id = ba.broker_id, then use b.name as the partner dimension (GROUP BY b.name for a by-partner breakdown; every trade_bookings row carries broker_account_id). NEVER use trade_bookings.platform for a partner/provider/broker breakdown — platform is the booking CHANNEL (web/ios/system/nexus), not a partner — and never alias platform AS payment_partner.

Direct user_id; group trade rows to one per user_id (COUNT(*) FILTER (WHERE status <> 'closed') for the default booked trade count; a settled-gated count ONLY when the user explicitly says settled). Window REALIZED money metrics (settled commission/revenue, daily run-rate, realized QoQ) on completed_at with status='settled' — NOT settlement_at (the deadline) and NOT traded_at (booking date). Use traded_at only for booking-date questions.

System: CT

broker

System: CT

System: PD

person ID

SELECT a.id AS user_id, c.status AS client_status, MIN(ba.verified_at) AS broker_verified_at FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id JOIN public.broker_accounts AS ba ON ba.user_id = a.id WHERE c.type = 'PersonalAccount' AND c.status = 'rejected' AND NOT ba.verified_at IS NULL GROUP BY a.id, c.status ORDER BY a.id ASC

'Private CT client' interpreted as client type 'PersonalAccount' in the clients table

broker verification timestamp exists interpreted as excluding null verified_at values

Show the full client details (names, emails, RMs) for 11027 and 11063

Do these two clients have any closed or active trades since verification?

Both records share the same earliest verification timestamp of 2026-07-31 23:00:00, suggesting a batch or shared verification event.

The query uses the earliest (MIN) broker verification timestamp per account, in case an account has multiple broker records.

activation-definition activation-definition-gold onboarding-duration churn-tier-definitions declared-currency-intent trade-existence-default-booked settlement-is-ct-trade-lifecycle client-type-sti-classes trade-existence-gold-booked

List every distinct private CT client records whose registration status is rejected but whose broker verification timestamp exists. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

Starts from the accounts table and joins clients on clients.user_id = accounts.id, so only clients linked to an account are kept.

Joins broker_accounts on the same user_id link.

Keeps only rows where clients.type is 'PersonalAccount' and clients.status equals rejected.

Excludes rows where the broker account's verified_at is missing (NULL) — a verification timestamp is required.

Groups by account id and client status, and computes the earliest (MIN) broker_verified_at per account.

Returns user_id, client_status, and broker_verified_at, ordered by user_id ascending.

Summary: For each account matched to a client of type PersonalAccount with status rejected that has at least one broker account verification timestamp, return the account id, client status, and the earliest verification time, sorted by id.


System: CT

client

System: CT

sell currency

System: CT

declared sell currencies

```json
{
  "sql": "",
  "explanation": "Cannot generate: the request requires ct.client_360 (not in the provided schema — all tables listed have no columns), plus a 'third result' set and declared sell-currency membership data that are not available.",
  "confidence": "uncertain",
  "warnings": [

Keep the CT client records who booked a non-deleted, non-closed trade in a sell currency absent from their declared sell currencies membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


Exactly 2 private CT client records have rejected registration status but an existing broker verification timestamp.

For exactly the private CT client records whose re...

System: CT

client

System: CT

broker

SELECT COUNT(DISTINCT a.id) AS client_count FROM public.accounts a JOIN public.clients c ON c.user_id = a.id WHERE c.type = 'PersonalAccount' AND c.status = 'rejected' AND EXISTS (SELECT 1 FROM public.broker_accounts ba WHERE ba.user_id = a.id AND ba.verified_at IS NOT NULL)

SELECT
  COUNT(DISTINCT a.id) AS client_count
FROM public.accounts AS a
JOIN public.clients AS c
  ON c.user_id = a.id
WHERE
  c.type = 'PersonalAccount'
  AND c.status = 'rejected'
  AND EXISTS(
    SELECT
      1
    FROM public.broker_accounts AS ba
    WHERE
      ba.user_id = a.id AND NOT ba.verified_at IS NULL
  )

'Private CT client records' interpreted as clients with type = 'PersonalAccount' (personal rather than corporate accounts).

Registration status taken directly from the clients.status field being 'rejected'.

Which 2 clients are these — list their IDs and broker verification timestamps?

The count is of distinct account IDs joined to personal-account client records, so each client is counted once.

The verification check requires at least one non-null verified_at on any linked broker account, regardless of which broker.

activation-definition activation-definition-gold onboarding-duration churn-tier-definitions declared-currency-intent trade-existence-default-booked settlement-is-ct-trade-lifecycle client-type-sti-classes

For exactly the private CT client records whose registration status is rejected but whose broker verification timestamp exists in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

Join accounts to clients on clients.user_id = accounts.id, keeping only matching pairs.

Keep only rows where clients.type = 'PersonalAccount' and clients.status = 'rejected'.

Require, via EXISTS, at least one broker_accounts row with broker_accounts.user_id equal to the account id and verified_at not null (i.e., exclude accounts with no verification timestamp on any broker account).

Return the count of distinct account ids as client_count.

Summary: Count distinct client records whose account has a rejected PersonalAccount client status and at least one broker account with a verification timestamp.


Two private CT clients with rejected registration but an existing broker verification timestamp are returned, in descending user_id order.

Show the same checked IDs for the private CT clien...

System: CT

client

System: CT

broker

System: CT

SELECT a.id AS user_id FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id JOIN public.broker_accounts AS ba ON ba.user_id = a.id WHERE c.type = 'PersonalAccount' AND c.status = 'rejected' AND NOT ba.verified_at IS NULL GROUP BY a.id ORDER BY a.id DESC NULLS LAST LIMIT 10000

Private CT clients were identified as clients of type 'PersonalAccount'

Checked IDs were limited to those with a broker verification timestamp present, i.e. excluding null verified_at

This result is a single-column list of user_id values as requested; the client and client_type columns are not shown per the instruction to display only user_id.

All rows shown carry the same client type, pfx (private).

Show the same checked IDs for the private CT client records whose registration status is rejected but whose broker verification timestamp exists from the first result, this time in descending user_id order. Keep every member and show only user_id.

Reads accounts, clients, and broker_accounts, joining each on the account's id matching their user_id columns.

Keeps only rows where the client's type is 'PersonalAccount'.

Keeps only rows where the client's status is 'rejected'.

Requires that the broker account's verified_at is not null (excludes rows where it is missing).

Groups by the account id, then sorts those ids from highest to lowest, with nulls placed last, and returns at most 10,000 of them.

Summary: From three linked tables, pick account IDs where the client record is a personal account with status rejected and the broker account has a verified-at timestamp, sorted by user_id descending, capped at 10,000 rows.


No complete question-to-answer semantic oracle was run.

SQL checks do not prove the whole question interpretation.

List every distinct Pipedrive people whose owner d...

System: PD

people

System: PD

owner

System: PD

organization

System: CT

System: PD

person ID

0 matching rows — a filter likely did not bind; verify filters. High confidence (85%) — moderate complexity

SELECT DISTINCT p.id AS user_id FROM pipedrive.persons AS p JOIN pipedrive.organizations AS o ON o.id = p.org_id WHERE p.owner_id IS DISTINCT FROM o.owner_id ORDER BY 1 LIMIT 10000

List every distinct Pipedrive people whose owner differs from the owner of their organization. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


Exactly 2 personal (CT) clients are registered as rejected yet have a broker verification timestamp.

Keep the private CT client records whose registrat...

System: CT

client

System: CT

broker

SELECT COUNT(DISTINCT a.id) AS client_count FROM public.accounts a JOIN public.clients c ON c.user_id = a.id WHERE c.status = 'rejected' AND c.type = 'PersonalAccount' AND EXISTS (SELECT 1 FROM public.broker_accounts ba WHERE ba.user_id = a.id AND ba.verified_at IS NOT NULL)

SELECT
  COUNT(DISTINCT a.id) AS client_count
FROM public.accounts AS a
JOIN public.clients AS c
  ON c.user_id = a.id
WHERE
  c.status = 'rejected'
  AND c.type = 'PersonalAccount'
  AND EXISTS(
    SELECT
      1
    FROM public.broker_accounts AS ba
    WHERE
      ba.user_id = a.id AND NOT ba.verified_at IS NULL
  )

'Private CT client' interpreted as accounts with client type = PersonalAccount and status = rejected

Broker verification means a broker_accounts row with verified_at populated

List the client IDs, names and registration dates for those 2 rejected-but-verified clients

Do any of these clients have subsequent trades or deposits despite rejection?

This is a single aggregate figure; there is no breakdown by dimension in this result.

The two states are contradictory on paper — rejected registration alongside a completed broker verification — so these records may merit a data-quality review.

Keep the private CT client records whose registration status is rejected but whose broker verification timestamp exists membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.

Reads the accounts table and joins each account to the clients table by matching clients.user_id to accounts.id, keeping only matching pairs.

Filters to client rows whose status is 'rejected' and whose type is 'PersonalAccount'; anything else is excluded.

Requires that a matching row exists in the broker_accounts table with the same user_id and with verified_at filled in (not null).

Counts the distinct account IDs that survive all filters and reports that number as client_count; if no rows qualify, the count is zero.

Summary: Count distinct account IDs that appear as clients with status 'rejected' and type 'PersonalAccount', and that have at least one broker account row with a verification timestamp present.


System: PD

people

System: PD

owner

System: PD

organization

System: CT

client

Sql: The generated SQL could not complete the requested query. Retry after CT data is available.

SELECT COUNT(DISTINCT a.id)::bigint AS client_count FROM public.accounts a

SELECT cc.user_id, COUNT(DISTINCT pe.id) FILTER (WHERE pe.owner_id IS DISTINCT FROM o.owner_id) AS client_count FROM ct.client_360 cc LEFT JOIN pipedrive.persons_enriched pe ON pe.ct_id = cc.user_id LEFT JOIN pipedrive.organizations o ON o.id = pe.org_id GROUP BY cc.user_id

For exactly the Pipedrive people whose owner differs from the owner of their organization in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

CT data unavailable: the CT leg returned no join key 'user_id'; a whole-book result cannot answer the selected cohort


System: PD

people

System: PD

owner

System: PD

organization

System: CT

```json
{
  "sql": "",
  "explanation": "I cannot generate this query confidently: the tables' column lists are empty in the provided schema, and the curated knowledge confirms only owner_id on persons/persons_enriched and owner_name on organizations — there is no confirmed organization owner_id to

Show the same checked IDs for the Pipedrive people whose owner differs from the owner of their organization from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

organization

System: CT

System: PD

person ID

Sql: The generated SQL could not complete the requested query. Rephrase to only request data you have access to

SELECT DISTINCT COALESCE(user_id, pe.id) AS user_id FROM persons_enriched AS pe JOIN deals AS d ON d.person_id = pe.id WHERE d.org_id IS DISTINCT FROM pe.org_id ORDER BY 1 LIMIT 10000

List every distinct Pipedrive people with a deal assigned to a different organization from their person record. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

The generated query couldn't be validated. This might be due to security restrictions or invalid SQL.


System: PD

people

System: PD

owner

System: PD

organization

System: CT

client

```json
{
  "sql": "",
  "explanation": "This question is entirely Pipedrive-side (person owner vs organization-membership owner) and this CT connection has no Pipedrive tables or owner columns; the gold/Pipedrive leg owns that join and count.",
  "confidence": "uncertain",
  "warnings": ["No Pipedr

Keep the Pipedrive people whose owner differs from the owner of their organization membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: PD

stage

System: CT

System: PD

person ID

```json
{
  "sql": "",
  "explanation": "The provided schema lists table names with no columns, and neither a 'lead qualification date' field nor a deals 'stage' column is defined anywhere in the schema or curated knowledge, so a trustworthy query cannot be constructed.",
  "confidence": "uncertain"

List every distinct Pipedrive people who have a lead qualification date but no deal in the Negotiation stage. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


Per-user client_count returned for 45 users, with the first 12 rows showing mostly 1 matching person and some zeros

Keyed merge and authoritative-source checks.

Keys, merge grain, and source membership were checked.

For exactly the Pipedrive people with a deal assig...

System: PD

people

System: PD

organization

System: CT

client

Query times are separate. Source refresh time is unknown unless supplied.

SELECT c.user_id, COUNT(DISTINCT pe.id) AS client_count FROM ct.client_360 c LEFT JOIN pipedrive.persons_enriched pe ON pe.ct_id = c.user_id LEFT JOIN pipedrive.deals d ON d.person_id = pe.id AND d.org_id IS DISTINCT FROM pe.org_id GROUP BY 1

SELECT a.id AS user_id FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id WHERE a.role = 'User' AND NOT a.skip_commission_calculations IS TRUE GROUP BY 1 LIMIT 10000

CT-realized and Pipedrive pipeline measures are kept separate; not summed across sources.

client_count counts distinct Pipedrive persons with at least one deal whose org_id differs from the person's org_id (nulls treated as distinct via IS DISTINCT FROM)

Row set driven by the CT ledger membership: accounts with role 'User' and excluding those with skip_commission_calculations set to true

List the specific Pipedrive persons and their mismatched deals for each user_id

Each row counts distinct Pipedrive persons whose deal org differs from the person record's org; sample values are 1s and 0s, but 45 rows were returned so totals or distribution can't be inferred from the sample alone

The LEFT JOIN means users with no qualifying persons still appear with client_count 0, e.g. 11007 and 11014 in the sample

For exactly the Pipedrive people with a deal assigned to a different organization from their person record in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

[CT Ledger] Reads accounts joined to clients on matching user id, keeping only accounts with role = 'User' and excluding any where skip_commission_calculations is true; returns distinct account ids, capped at 10,000 row…

[Pipedrive] For each user in client_360, attaches matching persons_enriched rows via ct_id = user_id.

[Pipedrive] Then attaches deals matching on person_id, keeping only deals whose org_id is distinct from the person's org_id (deals at the same organization are excluded); the join keeps person rows even with no such dea…

[Pipedrive] Groups by user and counts distinct person ids as client_count.

Merged: the two results are combined per user_id, so each user keeps the ledger's membership and gets their client_count, including zero when the count is empty.

Summary: For each user kept by the CT Ledger half, the Pipedrive half counts distinct persons whose assigned deal belongs to a different organization than the person record; the halves are joined per user_id, with zero kept if no one matches.


System: PD

people

System: PD

stage

System: CT

client

```json
{
  "sql": "",
  "explanation": "The provided schema lists table names only (no columns), so I cannot resolve 'lead qualification date', a Negotiation stage flag, or a user_id join key between ct.client_360 and pipedrive tables; generating SQL would require inventing columns.",
  "confidence

For exactly the Pipedrive people who have a lead qualification date but no deal in the Negotiation stage in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

organization

System: CT

SELECT DISTINCT COALESCE(user_id, pe.id) AS user_id FROM persons_enriched AS pe JOIN deals AS d ON d.person_id = pe.id WHERE d.org_id IS DISTINCT FROM pe.org_id ORDER BY 1 DESC LIMIT 10000

Show the same checked IDs for the Pipedrive people with a deal assigned to a different organization from their person record from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

stage

System: CT

```json
{
  "sql": "",
  "explanation": "The request refers to 'the first result' (a prior query's checked IDs) and depends on columns like a lead qualification date and deal stage, but the provided schema lists no column details, so the cohort cannot be reconstructed.",
  "confidence": "uncertain",

Show the same checked IDs for the Pipedrive people who have a lead qualification date but no deal in the Negotiation stage from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

organization

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on a prior ('third') result set and table columns that are not defined in the provided schema, so a reliable query cannot be constructed.",
  "confidence": "uncertain",
  "warnings": [
    "References 'the third result' from an earlier con

```json
{
  "sql": "",
  "explanation": "This question's cohort (Pipedrive people whose deal is assigned to a different organization than their person record) is defined entirely by Pipedrive-side data, which does not exist on this CT connection.",
  "confidence": "uncertain",
  "warnings": ["No Pip

Keep the Pipedrive people with a deal assigned to a different organization from their person record membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: PD

stage

System: CT

client

```json
{
  "sql": "",
  "explanation": "Cannot generate: the schema context lists no columns for any table (no lead-qualification-date field or deal stage column is defined), and the request depends on 'the third result' — an intermediate result set from prior queries that was not provided.",
  "co

Keep the Pipedrive people who have a lead qualification date but no deal in the Negotiation stage membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: CT

System: PD

person ID

SELECT DISTINCT a.person_id AS user_id FROM activities AS a WHERE a.type = 'note' AND a.done = TRUE AND NOT a.person_id IS NULL AND a.deal_id IS NULL ORDER BY 1 LIMIT 10000

List every distinct Pipedrive people with an active note pinned to the person but not pinned to a deal. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


54 distinct Pipedrive people have at least one activity whose due date precedes its creation date.

List every distinct Pipedrive people with an activ...

System: PD

people

System: PD

activity

System: PD

due date

System: CT

System: PD

person ID

Simple single-table query with basic aggregation

High confidence (100%) — clear time period, simple query

SELECT DISTINCT a.person_id AS user_id FROM pipedrive.activities AS a WHERE NOT a.person_id IS NULL AND NOT a.due_date IS NULL AND NOT a.add_time IS NULL AND a.due_date < CAST(a.add_time AS DATE) ORDER BY 1 LIMIT 10000

'Person ID' used as the identifier, as specified for Pipedrive-only people.

How many backdated activities does each of these people have?

Which activity types are most often backdated for these people?

Result excludes activities with a null due date, add_time or person_id, so backdated activities lacking these fields are not counted.

Sample rows are person IDs only; the full 54-row list continues beyond those shown.

List every distinct Pipedrive people with an activity whose due date precedes its creation date. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

Reads the pipedrive.activities table only.

Keeps only rows where person_id, due_date, and add_time are all filled in (rows missing any of these are excluded).

Keeps only rows where the due date is before the activity's creation date, comparing due_date to add_time converted to a date.

Returns each matching person_id once (duplicates removed), named user_id in the output.

Sorts the results by user_id in ascending order and caps the output at 10,000 rows.

Summary: Finds each distinct person ID that appears on an activity whose due date is earlier than the date the activity was created, and lists those IDs in ascending order.


System: PD

people

System: CT

client

```json
{
  "sql": "",
  "explanation": "The provided schema lists no column definitions for persons, notes, or any ct.* tables, so I cannot confidently reference note-pinning columns (person_id, deal_id) or map a user_id join key from ct.client_360.",
  "confidence": "uncertain",
  "warnings": ["Ta

For exactly the Pipedrive people with an active note pinned to the person but not pinned to a deal in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: CT

SELECT DISTINCT a.person_id AS user_id FROM activities AS a WHERE a.type = 'note' AND a.done = TRUE AND a.person_id IS NOT NULL AND a.deal_id IS NULL ORDER BY 1 DESC LIMIT 10000

SELECT DISTINCT
  a.person_id AS user_id
FROM activities AS a
WHERE
  a.type = 'note' AND a.done = TRUE AND NOT a.person_id IS NULL AND a.deal_id IS NULL
ORDER BY
  1 DESC
LIMIT 10000

Show the same checked IDs for the Pipedrive people with an active note pinned to the person but not pinned to a deal from the first result, this time in descending user_id order. Keep every member and show only user_id.


54 distinct Pipedrive people have an activity whose due date precedes its creation date.

For exactly the Pipedrive people with an activity ...

System: PD

people

System: PD

activity

System: PD

due date

System: CT

client

SELECT COUNT(DISTINCT a.person_id) AS client_count FROM pipedrive.activities AS a WHERE NOT a.person_id IS NULL AND NOT a.due_date IS NULL AND NOT a.add_time IS NULL AND a.due_date < CAST(a.add_time AS DATE)

due_date compared as a date against the creation date part of add_time, ignoring any time-of-day component

excluding activities with null person_id, due_date or add_time

List those 54 people and their anomalous activity counts

Which activity types or users create most of these backdated activities?

This points to backdated or data-entry-dated activities rather than genuine scheduling.

Count is of people, not activities, so some may have multiple such anomalies.

For exactly the Pipedrive people with an activity whose due date precedes its creation date in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

Reads the activities table in the pipedrive schema.

Keeps only rows where person_id, due_date, and add_time are all present (not null).

Requires that due_date comes before add_time, after converting add_time to a date.

Counts the distinct person_id values from those rows and labels the result client_count; if no rows qualify, the count is zero.

Summary: Counts distinct non-null people whose activity due date is earlier than the date the activity was created, among rows where both dates exist.


System: PD

people

System: CT

client

```json
{
  "sql": "",
  "explanation": "The provided schema lists tables with no columns, and the request depends on an unavailable 'third result' plus pinning semantics (people with active notes, excluding deal members) that cannot be mapped to known columns.",
  "confidence": "uncertain",
  "warn

Keep the Pipedrive people with an active note pinned to the person but not pinned to a deal membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


54 distinct person IDs have activities due before they were created, listed in descending user_id order.

Show the same checked IDs for the Pipedrive people...

System: PD

people

System: PD

activity

System: PD

due date

System: CT

SELECT DISTINCT a.person_id AS user_id FROM pipedrive.activities AS a WHERE NOT a.person_id IS NULL AND NOT a.due_date IS NULL AND NOT a.add_time IS NULL AND a.due_date < CAST(a.add_time AS DATE) ORDER BY 1 DESC LIMIT 10000

user_id is the Pipedrive person_id from activities, not a system user

Only activities with non-null due_date and add_time are considered

Show the names and organisations for these 54 person IDs

How many back-dated activities does each of these person IDs have?

All 54 rows are returned, so this is the complete set, matching the first result's membership.

Only user_id is shown, per request; no names or activity details included.

Show the same checked IDs for the Pipedrive people with an activity whose due date precedes its creation date from the first result, this time in descending user_id order. Keep every member and show only user_id.

Keeps only rows where person_id, due_date, and add_time are all filled in.

Keeps rows where due_date (compared as a date) comes before add_time converted to a date.

Returns each distinct person_id once, labeled user_id.

Sorts user_id from highest to lowest and limits output to 10000 rows.

Summary: From the activities table, list distinct non-null person IDs for activities whose due date is before their creation date, sorted descending, capped at 10000 rows.


List every distinct Pipedrive people with an open ...

System: PD

people

System: CT

System: PD

person ID

Time clarity

Explicit time period specified: 'september'

0 matching rows — a filter likely did not bind; verify filters. High confidence (100%) — clear time period, simple query

SELECT DISTINCT d.person_id AS user_id FROM pipedrive.deals AS d WHERE d.status = 'open' AND d.expected_close_date < CAST('2026-09-01' AS DATE) AND d.close_time IS NULL ORDER BY 1 ASC LIMIT 10000

churn-tier-definitions activation-definition-gold trade-existence-gold-booked calendar-window-defaults

List every distinct Pipedrive people with an open deal expected to close before 1 September 2026 but with no actual close timestamp. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


54 Pipedrive people have an activity whose due date precedes its creation date.

Keep the Pipedrive people with an activity whose d...

System: PD

people

System: PD

activity

System: PD

due date

System: CT

client

Counts distinct people with at least one anomalous activity; people with no activity, person_id, due_date or add_time are excluded.

Anomaly defined strictly as due_date earlier than the date part of add_time.

List those 54 people and how many anomalous activities each has.

How are these anomalous activities distributed by activity type?

Which users or pipelines created most of these backdated activities?

This is a data-quality anomaly count rather than a business metric — due dates logged before the activity was created typically indicate backdated or mis-imported records.

The count covers all such people across the activities table, so a single person with multiple anomalies is counted once.

Keep the Pipedrive people with an activity whose due date precedes its creation date membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.

Reads the pipedrive.activities table, each row being one activity.

Excludes rows where person_id, due_date, or add_time is missing.

Keeps only activities whose due_date is earlier than the date part of add_time (the creation timestamp).

Counts the distinct person_id values among those rows and labels the result client_count; if no rows qualify, the count is zero.

Summary: Count distinct person IDs from the pipedrive activities table where the activity's due date is earlier than the same row's creation date, ignoring rows missing either value.


System: PD

people

System: PD

organization

System: PD

company introducer

System: CT

System: PD

person ID

```json
{
  "sql": "",
  "explanation": "The available schema lists no columns for any table, and no documented field captures a company's 'introducer' organization, so the required predicate cannot be mapped to any known column.",
  "confidence": "uncertain",
  "warnings": [
    "No column inventor

List every distinct Pipedrive people at an organization whose company introducer is another known organization. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

organization

System: PD

company introducer

System: CT

client

```json
{
  "sql": "",
  "explanation": "The schema context lists no columns for ct.client_360 or the other ct.* tables, and no 'company introducer' field exists on pipedrive.organizations, so the membership condition cannot be mapped to real columns.",
  "confidence": "uncertain",
  "warnings": ["N

For exactly the Pipedrive people at an organization whose company introducer is another known organization in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: CT

client

Schema: The query refers to a table or column that is unavailable on the selected connection. Try the query again in a few moments

SELECT CAST(NULL AS BIGINT) AS client_count LIMIT 10000

SELECT c.user_id, COUNT(DISTINCT p.id) AS client_count FROM ct.client_360 c LEFT JOIN pipedrive.persons p ON p.id = c.person_id LEFT JOIN pipedrive.deals d ON d.person_id = p.id AND d.status = 'open' AND d.expected_close_date < DATE '2026-09-01' AND d.actual_close_time IS NULL GROUP BY 1

For exactly the Pipedrive people with an open deal expected to close before 1 September 2026 but with no actual close timestamp in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

organization

System: PD

company introducer

System: CT

```json
{
  "sql": "",
  "explanation": "The request depends on a prior result ('first result') and columns (e.g., company introducer) not present in the provided schema, so no safe SELECT can be generated.",
  "confidence": "uncertain",
  "warnings": ["Tables were provided without column definition

Show the same checked IDs for the Pipedrive people at an organization whose company introducer is another known organization from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: CT

SELECT DISTINCT d.person_id AS user_id FROM pipedrive.deals AS d WHERE d.status = 'open' AND d.expected_close_date < CAST('2026-09-01' AS DATE) AND d.close_time IS NULL ORDER BY 1 DESC LIMIT 10000

Show the same checked IDs for the Pipedrive people with an open deal expected to close before 1 September 2026 but with no actual close timestamp from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

organization

System: PD

company introducer

System: CT

client

Sql: The generated SQL could not complete the requested query. Try the query again in a few moments

SELECT COUNT(DISTINCT public.accounts.id)::bigint AS client_count FROM public.accounts

SELECT pe.id AS user_id, COUNT(DISTINCT pe.id) OVER () AS client_count FROM persons_enriched pe

Keep the Pipedrive people at an organization whose company introducer is another known organization membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: CT

client

```json
{
  "sql": "",
  "explanation": "The requested query needs pipedrive.people/deals columns (person id, deal expected close date, status, actual close timestamp), but the provided schema lists no columns for any table, and the reference to 'the third result' cannot be resolved.",
  "confidence

Keep the Pipedrive people with an open deal expected to close before 1 September 2026 but with no actual close timestamp membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: PD

organization

System: PD

employee count

System: PD

people count

System: CT

System: PD

person ID

```json
{
  "sql": "",
  "explanation": "The provided schema lists table names only, with no columns; I cannot confirm the column names for an organization's employee count or people count on pipedrive.organizations.",
  "confidence": "uncertain",
  "warnings": ["Schema context contains no column de

List every distinct Pipedrive people whose organization has a known positive employee count but an unknown people count. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

organization

System: PD

employee count

System: PD

people count

System: CT

client

SELECT c360.user_id, COUNT(DISTINCT pe.id)::int AS client_count FROM ct.client_360 c360 JOIN pipedrive.persons_enriched pe ON pe.ct_id = c360.user_id JOIN pipedrive.organizations o ON o.id = pe.org_id WHERE o.employee_count > 0 AND o.people_count IS NULL GROUP BY c360.user_id

For exactly the Pipedrive people whose organization has a known positive employee count but an unknown people count in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: CT

phone

System: CT

System: PD

person ID

Schema: The generated query referenced Pipedrive columns/tables that don't exist (persons.contact_preferences, ct.persons), and a corrected query could not be produced. Rather than run an invented column, this answer was stopped — try naming the exact fields you need. Name a measure and source available on this connection.

SELECT DISTINCT p.id AS user_id, NULL AS email, NULL AS phone FROM ct.persons AS p WHERE 'email' = ANY(p.contact_preferences) AND NOT ('phone' = ANY(p.contact_preferences)) ORDER BY 1 ASC LIMIT 10000

SELECT DISTINCT
  p.id AS user_id,
  NULL AS email,
  NULL AS phone
FROM ct.persons AS p
WHERE
  'email' = ANY(
    p.contact_preferences
  )
  AND NOT (
    'phone' = ANY(
      p.contact_preferences
    )
  )
ORDER BY
  1 ASC
LIMIT 10000

```json
{
  "sql": "",
  "explanation": "The request depends on a 'contact preference' array column which is not documented in the provided schema, and no column inventory is available for persons_enriched.",
  "confidence": "uncertain",
  "warnings": ["No schema columns are listed for persons_enric

```json
{
  "sql": "",
  "explanation": "No table in this warehouse carries a contact-preference array or any email/phone column, so this filter cannot be expressed in SQL.",
  "confidence": "uncertain",
  "warnings": ["Contact preferences (email vs phone) do not exist on pipedrive.persons (columns:

List every distinct Pipedrive people whose contact preference array includes email but not phone. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: CT

phone

System: CT

client

```json
{
  "sql": "",
  "explanation": "Cannot generate: the schema listing provides no columns for pipedrive.persons (or any table), so the contact-preference array field needed to test 'includes email but not phone' cannot be identified.",
  "confidence": "uncertain",
  "warnings": [
    "All tab

For exactly the Pipedrive people whose contact preference array includes email but not phone in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

organization

System: PD

employee count

System: PD

people count

System: CT

SELECT DISTINCT p.id AS user_id FROM pipedrive.persons AS p JOIN pipedrive.organizations AS o ON o.id = p.org_id WHERE o.employee_count > 0 AND o.people_count IS NULL ORDER BY 1 DESC LIMIT 10000

Show the same checked IDs for the Pipedrive people whose organization has a known positive employee count but an unknown people count from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: CT

phone

System: CT

SELECT DISTINCT p.id AS user_id, NULL AS email, NULL AS phone FROM ct.persons AS p WHERE 'email' = ANY(p.contact_preferences) AND NOT ('phone' = ANY(p.contact_preferences)) ORDER BY 1 DESC LIMIT 10000

SELECT DISTINCT
  p.id AS user_id,
  NULL AS email,
  NULL AS phone
FROM ct.persons AS p
WHERE
  'email' = ANY(
    p.contact_preferences
  )
  AND NOT (
    'phone' = ANY(
      p.contact_preferences
    )
  )
ORDER BY
  1 DESC
LIMIT 10000

```json
{
  "sql": "",
  "explanation": "The requested filter depends on a contact_preferences array that does not exist on pipedrive.persons (columns: add_time, ct_id, id, name, org_id, owner_id, update_time) or any other relation in this warehouse, so the query cannot be generated without inventin

Show the same checked IDs for the Pipedrive people whose contact preference array includes email but not phone from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

organization

System: PD

employee count

System: PD

people count

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request refers to 'the third result' from a prior conversation and schema details that were not provided, so a faithful query cannot be constructed.",
  "confidence": "uncertain",
  "warnings": ["No prior results or intermediate result set are available i

Keep the Pipedrive people whose organization has a known positive employee count but an unknown people count membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: CT

phone

System: CT

client

SELECT COUNT(DISTINCT p.id) AS client_count, NULL AS email, NULL AS phone FROM ct.persons AS p WHERE 'email' = ANY(p.contact_preferences) AND NOT ('phone' = ANY(p.contact_preferences))

SELECT
  COUNT(DISTINCT p.id) AS client_count,
  NULL AS email,
  NULL AS phone
FROM ct.persons AS p
WHERE
  'email' = ANY(
    p.contact_preferences
  )
  AND NOT (
    'phone' = ANY(
      p.contact_preferences
    )
  )

```json
{
  "sql": "",
  "explanation": "Neither pipedrive.persons nor any other table on this connection has a contact_preferences (or email/phone preference) column, so the 'contact preference includes email but not phone' membership cannot be derived or reused from the previous draft.",
  "confid

Keep the Pipedrive people whose contact preference array includes email but not phone membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

Declared intended trading frequency captured at onboarding (synced onward to Pipedrive).

System: CT

trade frequency

System: CT

System: PD

person ID

```json
{
  "sql": "",
  "explanation": "Cannot generate: trade frequency is a Pipedrive custom field whose storage location (raw column vs. field_dict/option_dict join key) is not described in the provided schema, and persons_enriched columns are not enumerated.",
  "confidence": "uncertain",
  "wa

List every distinct Pipedrive people who declared more than one trade frequency. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

organization

System: CT

System: PD

person ID

```json
{
  "sql": "",
  "explanation": "The schema provides no column or table representing an organization's booking or hedging policy, so the cohort cannot be derived from the available gold/pipedrive schema.",
  "confidence": "uncertain",
  "warnings": ["No 'booking policy' or 'hedging policy' c

List every distinct Pipedrive people whose organization declares more than one booking or hedging policy. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: CT

trade frequency

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on a prior result ('first checked result') and a trade-frequency column in ct.client_360, but no columns are defined in the provided schema, so I cannot construct a valid query.",
  "confidence": "uncertain",
  "warnings": ["No column info

For exactly the Pipedrive people who declared more than one trade frequency in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

organization

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on a prior result and a 'booking or hedging policy' field, but no such column exists in the provided schema (ct.client_360 columns are not listed and no policy field is documented anywhere).",
  "confidence": "uncertain",
  "warnings": [

For exactly the Pipedrive people whose organization declares more than one booking or hedging policy in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: CT

trade frequency

System: CT

```json
{
  "sql": "",
  "explanation": "Cannot generate: the request references 'the first result' (a prior query not available here), and the schema provides no column names for ct.client_360, so 'declared more than one trade frequency' cannot be resolved.",
  "confidence": "uncertain",
  "warning

Show the same checked IDs for the Pipedrive people who declared more than one trade frequency from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: CT

trade frequency

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on a prior ('third') result set and a 'trade frequency membership' concept that are not defined in the provided schema or context, so no safe SQL can be generated.",
  "confidence": "uncertain",
  "warnings": [
    "No prior query results

Keep the Pipedrive people who declared more than one trade frequency membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: PD

organization

System: CT

Semantic: The query could not preserve the requested meaning or scope. Try using specific metric names like 'MRR' or 'revenue'

```json
{
  "sql": "",
  "explanation": "The schema provides no columns for booking or hedging policy on organizations, and tables are listed without columns, so the filter cannot be built.",
  "confidence": "uncertain",
  "warnings": ["No 'booking/hedging policy' column exists in the provided schem

```json
{
  "sql": "",
  "explanation": "The provided schema lists table names only (no columns), and there is no identifiable column or field-dictionary entry defining 'booking or hedging policy' on organizations, so I cannot confidently map the question to a real column.",
  "confidence": "uncerta

Show the same checked IDs for the Pipedrive people whose organization declares more than one booking or hedging policy from the first result, this time in descending user_id order. Keep every member and show only user_id.

I couldn't understand that query. Try rephrasing it or using simpler terms.


System: PD

people

System: PD

organization

System: CT

client

SELECT accounts.id AS user_id FROM public.accounts LIMIT 0

Keep the Pipedrive people whose organization declares more than one booking or hedging policy membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


List every distinct Pipedrive people at an organiz...

System: PD

people

System: PD

organization

System: PD

LinkedIn

System: PD

website

System: CT

System: PD

person ID

SELECT DISTINCT p.id AS user_id FROM pipedrive.persons AS p JOIN pipedrive.organizations AS o ON o.id = p.org_id WHERE COALESCE(o.linkedin, '') <> '' AND (o.website IS NULL OR o.website = '') ORDER BY 1 LIMIT 10000

SELECT DISTINCT
  p.id AS user_id
FROM pipedrive.persons AS p
JOIN pipedrive.organizations AS o
  ON o.id = p.org_id
WHERE
  COALESCE(o.linkedin, '') <> '' AND (
    o.website IS NULL OR o.website = ''
  )
ORDER BY
  1
LIMIT 10000

List every distinct Pipedrive people at an organization with a LinkedIn page and no website. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

activity

System: PD

owner

System: CT

System: PD

person ID

SELECT DISTINCT a.person_id AS user_id FROM activities AS a JOIN deals AS d ON d.id = a.deal_id WHERE NOT a.person_id IS NULL AND a.assigned_to_name IS DISTINCT FROM d.owner_name ORDER BY 1 LIMIT 10000

List every distinct Pipedrive people with an activity assigned to someone other than the owner of its deal. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

organization

System: PD

LinkedIn

System: PD

website

System: CT

client

SELECT COUNT(DISTINCT a.id)::numeric AS client_count FROM public.accounts a JOIN public.clients c ON c.user_id = a.id WHERE a.role = 'User' AND NOT a.skip_commission_calculations IS TRUE

SELECT c.user_id, COALESCE(COUNT(DISTINCT pe.id) FILTER (WHERE o.linkedin IS NOT NULL AND o.website IS NULL), 0) AS client_count FROM ct.client_360 c LEFT JOIN pipedrive.persons_enriched pe ON pe.ct_id = c.user_id LEFT JOIN pipedrive.organizations o ON o.id = pe.org_id GROUP BY 1

For exactly the Pipedrive people at an organization with a LinkedIn page and no website in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

activity

System: PD

owner

System: CT

client

```json
{
  "sql": "",
  "explanation": "I cannot confidently generate this query: the request references a prior ('first checked') result I don't have, and the schema lists no columns for ct.client_360, so I cannot build the required gold-leg projection of user_id plus gold fields or correctly reso

For exactly the Pipedrive people with an activity assigned to someone other than the owner of its deal in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

organization

System: PD

LinkedIn

System: PD

website

System: CT

SELECT DISTINCT p.id AS user_id FROM pipedrive.persons AS p JOIN pipedrive.organizations AS o ON o.id = p.org_id WHERE COALESCE(o.linkedin, '') <> '' AND (o.website IS NULL OR o.website = '') ORDER BY 1 DESC LIMIT 10000

SELECT DISTINCT
  p.id AS user_id
FROM pipedrive.persons AS p
JOIN pipedrive.organizations AS o
  ON o.id = p.org_id
WHERE
  COALESCE(o.linkedin, '') <> '' AND (
    o.website IS NULL OR o.website = ''
  )
ORDER BY
  1 DESC
LIMIT 10000

Show the same checked IDs for the Pipedrive people at an organization with a LinkedIn page and no website from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

activity

System: PD

owner

System: CT

SELECT DISTINCT a.person_id AS user_id FROM activities AS a JOIN deals AS d ON d.id = a.deal_id WHERE NOT a.person_id IS NULL AND a.assigned_to_name IS DISTINCT FROM d.owner_name ORDER BY 1 DESC LIMIT 10000

Show the same checked IDs for the Pipedrive people with an activity assigned to someone other than the owner of its deal from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

activity

System: PD

owner

System: CT

client

```json
{
  "sql": "",
  "explanation": "The question depends on 'the third result' from a prior multi-step query chain whose definition (the specific deal-membership set of people) is not available in this request, so I cannot confidently build the filter.",
  "confidence": "uncertain",
  "warnings

Keep the Pipedrive people with an activity assigned to someone other than the owner of its deal membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


45 user_id rows returned, with the top rows shown carrying a client_count of 1 and a few at zero

Keep the Pipedrive people at an organization with ...

System: PD

people

System: PD

organization

System: PD

LinkedIn

System: PD

website

System: CT

client

SELECT c.user_id, COUNT(DISTINCT pe.id)::int AS client_count FROM ct.client_360 c LEFT JOIN pipedrive.persons_enriched pe ON pe.ct_id = c.user_id AND pe.pd_is_ct_employee IS NOT TRUE LEFT JOIN pipedrive.organizations o ON o.id = pe.org_id AND o.linkedin IS NOT NULL AND (o.website IS NULL OR o.website = '') GROUP BY 1

SELECT a.id AS user_id FROM public.accounts AS a JOIN public.clients AS c ON c.user_id = a.id WHERE a.role = 'User' AND NOT a.skip_commission_calculations IS TRUE LIMIT 10000

client_count counts Pipedrive persons joined to the user's organisations with a non-null LinkedIn page and an empty or null website

Excludes persons flagged as CT employees (pd_is_ct_employee is not true)

Which users have the highest client_count?

List the Pipedrive person IDs behind each user_id in this group

client_count is a per-user_id count of distinct Pipedrive person IDs; 45 rows counts users, not clients

The top rows shown are mostly 1 with occasional 0s; the remaining rows are not visible so no total client_count can be stated

Keep the Pipedrive people at an organization with a LinkedIn page and no website membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.

[Gold / Pipedrive] Start from ct.client_360 and attach matching rows from pipedrive.persons_enriched by matching pe.ct_id to c.user_id, keeping users even without a match.

[Gold / Pipedrive] Only attach person rows where pd_is_ct_employee is not true (excluding flagged employees); similarly join pipedrive.organizations by org_id only when linkedin is filled in and website is null or empty…

[Gold / Pipedrive] For each user_id, count the distinct person ids as client_count, converted to a whole number.

[CT Ledger] Separately, list account ids from public.accounts where role = 'User' and skip_commission_calculations is not true, joined to public.clients on the client's user_id matching the account id.

[CT Ledger] These two lists are merged per user_id: only users passing the account/client eligibility check are kept, and an empty count from the first part is treated as zero.

Summary: For each eligible user account, count the distinct Pipedrive people (non-employees at organizations with a LinkedIn page but no website) linked to that user, reporting zero when there are none.


System: PD

people

Client sign-up / registration timestamp.

System: CT

signup date

System: CT

System: PD

person ID

activation-definition activation-definition-gold onboarding-duration churn-tier-definitions declared-currency-intent client-type-sti-classes trade-existence-gold-booked

```json
{
  "sql": "",
  "explanation": "This question requires Pipedrive person attributes (lead qualification date, non-employee flag) and a Pipedrive person ID, but this connection has no Pipedrive tables and no synced equivalent of those fields.",
  "confidence": "uncertain",
  "warnings": ["No

List every distinct Pipedrive people explicitly marked as non-employees whose lead qualification date follows their signup date. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

owner

System: CT

System: PD

person ID

SELECT DISTINCT p.id AS user_id FROM persons AS p JOIN deals AS d ON d.person_id = p.id AND d.status = 'open' JOIN notes AS n ON n.deal_id = d.id AND user_id <> user_id ORDER BY 1 LIMIT 10000

List every distinct Pipedrive people with an active deal note authored by someone other than the deal owner. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: CT

signup date

System: CT

client

```json
{
  "sql": "",
  "explanation": "The question depends on Pipedrive-only fields (non-employee flag, lead qualification date) that do not exist on this CT-only connection, so the membership cannot be derived here.",
  "confidence": "uncertain",
  "warnings": [
    "This connection has no Piped

For exactly the Pipedrive people explicitly marked as non-employees whose lead qualification date follows their signup date in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

owner

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on an undefined 'first checked result' membership and a notes/author schema (pipedrive notes table, note-author vs deal-owner columns) that is not present in the provided schema, so no faithful query can be generated.",
  "confidence": "un

For exactly the Pipedrive people with an active deal note authored by someone other than the deal owner in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


70 results — first client: Fictional Partner 4

System: PD

people

System: CT

signup date

System: CT

Fictional Company 64
cfx (corporate)
2026-08-15

Synthetic63 Client
pfx (private)
2026-07-31

Fictional Company 61
cfx (corporate)
2026-08-31

Fictional Company 60
cfx (corporate)
2026-08-31

Fictional Company 59
cfx (corporate)
2026-09-05

Fictional Company 58
cfx (corporate)
2026-06-01

Fictional Company 57
cfx (corporate)
2024-02-29

Fictional Company 56
cfx (corporate)
2025-10-26

Fictional Company 55
cfx (corporate)
2025-10-26

Synthetic54 Client
pfx (private)
2026-03-29

Fictional Company 52
cfx (corporate)
2026-08-15

Fictional Company 51
cfx (corporate)
2026-07-31

Fictional Company 50
cfx (corporate)
2026-09-01

Fictional Company 49
cfx (corporate)
2026-08-31

Fictional Company 48
cfx (corporate)
2026-08-31

Fictional Company 47
cfx (corporate)
2026-09-05

Fictional Company 46
cfx (corporate)
2026-06-01

Synthetic45 Client
pfx (private)
2024-02-29

Fictional Company 43
cfx (corporate)
2025-10-26

Fictional Company 42
cfx (corporate)
2026-03-29

Fictional Company 41
cfx (corporate)
2026-03-29

Fictional Company 40
cfx (corporate)
2026-08-15

Fictional Company 39
cfx (corporate)
2026-07-31

Fictional Company 38
cfx (corporate)
2026-09-01

Fictional Company 37
cfx (corporate)
2026-08-31

Synthetic36 Client
pfx (private)
2026-08-31

Fictional Company 34
cfx (corporate)
2026-06-01

Fictional Company 33
cfx (corporate)
2024-02-29

Fictional Company 32
cfx (corporate)
2025-10-26

Fictional Company 31
cfx (corporate)
2025-10-26

Fictional Company 30
cfx (corporate)
2026-03-29

Fictional Company 29
cfx (corporate)
2026-03-29

Fictional Company 28
cfx (corporate)
2026-08-15

Synthetic27 Client
pfx (private)
2026-07-31

Fictional Company 25
cfx (corporate)
2026-08-31

Fictional Company 24
cfx (corporate)
2026-08-31

Fictional Company 23
cfx (corporate)
2026-09-05

Fictional Company 22
cfx (corporate)
2026-06-01

Fictional Company 21
cfx (corporate)
2024-02-29

Fictional Company 20
cfx (corporate)
2025-10-26

Fictional Company 19
cfx (corporate)
2025-10-26

Synthetic18 Client
pfx (private)
2026-03-29

Fictional Company 16
cfx (corporate)
2026-08-15

Fictional Company 15
cfx (corporate)
2026-07-31

Fictional Company 14
cfx (corporate)
2026-09-01

Fictional Company 13
cfx (corporate)
2026-08-31

Fictional Company 12
cfx (corporate)
2026-08-31

Fictional Company 11
cfx (corporate)
2026-09-05

Fictional Company 10
cfx (corporate)
2026-06-01

Synthetic9 Client
pfx (private)
2024-02-29

Fictional Company 7
cfx (corporate)
2025-10-26

Fictional Company 6
cfx (corporate)
2026-03-29

Fictional Company 5
cfx (corporate)
2026-03-29

Fictional Company 4
cfx (corporate)
2026-08-15

Fictional Company 3
cfx (corporate)
2026-07-31

Fictional Company 1
cfx (corporate)
2026-08-31

Fictional Company 2
cfx (corporate)
2026-09-01

SELECT a.id AS user_id, CAST(a.created_at AS DATE) AS signup_date FROM public.accounts AS a ORDER BY a.id DESC NULLS LAST LIMIT 10000

Show the same checked IDs for the Pipedrive people explicitly marked as non-employees whose lead qualification date follows their signup date from the first result, this time in descending user_id order. Keep every member and show only user_id.

For each account, outputs the id (renamed user_id) and created_at cast to a date, named signup_date.

Sorts by id in descending order, placing any rows with a missing id last.

Keeps only the first 10,000 rows after sorting.

Summary: Reads the accounts table, returns each account's id and its creation date converted to a date, ordered by id from highest to lowest with blank ids last, capped at 10,000 rows.


System: PD

people

System: CT

signup date

System: CT

client

```json
{
  "sql": "",
  "explanation": "Cannot generate: the requested membership (Pipedrive people explicitly marked non-employees whose lead qualification date follows their signup date) comes from a prior result that was not supplied, and Pipedrive-only fields (non-employee flag, lead qualificat

Keep the Pipedrive people explicitly marked as non-employees whose lead qualification date follows their signup date membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


System: PD

people

System: PD

owner

System: CT

SELECT DISTINCT p.id AS user_id FROM persons AS p JOIN deals AS d ON d.person_id = p.id AND d.status = 'open' JOIN notes AS n ON n.deal_id = d.id AND user_id <> user_id ORDER BY 1 DESC LIMIT 10000

Show the same checked IDs for the Pipedrive people with an active deal note authored by someone other than the deal owner from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

organization

System: CT

System: PD

person ID

```json
{
  "sql": "",
  "explanation": "The schema context lists no columns for organizations or persons_enriched, and there is no documented field identifying which ERP systems an organization uses, so I cannot reliably map 'organization names at least two ERP systems' to any column.",
  "confiden

List every distinct Pipedrive people whose organization names at least two ERP systems. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.


System: PD

people

System: PD

owner

System: CT

client

```json
{
  "sql": "",
  "explanation": "Cannot generate: the provided schema lists tables with no columns, no pipedrive notes/deals/people tables, and 'the third result' from a prior query is unavailable.",
  "confidence": "uncertain",
  "warnings": ["Schema context contains empty column lists and

Keep the Pipedrive people with an active deal note authored by someone other than the deal owner membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


For exactly the Pipedrive people whose organizatio...

System: PD

people

System: PD

organization

System: CT

client

SELECT c360.user_id, COUNT(DISTINCT pe.id)::int AS client_count FROM ct.client_360 c360 LEFT JOIN pipedrive.persons_enriched pe ON pe.ct_id = c360.user_id AND pe.pd_is_ct_employee IS NOT TRUE LEFT JOIN pipedrive.organizations o ON o.id = pe.org_id WHERE (SELECT COUNT(*) FROM (VALUES ('%sap%'), ('%oracle%'), ('%netsuite%'), ('%dynamics%'), ('%infor%'), ('%odoo%'), ('%sage%'), ('%workday%')) AS k(pat) WHERE o.name ILIKE k.pat) >= 2 GROUP BY 1

SELECT a.id AS user_id FROM public.accounts AS a LIMIT 10000

For exactly the Pipedrive people whose organization names at least two ERP systems in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


System: PD

people

System: PD

organization

System: CT

```json
{
  "sql": "",
  "explanation": "The request depends on a prior result (\"the first result\" / \"same checked IDs\") and a criterion (\"organization names at least two ERP systems\") that cannot be resolved from the provided schema, which lists no columns.",
  "confidence": "uncertain",
  "w

Show the same checked IDs for the Pipedrive people whose organization names at least two ERP systems from the first result, this time in descending user_id order. Keep every member and show only user_id.


System: PD

people

System: PD

organization

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request is incoherent and references ERP system membership data ('the third result') that does not exist in the provided schema, so no reliable SQL can be generated.",
  "confidence": "uncertain",
  "warnings": ["No ERP-system membership or multi-result c

```json
{
  "sql": "",
  "explanation": "This question's cohort (Pipedrive people whose organization names two or more ERP systems from a prior result) is defined entirely by CRM/Pipedrive-side data that does not exist on this connection — this CT leg has no persons/organizations tables and no ERP-s

Keep the Pipedrive people whose organization names at least two ERP systems membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


24 distinct Pipedrive people sit at organisations with a known service level but no recorded wallet-share value.

System: PD

people

System: PD

organization

System: PD

wallet-share

System: CT

System: CT

System: PD

System: both

System: PD

value

System: CT

System: PD

person ID

SELECT DISTINCT pe.id AS user_id FROM pipedrive.persons_enriched AS pe JOIN pipedrive.organizations AS o ON o.id = pe.org_id WHERE NOT o.cf_level_of_service IS NULL AND o.cf_wallet_share IS NULL ORDER BY 1 LIMIT 10000

Unknown wallet-share means the organisation field cf_wallet_share is NULL, not zero or an explicit 'unknown' value.

Known service level means the organisation field cf_level_of_service is non-NULL; people are matched to organisations via the person's org_id.

Show the organisation names behind these 24 people to see which firms are missing wallet-share data.

Group these people by their organisation's service level to see which service tiers have the most missing wallet-share values.

All 12 sampled rows are person IDs (31001 to 31031 of 24 returned), listed in ascending order as requested.

This is a data-quality gap: these organisations are classed by service level yet wallet-share is unfilled, so wallet-share reporting will undercount them.

List every distinct Pipedrive people at an organization with a known service level but an unknown wallet-share value. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

Reads the pipedrive.persons_enriched table and joins it to pipedrive.organizations by matching the organization's id to the person's org_id, keeping only persons with a matching organization.

Keeps only rows where the organization's cf_level_of_service is filled in (not null).

Excludes rows where the organization's cf_wallet_share has any value — it must be unknown (null).

Returns each distinct person id once, named user_id.

Sorts user_id from smallest to largest and caps the result at 10,000 rows.

Summary: Finds each unique person ID from the pipedrive.persons_enriched table whose linked organization has a service level filled in but a wallet-share value missing.


System: PD

people

System: PD

organization

System: PD

wallet-share

System: CT

System: CT

System: PD

System: both

System: PD

value

System: CT

client

```json
{
  "sql": "",
  "explanation": "The referenced schema lists no columns for any table, so I cannot verify the existence or semantics of 'service level', 'wallet-share', or the prior-result membership needed to build this federated query.",
  "confidence": "uncertain",
  "warnings": [
    "Al

```json
{
  "sql": "",
  "explanation": "This request's cohort (organization, known service level, unknown wallet-share) is defined entirely by Pipedrive/gold-side fields that do not exist in this CT-production connection, and the requested output is a scalar count of that gold cohort — not a per-us

For exactly the Pipedrive people at an organization with a known service level but an unknown wallet-share value in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.


22 distinct Pipedrive people have an activity with a duration but no due time

System: PD

people

System: PD

activity

System: PD

duration

System: PD

due time

System: CT

System: PD

person ID

High confidence (85%) — clear time period, moderate complexity

SELECT DISTINCT COALESCE(CAST(user_id AS TEXT), CAST(pe.id AS TEXT)) AS user_id FROM pipedrive.activities AS a JOIN pipedrive.persons_enriched AS pe ON pe.id = a.person_id WHERE NOT a.duration IS NULL AND a.due_time IS NULL ORDER BY 1 LIMIT 10000

user_id is taken as the person identifier, falling back to the Pipedrive person ID when user_id is null

Only activities linked to a person in persons_enriched are counted

Show the names and owners of these 22 people

Which activities for these people have a duration set but no due time?

IDs shown are a sample of the 22; the full list follows in ascending ID order

The query falls back to the person ID when user_id is null, so some values may be Pipedrive-only persons

List every distinct Pipedrive people with an activity that has a duration but no due time. Return only user_id, in ascending order; use the person ID for a Pipedrive-only person.

Reads from pipedrive.activities (aliased a) joined to pipedrive.persons_enriched (aliased pe) by matching pe.id to a.person_id.

Keeps only activity rows where duration is not empty and due_time is empty.

For each matching row, outputs the activity's user_id as text; if that is missing, uses the person's id as text instead.

Removes duplicates, sorts the resulting user_id values in ascending order, and caps the output at 10,000 rows.

Summary: Finds distinct user identifiers from Pipedrive activities that have a duration but no due time, preferring the activity's user_id and falling back to the person's ID when user_id is missing.


24 Pipedrive people sit at organisations with a known service level but a missing wallet-share value, listed by descending user_id.

System: PD

people

System: PD

organization

System: PD

wallet-share

System: CT

System: CT

System: PD

System: both

System: PD

value

System: CT

SELECT DISTINCT pe.id AS user_id FROM pipedrive.persons_enriched AS pe JOIN pipedrive.organizations AS o ON o.id = pe.org_id WHERE NOT o.cf_level_of_service IS NULL AND o.cf_wallet_share IS NULL ORDER BY 1 DESC LIMIT 10000

Persons are matched to organisations via the org_id join, so only people linked to an organisation are included.

'Unknown wallet-share' is interpreted as cf_wallet_share being null.

List the organisations these 24 people belong to, with their service levels, to see where wallet-share is missing.

Which of these organisations have never had a wallet-share value recorded versus having it cleared?

This is the same set as the first result, re-sorted by descending user_id; membership is unchanged.

The filter keeps organisations where cf_level_of_service is populated and cf_wallet_share is null, so all 24 belong to organisations lacking a wallet-share figure.

Show the same checked IDs for the Pipedrive people at an organization with a known service level but an unknown wallet-share value from the first result, this time in descending user_id order. Keep every member and show only user_id.

Reads the pipedrive.persons_enriched table (aliased pe) and takes each person's id, shown as user_id.

Joins to pipedrive.organizations (aliased o) by matching the organization's id to the person's org_id.

Requires that the organization's cf_wallet_share is null — rows with any wallet-share value are excluded.

Sorts by user_id from largest to smallest and caps the output at 10,000 rows.

Summary: Lists distinct person IDs from enriched Pipedrive people whose organization has a service level filled in but no wallet-share value, sorted by ID from highest to lowest.


System: PD

people

System: PD

organization

System: PD

wallet-share

System: CT

System: CT

System: PD

System: both

System: PD

value

System: CT

client

```json
{
  "sql": "",
  "explanation": "The request depends on a prior (third) result set and Pipedrive-side attributes (service level, wallet-share membership) that do not exist on this CT-production connection, so no SQL can reproduce that cohort here.",
  "confidence": "uncertain",
  "warnings":

Keep the Pipedrive people at an organization with a known service level but an unknown wallet-share value membership from the third result and count those distinct IDs as client_count. An empty result counts as zero.


22 Pipedrive people have an activity with a duration but no due time.

System: PD

people

System: PD

activity

System: PD

duration

System: PD

due time

System: CT

client

SELECT COUNT(DISTINCT COALESCE(CAST(a.user_id AS TEXT), CAST(pe.id AS TEXT))) AS client_count FROM pipedrive.activities AS a JOIN pipedrive.persons_enriched AS pe ON pe.id = a.person_id WHERE a.duration IS NOT NULL AND a.due_time IS NULL

SELECT
  COUNT(DISTINCT COALESCE(CAST(a.user_id AS TEXT), CAST(pe.id AS TEXT))) AS client_count
FROM pipedrive.activities AS a
JOIN pipedrive.persons_enriched AS pe
  ON pe.id = a.person_id
WHERE
  NOT a.duration IS NULL AND a.due_time IS NULL

client_count is distinct people implied by activities meeting the duration/no-due-time filter

counts all such activities regardless of date

List those 22 people with their activity details

Which of these people have had no such activity recently?

Count is of distinct people, via a join on activities with duration set and due time null.

The distinct key coalesces activity user_id with person id, so it counts whoever is linked to the activity, not necessarily the person record.

For exactly the Pipedrive people with an activity that has a duration but no due time in the first checked result, return their distinct count as client_count. Keep that membership and return zero if it is empty.

Joins activities to persons_enriched by matching the activity's person_id to the person's id.

Keeps only activities where duration is filled in and due_time is empty.

For each match, uses the activity's user_id as the identity, falling back to the person's id when user_id is missing (both cast to text).

Counts how many distinct identities match; if none match, the count is zero.

Summary: Counts distinct people linked to activities that have a duration but no due time, identifying each by the activity's user when present, otherwise by the person's id.


22 distinct members have activities with a duration but no due time, listed by user_id in descending order

System: PD

people

System: PD

activity

System: PD

duration

System: PD

due time

System: CT

SELECT DISTINCT COALESCE(CAST(user_id AS TEXT), CAST(pe.id AS TEXT)) AS user_id FROM pipedrive.activities AS a JOIN pipedrive.persons_enriched AS pe ON pe.id = a.person_id WHERE NOT a.duration IS NULL AND a.due_time IS NULL ORDER BY 1 DESC LIMIT 10000

user_id taken from the activity's owner, falling back to the person id when null

Duplicate members collapsed via DISTINCT, so 22 is the member count, not the activity count

The identifier falls back to the person id when user_id is missing, so some values may be person ids rather than user ids

Show the same checked IDs for the Pipedrive people with an activity that has a duration but no due time from the first result, this time in descending user_id order. Keep every member and show only user_id.

Join pipedrive.activities to pipedrive.persons_enriched by matching pe.id to a.person_id.

Keep only activities where duration is not null and due_time is null — i.e., exclude rows lacking a duration or having a due time.

For each row, output user_id if it exists; otherwise fall back to the person's pe.id, both converted to text.

Keep only distinct values of that combined identifier.

Sort the results in descending order and return at most 10,000 rows.

Summary: From activities joined to enriched persons, keep rows where the activity has a duration set but no due time, then output a distinct combined identifier per person, sorted descending, capped at 10,000 rows.