Context for agents

Claude with internal data, sources and permissions

Use Claude with your company documents, knowledge bases and systems without giving up trusted sources, controlled scope and traceability. Chatydata prepares the governed context Claude consumes via MCP or API.

Assess how to prepare your data for agents with Claude

Why using Claude with internal data needs a context layer

Connecting Claude to company data is what turns a great model into a useful agent — but it is also where the risk lives. Claude reasons well and uses tools, yet it is only as reliable as the sources it receives. The blind spot is never the model: it is where the knowledge it uses comes from.

An excellent Claude answering from outdated documents, contradictory sources or data the user should never see still errs — with confidence. That is why using Claude with internal data calls for a layer that prepares and governs the context before it reaches the model.

Where Chatydata fits

Chatydata prepares and governs the context Claude consumes. It organizes the sources, defines which ones count, controls access scope per workspace and collection, records the sources consulted in each interaction and measures knowledge gaps.

In practice, Claude remains the runtime; Chatydata ensures that what reaches it is trusted, authorized and auditable — delivered via MCP or API.

  • Organizes the context: Ingests and normalizes company sources into versioned collections.
  • Defines sources: Curates what may feed the agent and excludes untrusted content.
  • Controls scope: Permissions per workspace and collection determine what Claude can access.
  • Records consulted sources: Each answer carries a trace of which documents supported it.
  • Measures gaps: Shows where context is missing for Claude to answer with confidence.

Why this matters

Without a governed-context layer, connecting Claude directly to enterprise sources reproduces the classic risks of agents in production:

  • Confident, wrong answers. Outdated sources lead Claude to assert something incorrect with confidence.
  • Data exposure. Without scope, Claude can access and reveal restricted content.
  • No traceability. Without a record of sources, there is no way to audit where an answer came from.
  • Context lock-in. Tying context only to Claude makes it harder to use other runtimes later.

Architecture: Chatydata + Claude

Claude connects to Chatydata’s governed context via MCP or API. Retrieval respects scope and permissions before any passage reaches the model, and every query is recorded.

Fontes

Drive, SharePoint, ERP, CRM, PDFs, APIs

Chatydata · Context Engine

Organiza · versiona · governa · observa o contexto

Runtimes

via MCP · API · conectores · pipelines

Use cases with Claude

The combination fits well in cases that require reasoning over extensive, controlled knowledge:

Long-document analysis

Claude reasons over contracts and reports from approved, versioned sources.

Internal assistant

Answers about policies and processes with scope per area and a traceable source.

Specialized support

Technical support grounded in a governed knowledge base.

How to start with an assisted pilot

The recommended path is an assisted pilot: choose a high-value use case, prepare and govern that case’s sources, connect Claude via MCP or API and measure results with observability. In a few weeks you validate the gain with controlled risk.

The readiness assessment helps choose the case and design the initial scope before implementation.

Frequently asked questions

Does Chatydata replace Claude?

No. Claude is the runtime that executes the reasoning and generates answers. Chatydata is the layer that prepares and governs the context Claude consumes. They are complementary: you keep Claude and gain control over the knowledge it uses.

How does Claude consume Chatydata’s context?

Via MCP (Model Context Protocol) or via API. Retrieval respects scope and permissions before delivering any passage to the model, and every query is recorded for audit.

Can I use Claude and another runtime at the same time?

Yes. The governed-context base is independent of the runtime. The same collection can serve Claude and, for example, OpenAI Agents or LangGraph simultaneously, without duplicating the preparation.

Does this solve Claude’s hallucinations?

There is no absolute guarantee against a model’s errors. What Chatydata does is reduce the risk: provide trusted, up-to-date sources in the right scope, and make what was consulted traceable — which substantially improves reliability in practice.

Free assessment: we design the pilot and the context scope for Claude.

Assess how to prepare your data for agents with Claude