Real-time
Real-time sync
Changes in Full Enrich or Openai instantly reflect in both systems. No stale data, no manual imports.
Business productivity ⇄ AI
Full Enrich to Openai integration — real-time data sync
Keep Full Enrich and Openai in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Full Enrich and Openai
Flow Openai data into Full Enrich in real time — no exports, no schedulers, no custom scripts.
Openai is a read-only source: Stacksync reads its data in real time and delivers it into Full Enrich, so Full Enrich always reflects the current state of Openai — without exports, scripts, or schedulers.
Openai works on data it does not own. The records, conversations, tickets, messages, and events it needs to embed, classify, summarize, or answer questions about actually live in Full Enrich, the tool the team uses every day. So the value of Openai depends on two flows that most teams stitch together with a custom script or a one-time export: getting Full Enrich's data in, and getting the model's results back out to where people can act on them. When either flow runs on a batch or a stale snapshot, the model reasons over yesterday's data and its output never reaches the record it belongs to.
Stacksync syncs Phone numbers, Credits, Enrichment requests, Contacts from Full Enrich into Openai continuously, so the model always works from current records instead of a snapshot, and writes Audit logs, Models, Fine-tuning jobs, Files, the scores, labels, summaries, drafts, and embedding metadata Openai produces, back onto the matching record in Full Enrich. The sync is field-level and keyed on a stable identifier, so every output attaches to the exact record it came from and each system keeps its own extra fields untouched.
You decide the direction and the trigger conditions per field: pull records one way to build and keep a retrieval corpus current, push results the other way onto the operational record, or both.
Common use cases
- 01 Land Fine-tuning jobs with their status, base model, hyperparameters, and result Files in a warehouse to power MLOps dashboards without per-viewer API calls.
- 02 Subscribe to batch.completed and fine_tuning.job.succeeded webhooks so a downstream pipeline step fires the moment an offline-inference or training job finishes.
- 03 Run waterfall enrichment over warehouse lead lists before importing them into outbound sequencers.
- 04 Backfill missing phone numbers on aging CRM records on a schedule.
Common sync patterns
Keep the model's knowledge current
As records change in Full Enrich, the synced copy in Openai updates within seconds, so retrieval and generation reason over live data rather than a stale export.
Where Openai classifies or scores: results land on the record
Categories, sentiment, priority, or scores produced by Openai write back onto the matching record in Full Enrich, so the team acts on them in the tool they already use.
Where Openai generates text: drafts and summaries where the work happens
Summaries, suggested replies, or generated content from Openai sync onto the Full Enrich record as a field or note, ready for a person to review before it goes out.
What you can sync between Full Enrich and Openai
Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.
| Full Enrich objects | Openai objects | How this pairing syncs | |
|---|---|---|---|
| Contacts The person inputs (name plus company or domain) sent for enrichment. | Audit logs Organization audit-log events (API-key changes, logins, project edits) from the Administration API; read for compliance and security monitoring. | Contacts is specific to Full Enrich and Audit logs to Openai — each maps to any object or custom field on the other side. | |
| Enrichment results Completed records fetched by request ID or delivered via webhook. | Models Catalog of available base, snapshot, and fine-tuned models with owner and capabilities; read-only reference data used to resolve inference and fine-tuning targets. | Enrichment results is specific to Full Enrich and Models to Openai — each maps to any object or custom field on the other side. | |
| Emails Returned addresses with verification status, written back to the CRM or database. | Fine-tuning jobs Training jobs with status, base model, hyperparameters, trained-model name, and result files; status received by webhook or polled from queued through succeeded or failed. | Emails is specific to Full Enrich and Fine-tuning jobs to Openai — each maps to any object or custom field on the other side. | |
| Phone numbers Returned mobile and direct-dial numbers from the provider waterfall. | Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back as business records in sync. | Phone numbers is specific to Full Enrich and Files to Openai — each maps to any object or custom field on the other side. | |
| Credits Account balance consumed on successful finds; monitored to pace sync jobs. | Batch jobs Asynchronous bulk-inference jobs within a 24-hour window, with status and output/error file IDs; completion detected by the batch.completed webhook or by polling. | Credits is specific to Full Enrich and Batch jobs to Openai — each maps to any object or custom field on the other side. | |
| Enrichment requests Submitted batches of contacts to enrich; processed asynchronously by the waterfall. | Vector stores File collections backing file-search retrieval, with name, file counts, usage bytes, and status; read as a metadata inventory of retrieval assets. | Enrichment requests is specific to Full Enrich and Vector stores to Openai — each maps to any object or custom field on the other side. |
How changes propagate between Full Enrich and Openai
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
Full Enrich Openai Sub-second propagation
DetectionFull Enrich notifies Stacksync of record changes through webhook events. Webhook callbacks when enrichment batches complete, with polling of result endpoints as a fallback.
DeliveryOpenai does not accept inbound record writes, so this direction carries requests rather than records: Openai's output flows back as field updates on the originating Full Enrich records.
Openai Full Enrich Sub-second propagation
DetectionOpenai notifies Stacksync of record changes through webhook events. Push webhooks (Standard Webhooks spec, whsec_ signing secret) fire on batch.completed, fine_tuning.job.succeeded/failed, response.completed/failed,.
DeliveryEach detected change is written to Full Enrich through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Full Enrich: Subject to the platform's API rate limits and per-batch size constraints.
- Openai: Rate limits are set per organization and per project as RPM/RPD and TPM/TPD and rise across five spend-based usage tiers; responses carry x-ratelimit-remaining headers and return HTTP 429 on breach.
What ships with Full Enrich ⇄ Openai
Connect Full Enrich and Openai for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Full Enrich–Openai connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Full Enrich or Openai data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
At scale
Event queues
Handle millions of events per minute without losing a single Full Enrich or Openai record.
Observability
Monitoring
Track your Full Enrich ⇄ Openai sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Full Enrich and Openai.
How the Full Enrich and Openai connectors work
Full Enrich
- Integration surface
- REST API with asynchronous bulk enrichment endpoints
- Authentication
- API key (Bearer token)
- Change detection
- Webhook callbacks when enrichment batches complete, with polling of result endpoints as a fallback
- Capabilities
- read · write · webhooks
- Rate limits
- Subject to the platform's API rate limits and per-batch size constraints
Openai
- Integration surface
- REST API: data-plane inference and authoring (api.openai.com/v1) plus the Administration API (/v1/organization/*) for usage, costs, projects, and audit logs
- Authentication
- Bearer API key scoped to a project or user (sk-...) in the Authorization header, with optional OpenAI-Organization and OpenAI-Project headers; the Administration API requires an Admin key (sk-admin-...)
- Change detection
- Push webhooks (Standard Webhooks spec, whsec_ signing secret) fire on batch.completed, fine_tuning.job.succeeded/failed, response.completed/failed, and eval.run events; objects without a webhook are read by list plus GET-by-ID. No row-level CDC feed.
- Capabilities
- read · webhooks
- Rate limits
- Rate limits are set per organization and per project as RPM/RPD and TPM/TPD and rise across five spend-based usage tiers; responses carry x-ratelimit-remaining headers and return HTTP 429 on breach.
How it works
How to connect Full Enrich to Openai — three steps, no code
Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.
- 01
Connect your apps
Authenticate Full Enrich and Openai with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Full Enrich
connected
Openai
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Full Enrich and Openai objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.
objects · Full Enrich ⇄ OpenaiCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Full Enrich OpenaiCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Full Enrich and Openai integration FAQ
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Yes. Stacksync provides a managed, real-time integration between Full Enrich and Openai — Openai is a read-only source, so data flows from it into the other system: authenticate both systems, choose the objects to sync, map fields visually, and changes propagate in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Full Enrich and Openai. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Full Enrich: Webhook callbacks when enrichment batches complete, with polling of result endpoints as a fallback. On Openai: Push webhooks (Standard Webhooks spec, whsec_ signing secret) fire on batch.completed, fine_tuning.job.succeeded/failed, response.completed/failed, and eval.run events; objects without a webhook are read by list plus GET-by-ID. No row-level CDC feed. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Openai side: Audit logs, Models, Fine-tuning jobs, Files, plus custom fields where Openai exposes them. On the Full Enrich side: Phone numbers, Credits, Enrichment requests, Contacts. Stacksync auto-detects both schemas and converts types between the two systems.
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Openai is a read-only source, so this integration runs one-way: Stacksync reads from Openai in real time and delivers into Full Enrich. Field mapping and monitoring work the same as for two-way pairs.
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Common patterns for Full Enrich and Openai: Keep the model's knowledge current; Where Openai classifies or scores: results land on the record; Where Openai generates text: drafts and summaries where the work happens. As records change in Full Enrich, the synced copy in Openai updates within seconds, so retrieval and generation reason over live data rather than a stale export.
SECURITY
Security teams trust Stacksync
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS
SSO & SCIM
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Alerts
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Secure connection options
Securely connects to your systems with:
Related integrations
Every pair below is a real-time, two-way sync. Search all 359 integrations available for Full Enrich and Openai.
Popular · 6 of 359
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Full Enrich ⇄ Pinecone -
Full Enrich ⇄ Azure OpenAI -
Full Enrich ⇄ Autopilot -
Openai ⇄ Agiloft -
Openai ⇄ Gainsight -
Openai ⇄ Ironclad
No integrations match .
