CONNECTIONS / PostgreSQL / PostgreSQL to OpenAI Offline CAPI
Your PostgreSQL tables, wired to OpenAI's Offline Conversions API.
Signals reads your PostgreSQL tables through a read-only database user scoped to the PostgreSQL tables you name, then reports downstream revenue as OpenAI's conversion measurement comes online so OpenAI sees offline conversions without an export.
- ● postgresql → openai.capi :: live
- > read closed_deals rows=12,387
- hash sha256(email,phone) consent=filtered
- route openai deliver
- ✓ delivered · 9,662 matched
WHAT THIS ENABLES
PostgreSQL to OpenAI Offline CAPI
OpenAI Offline CAPI for PostgreSQL: Signals reports downstream revenue as OpenAI's conversion measurement comes online straight from your PostgreSQL tables, hashed and consent-screened each time the read runs.
- Signals reports downstream revenue as OpenAI's conversion measurement comes online, reading your PostgreSQL tables each time the read runs.
- Measurement in place before the surface scales, with no export step out of your PostgreSQL tables.
The click, the conversion, and the credit.
The conversion happens off OpenAI, away from any pixel. Here is how PostgreSQL closes the loop.
Built for the teams that own the number.
Engineering teams running PostgreSQL who need a revenue feed ready the moment OpenAI's offline measurement expands out of PostgreSQL tables.
From kickoff to verified events.
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Connect
A read-only database user scoped to the PostgreSQL tables you name, mapped to OpenAI's Offline Conversions API and tuned for the emerging OpenAI ad surface.
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Map
Columns from your PostgreSQL tables align to OpenAI's Offline Conversions API in the visual mapper, hashed as the read runs and checked for the emerging OpenAI ad surface.
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Deliver
Each time the read runs, Signals reads your PostgreSQL tables and reports downstream revenue as OpenAI's conversion measurement comes online, reads only the records changed since the last run, with the emerging OpenAI ad surface watched in the debugger.
What changes when the CSV goes away.
| Capability | Manual CSV upload | Datahash |
|---|---|---|
| Reporting | Offline sales sit in a separate export, reconciled by hand. | Revenue counted in the same Offline CAPI reporting as web conversions. |
| Deduplication | A re-uploaded file risks counting the same conversion twice. | Deduped delivery, so a resent record never counts as a second conversion. |
| Match visibility | Match quality is a guess until the numbers look off. | Match rate reported per upload, tied to the source event set that produced it. |
| Effort and latency | An analyst exports and uploads on a manual cadence. | Server-side and automatic the moment PostgreSQL records the event. |
What Offline CAPI actually receives.
- ● signals :: event payload
- > POST /offline_conversions source=postgresql.closed_deals
- event_name "Purchase" · event_time 1784353012
- em sha256 "2b9d64…" · value 43126 · currency AED
- ✓ accepted · row_status delivered
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How often does the PostgreSQL OpenAI Offline CAPI sync run?
Delivery tracks PostgreSQL, run by engineering teams running PostgreSQL: a near-real-time or scheduled sync of your PostgreSQL tables feeds OpenAI on that cadence. Each run reads only the records changed since the last run, so measurement in place before the surface scales holds up on the emerging OpenAI ad surface. The live debugger confirms every delivery inline.
What match rate should a PostgreSQL-sourced OpenAI Offline CAPI batch expect?
Coverage of hashed identifiers across your PostgreSQL tables decides it, and engineering teams running PostgreSQL own that inside PostgreSQL. A live email or phone matches into OpenAI; neither, and it will not. First-run feedback flags the emerging OpenAI ad surface, which engineering teams running PostgreSQL then raise inside your PostgreSQL tables.
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