CONNECTIONS / MongoDB / MongoDB to OpenAI Offline CAPI
From MongoDB collections to OpenAI's Offline Conversions API.
Signals reads your MongoDB collections through a read-only database user scoped to the MongoDB collections you name, then reports downstream revenue as OpenAI's conversion measurement comes online so OpenAI sees offline conversions without an export.
- ● mongodb → openai.capi :: live
- > read closed_deals rows=68,674
- hash sha256(email,phone) consent=filtered
- route openai deliver
- ✓ delivered · 60,433 matched
WHAT THIS ENABLES
MongoDB to OpenAI Offline CAPI
MongoDB to OpenAI Offline CAPI: Signals reads your MongoDB collections and reports downstream revenue as OpenAI's conversion measurement comes online, hashed and consent-checked each time the read runs.
- Signals reports downstream revenue as OpenAI's conversion measurement comes online, reading your MongoDB collections each time the read runs.
- Measurement in place before the surface scales, with no export step out of your MongoDB collections.
The click, the conversion, and the credit.
The conversion happens off OpenAI, away from any pixel. Here is how MongoDB closes the loop.
Built for the teams that own the number.
Application teams running MongoDB who need a revenue feed ready the moment OpenAI's offline measurement expands out of MongoDB collections.
From kickoff to verified events.
-
Connect
A read-only database user scoped to the MongoDB collections you name, mapped to OpenAI's Offline Conversions API and tuned for the emerging OpenAI ad surface.
-
Map
Columns from your MongoDB collections 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 MongoDB collections 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 MongoDB records the event. |
What Offline CAPI actually receives.
- ● signals :: event payload
- > POST /offline_conversions source=mongodb.closed_deals
- event_name "Purchase" · event_time 1784193074
- em sha256 "2b9d64…" · value 20522 · currency GBP
- ✓ accepted · row_status delivered
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How often does the MongoDB OpenAI Offline CAPI sync run?
The pace follows your MongoDB collections: Signals sends changes from MongoDB to OpenAI each time the read runs. Because it reads only the records changed since the last run, a revenue feed ready the moment OpenAI's offline measurement expands stays current while application teams running MongoDB keep the emerging OpenAI ad surface in view. Delivery needs no export and no manual upload.
What match rate should a MongoDB-sourced OpenAI Offline CAPI batch expect?
In MongoDB, match rate tracks the email and phone quality your MongoDB collections carry. Clean identifiers land for OpenAI; blank ones never will. The first Offline Conversions API report shows application teams running MongoDB where the emerging OpenAI ad surface needs work, a data-quality task handled in MongoDB.
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