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Azure OpenAI to Gatekeeper integration — real-time data sync

Keep Azure OpenAI and Gatekeeper in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Azure OpenAI and Gatekeeper

Flow Azure OpenAI data into Gatekeeper in real time — no exports, no schedulers, no custom scripts.

Azure OpenAI is a read-only source: Stacksync reads its data in real time and delivers it into Gatekeeper, so Gatekeeper always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.

Azure 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 Gatekeeper, the tool the team uses every day. So the value of Azure OpenAI depends on two flows that most teams stitch together with a custom script or a one-time export: getting Gatekeeper'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 Workflow form data, Custom data groups, Users, Categories from Gatekeeper into Azure OpenAI continuously, so the model always works from current records instead of a snapshot, and writes Vector stores, Deployments, Models, Fine-tuning jobs, the scores, labels, summaries, drafts, and embedding metadata Azure OpenAI produces, back onto the matching record in Gatekeeper. 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 Mirror Assistants and Vector stores configuration into a database as an auditable inventory of retrieval assets and their linked files.
  • 02 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 03 Write contract or vendor records into Gatekeeper when a deal closes in the CRM to kick off downstream procurement and legal workflows.
  • 04 Two-way sync Contracts between Gatekeeper and an ERP or warehouse so value, renewal dates, owner, and status stay current without manual re-keying.

Common sync patterns

Every output routes back to the right record

Because each item is matched on a stable identifier, an Azure OpenAI result always attaches to the record in Gatekeeper it was computed from, with no manual reconciliation.

Build a retrieval corpus from Gatekeeper's records

Records, tickets, messages, or events from Gatekeeper sync into Azure OpenAI so they can be indexed, embedded, or retrieved as context, without a hand-built extraction job.

Keep the model's knowledge current

As records change in Gatekeeper, the synced copy in Azure OpenAI updates within seconds, so retrieval and generation reason over live data rather than a stale export.

What you can sync between Azure OpenAI and Gatekeeper

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.

Azure OpenAI objects Gatekeeper objects How this pairing syncs
Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Files Document files attached to contracts and vendors - executed PDFs, certificates, and compliance evidence; read to pull signed files and evidence out, or written to push generated documents in. Same entity on both sides — records pair one-to-one and field-level changes reconcile in the writable direction.
Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Custom data groups Customer-configured custom fields and data groups; because the JSON:API and its docs are dynamic, any custom data added in Configuration exposes the same read/write endpoints as the standard objects and syncs the same way. Deployments is specific to Azure OpenAI and Custom data groups to Gatekeeper — each maps to any object or custom field on the other side.
Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Users Gatekeeper user and team records governed by role-based access; read to map contract and vendor owners, approvers, and internal contacts to CRM or HR records. Models is specific to Azure OpenAI and Users to Gatekeeper — each maps to any object or custom field on the other side.
Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Categories The classification taxonomy applied to contracts and vendors (type, department, business unit); synced so categorization stays consistent between Gatekeeper and downstream reporting or ERP dimensions. Fine-tuning jobs is specific to Azure OpenAI and Categories to Gatekeeper — each maps to any object or custom field on the other side.
Batch jobs Asynchronous bulk-inference jobs; status and output-file IDs are polled to completion to drive downstream pipeline triggers. Contracts The core contract records holding value, key dates, renewal terms, status, type, owner, and the linked vendor; created, read, updated, and deleted so contract data moves two-way between Gatekeeper and a database, ERP, or CRM. Batch jobs is specific to Azure OpenAI and Contracts to Gatekeeper — each maps to any object or custom field on the other side.
Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Vendors (Suppliers) Company records for counterparties and suppliers with onboarding status, compliance, risk, contacts, and spend; read and written to keep vendor master data aligned with a CRM or ERP. Usage and quota is specific to Azure OpenAI and Vendors (Suppliers) to Gatekeeper — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Gatekeeper

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.

Azure OpenAI Gatekeeper Interval-based propagation

DetectionStacksync polls Azure OpenAI for changes on an incremental schedule, reading only records changed since the previous pass. Polling: list endpoints plus GET on job IDs for status.

DeliveryEach detected change is written to Gatekeeper through its API, with automatic retries and rate-limit backoff.

Gatekeeper Azure OpenAI Interval-based propagation

DetectionStacksync polls Gatekeeper for changes on an incremental schedule, reading only records changed since the previous pass. No native developer webhook subscription API and no database change-data-capture log.

DeliveryAzure OpenAI does not accept inbound record writes, so this direction carries requests rather than records: Azure OpenAI's output flows back as field updates on the originating Gatekeeper records.

Rate-limit considerations

  • Azure OpenAI: Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
  • Gatekeeper: Gatekeeper publishes no fixed public per-minute request quota; throughput is governed per key by its endpoint permissions, and every call is recorded (parameters, payload, response) under API Logs for monitoring. Pace bulk writes and use JSON:API pagination on list endpoints.
What ships with Azure OpenAI ⇄ Gatekeeper

Connect Azure OpenAI and Gatekeeper for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure OpenAI–Gatekeeper connection.

Real-time

Real-time sync

Changes in Azure OpenAI or Gatekeeper instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or Gatekeeper 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 Azure OpenAI or Gatekeeper record.

Observability

Monitoring

Track your Azure OpenAI ⇄ Gatekeeper sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Azure OpenAI and Gatekeeper.

How the Azure OpenAI and Gatekeeper connectors work

Azure OpenAI

Integration surface
REST data-plane (inference + authoring) and Azure Resource Manager control-plane
Authentication
API key in the api-key header, or a Microsoft Entra ID bearer token / managed identity
Change detection
Polling: list endpoints plus GET on job IDs for status; no webhooks or change feed. Fine-tuning and batch jobs expose queued/running/succeeded states.
Capabilities
read
Rate limits
Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.

Gatekeeper

Integration surface
RESTful API following the JSON:API specification, tenant-scoped with interactive docs at {tenant}.gatekeeperhq.com/api_docs and a published Postman collection. The API is dynamic: it exposes the standard Contract and Vendor objects plus any custom data groups and workflow-form data configured in the tenant.
Authentication
API keys created and managed under Configuration > API Keys and passed as a token; each key carries granular per-endpoint permissions set to read-only or write, so access is scoped per object. Multiple keys can be issued and revoked independently.
Change detection
No native developer webhook subscription API and no database change-data-capture log; detect changes by polling the JSON:API list endpoints filtered and sorted on updated-at timestamps. Gatekeeper's own event automation - Workflow Engine phase transitions and Interconnect process orchestration - runs inside the platform rather than as a subscribable webhook stream.
Capabilities
read · write
Rate limits
Gatekeeper publishes no fixed public per-minute request quota; throughput is governed per key by its endpoint permissions, and every call is recorded (parameters, payload, response) under API Logs for monitoring. Pace bulk writes and use JSON:API pagination on list endpoints.
How it works

How to connect Azure OpenAI to Gatekeeper — 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.

  1. 01

    Connect your apps

    Authenticate Azure OpenAI and Gatekeeper with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Azure OpenAI connected
    Gatekeeper connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Azure OpenAI and Gatekeeper 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Azure OpenAI ⇄ Gatekeeper
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Azure OpenAI Gatekeeper
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Azure OpenAI and Gatekeeper integration FAQ

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.

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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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 380 integrations available for Azure OpenAI and Gatekeeper.

Popular · 7 of 380
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