Real-time
Two-way sync
Changes in Gatekeeper or Pinecone instantly reflect in both systems. No stale data, no manual imports.
Business productivity ⇄ AI
Gatekeeper to Pinecone integration — real-time, two-way sync
Keep Gatekeeper and Pinecone 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 Gatekeeper and Pinecone
Feed Gatekeeper's records into Pinecone for indexing, classification, and generation, then land the model's output back on those same records, in real time and in both directions.
Pinecone 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 Pinecone 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 Custom data groups, Users, Categories, Contracts from Gatekeeper into Pinecone continuously, so the model always works from current records instead of a snapshot, and writes Indexes, Vectors (records), Namespaces, Collections, the scores, labels, summaries, drafts, and embedding metadata Pinecone 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 Write embeddings and their metadata into a Pinecone index from a Postgres or warehouse table so a semantic-search or RAG feature always queries fresh vectors.
- 02 Keep a Pinecone index aligned with a source of truth (a product catalog, knowledge base, or CRM) so new, changed, and deleted records upsert and delete the matching vectors.
- 03 Sync Vendors (Suppliers) with a CRM or ERP so counterparty, compliance, contact, and spend records match across the VCLM and the systems of record.
- 04 Push generated documents into Gatekeeper Files, or pull executed contract PDFs and compliance evidence out into a document store or archive.
Common sync patterns
Every output routes back to the right record
Because each item is matched on a stable identifier, an Pinecone 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 Pinecone 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 Pinecone updates within seconds, so retrieval and generation reason over live data rather than a stale export.
What you can sync between Gatekeeper and Pinecone
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.
| Gatekeeper objects | Pinecone objects | How this pairing syncs | |
|---|---|---|---|
| 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. | Indexes Serverless or pod-based containers holding vectors of a fixed dimension and distance metric (cosine, dotproduct, euclidean); managed on the control plane (api.pinecone.io) via create, list, describe, configure, and delete. describe_index returns the per-index data-plane host. | Vendors (Suppliers) is specific to Gatekeeper and Indexes to Pinecone — each maps to any object or custom field on the other side. | |
| 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. | Vectors (records) The core data: an id (up to 512 chars), a dense values array, optional sparse_values, and JSON metadata (up to 40 KB filterable per record). Full CRUD on the data plane via upsert, update, fetch, query, and delete, so write is supported here. | Files is specific to Gatekeeper and Vectors (records) to Pinecone — each maps to any object or custom field on the other side. | |
| Workflow form data The structured data captured on Gatekeeper workflow forms (intake requests, vendor onboarding, risk assessments); exposed by the API since 2025 so form results sync into an operational database, not only contract and vendor records. | Namespaces Partitions inside an index; every read and write targets one namespace and vectors across namespaces are isolated. Enumerated with list_namespaces and sized per namespace via describe_index_stats. | Workflow form data is specific to Gatekeeper and Namespaces to Pinecone — each maps to any object or custom field on the other side. | |
| 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. | Collections Immutable snapshots of a pod-based index that store its data but not its definition; created, listed, and deleted on the control plane and used to recreate a pod-based index. Serverless indexes use Backups instead. | Custom data groups is specific to Gatekeeper and Collections to Pinecone — each maps to any object or custom field on the other side. | |
| 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. | Backups Point-in-time snapshots of a serverless index; created, listed, and restored into a new index on the control plane for recovery or cloning. Read as a recovery-asset inventory. | Users is specific to Gatekeeper and Backups to Pinecone — each maps to any object or custom field on the other side. | |
| 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. | Index statistics Describe_index_stats returns total and per-namespace vector counts, the index dimension, and index fullness; read to size a sync and to detect drift between Pinecone and the source of truth. | Categories is specific to Gatekeeper and Index statistics to Pinecone — each maps to any object or custom field on the other side. |
How changes propagate between Gatekeeper and Pinecone
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.
Gatekeeper Pinecone 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.
DeliveryEach detected change is written to Pinecone through its API, with automatic retries and rate-limit backoff.
Pinecone Gatekeeper Interval-based propagation
DetectionStacksync polls Pinecone for changes on an incremental schedule, reading only records changed since the previous pass. No webhooks and no native change-data-capture feed.
DeliveryEach detected change is written to Gatekeeper through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- 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.
- Pinecone: Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.
What ships with Gatekeeper ⇄ Pinecone
Connect Gatekeeper and Pinecone for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Gatekeeper–Pinecone connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Gatekeeper or Pinecone 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 Gatekeeper or Pinecone record.
Observability
Monitoring
Track your Gatekeeper ⇄ Pinecone sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Gatekeeper and Pinecone.
How the Gatekeeper and Pinecone connectors work
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.
Pinecone
- Integration surface
- Two HTTP APIs: a control plane at api.pinecone.io (manage indexes, collections, backups, and, via the Admin API, projects and API keys) and a per-index data plane at the host returned by describe_index (upsert, query, fetch, update, delete, list). A gRPC data-plane transport is available through the official SDKs.
- Authentication
- API key in the Api-Key request header, scoped to one project; every request also sends an X-Pinecone-Api-Version header (date-based, e.g. 2025-10). The organization Admin API instead uses OAuth2 client-credentials (service accounts) via login.pinecone.io/oauth/token, passing a Bearer token to api.pinecone.io/admin (Enterprise).
- Change detection
- No webhooks and no native change-data-capture feed. Vectors carry no server-side update timestamp, so Stacksync detects changes by re-reading - paginating vector ids with the list operation (serverless indexes) and fetching by id, or by re-upserting from the source of truth. describe_index_stats bounds a resync with per-namespace counts.
- Capabilities
- read · write
- Rate limits
- Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.
How it works
How to connect Gatekeeper to Pinecone — 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 Gatekeeper and Pinecone with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Gatekeeper
connected
Pinecone
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Gatekeeper and Pinecone 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 · Gatekeeper ⇄ PineconeCustomers 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.
Gatekeeper PineconeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Gatekeeper and Pinecone integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Gatekeeper and Pinecone: authenticate both systems, choose the objects to sync (such as Gatekeeper's Vendors (Suppliers) and Files), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Pinecone side: Indexes, Vectors (records), Namespaces, Collections, plus custom fields where Pinecone exposes them. On the Gatekeeper side: Custom data groups, Users, Categories, Contracts. Stacksync auto-detects both schemas and converts types between the two systems.
-
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
-
Common patterns for Gatekeeper and Pinecone: Every output routes back to the right record; Build a retrieval corpus from Gatekeeper's records; Keep the model's knowledge current. Because each item is matched on a stable identifier, an Pinecone result always attaches to the record in Gatekeeper it was computed from, with no manual reconciliation.
-
Gatekeeper: 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. Pinecone: Two HTTP APIs: a control plane at api.pinecone.io (manage indexes, collections, backups, and, via the Admin API, projects and API keys) and a per-index data plane at the host returned by describe_index (upsert, query, fetch, update, delete, list). A gRPC data-plane transport is available through the official SDKs. Authentication: API key in the Api-Key request header, scoped to one project; every request also sends an X-Pinecone-Api-Version header (date-based, e.g. 2025-10). The organization Admin API instead uses OAuth2 client-credentials (service accounts) via login.pinecone.io/oauth/token, passing a Bearer token to api.pinecone.io/admin (Enterprise). Stacksync manages authentication, retries, and rate limits on both sides.
-
Pinecone: Hard request limits: an upsert is capped at 2 MB or 1000 records, filterable metadata at 40 KB per record, dense vectors at up to 20,000 dimensions, and query top_k at up to 10,000 with a 4 MB result cap. Gatekeeper: Gatekeeper is a Vendor and Contract Lifecycle Management (VCLM) platform; its core objects are Contracts, Vendors/Suppliers, and Files, alongside Risk, Spend, and Scorecard data. Stacksync's field mapping accounts for these differences between Gatekeeper and Pinecone without custom code.
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 380 integrations available for Gatekeeper and Pinecone.
Popular · 6 of 380
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Gatekeeper ⇄ Openai -
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Gatekeeper ⇄ Autopilot -
Pinecone ⇄ Lemlist -
Pinecone ⇄ Microsoft 365 -
Pinecone ⇄ Microsoft Dynamics 365
No integrations match .
