Context Data

What is Context Data?

Context Data builds InterLock, a proxy that sits between AI agents and the data stores they use. Agents connect to InterLock instead of directly to Postgres, MySQL, S3, Slack, GitHub or HTTP APIs, and it supports the protocols they already rely on: HTTP, the Postgres wire protocol and the Model Context Protocol (MCP). Every request is authorized by role and policy, down to table and column for SQL sources, and anything not explicitly allowed is refused. Personal data such as a social security number is masked in results before the agent sees it. Risky writes, including updates, schema changes and a DELETE with no WHERE clause, wait for a person to approve them in a console, with optional Slack notices, and every decision lands in an append-only audit log. It deploys inside your environment with Docker Compose or Kubernetes. The company also sells a low-code data platform for embeddings and vector databases. Pricing is not published.

Use cases

• Limit which tables agents can query
• Mask sensitive fields in agent results
• Hold risky database writes for approval
• Audit every agent request to data
• Give each agent its own access key
• Connect agents to Postgres over MCP
• Build ETL pipelines into vector databases

Who it's for

• Platform engineers
• Security teams
• Data engineers
• AI teams

Why use Context Data

• Controls apply at the data layer, covering rows and columns that tool-level permissions do not reach.
• Adoption is a connection-string change, so agents and data stores stay as they are.
• Sensitive values are masked before they ever reach the model.
• The vendor publishes a feature status page, so beta and planned items are visible up front.

Context Data features

• Role and policy authorization down to table and column
• SQL parsed before it runs, deny by default
• Redaction of personal data before the agent sees it
• Risk classification with approval holds for risky writes
• Append-only audit log exportable as CSV
• One key and identity per agent
• MCP, Postgres wire protocol and HTTP support
• Cache scoped to each identity and source
• Docker Compose and Kubernetes (Helm) deployment

Context Data integrations

• PostgreSQL
• MySQL
• Amazon S3
• Slack
• GitHub
• Claude Code
• OpenAI Codex
• Google Gemini
• Pinecone
• Weaviate
• Qdrant
• Milvus
• LanceDB
• Supabase

How to get started with Context Data

1. Deploy InterLock next to your data with Docker Compose or Kubernetes.
2. Register data sources and define roles that allow or deny actions per source.
3. Issue each agent its own key and point it at InterLock instead of the database.
4. Add policies for redaction, rate limits and write risk.
5. Review held writes in the console and export the audit log when needed.

Context Data limitations

• You deploy and run it yourself as infrastructure
• Roles and policies must be written for each data source
• The site lists some capabilities as public beta and others as planned or disabled
• Pricing is not published

Context Data pricing

We couldn't confirm pricing on the official site. Check contextdata.ai for current prices.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Context Data alternatives

The main Context Data alternatives listed on Debuly are Formal, Metorial, AI for Database, Busabase, Cognee and LightAgent. The table compares pricing and platforms; order is A–Z.

1. 

   AI for Database

   Ask your PostgreSQL, MySQL or Google Sheets data questions in plain English and get charts, dashboards and automated alerts.

   Developer platformsProductivityAutomation and agents
   FreemiumVisit AI for Database (official site)
2. 

   Busabase

   MIT-licensed database and workspace where AI agents store data, docs, skills and apps, available on desktop, self-hosted or in Busabase Cloud.

   Developer platformsAutomation and agentsProductivity
   Open sourceVisit Busabase (official site)
3. 

   Cognee

   Open-source memory engine for AI agents that combines vector search, knowledge graphs and relational storage, with a hosted cloud.

   Developer platformsAutomation and agentsResearch and search
   FreemiumVisit Cognee (official site)
4. 

   Formal

   Network security and access control proxy that enforces least privilege for AI agents and engineers across databases, APIs and MCP servers.

   Developer platformsAutomation and agents
   Contact salesVisit Formal (official site)
5. 

   LightAgent

   Compact open-source Python framework for building agents with tools, memory, MCP, tracing, guardrails and multi-step workflows.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit LightAgent (official site)
6. 

   Metorial

   Control plane for AI agents with 1,000+ managed MCP integrations, per-user permissions, SSO and audit logs behind one connection URL.

   Developer platformsAutomation and agents
   FreemiumVisit Metorial (official site)

Context Data FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from contextdata.ai · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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