Contextually is the governed runtime where AI agents compute over your data. Your agent writes the analysis code; we run it next to your stores — warehouse, Postgres, SaaS, object storage — and return only the answer to context. The data never makes the round trip.
MCP made it easy to connect agents to everything. It also made it easy to route every intermediate result through the model — and at enterprise scale, that breaks down fast.
Connect a few dozen servers and tool schemas consume six figures of tokens before the agent reads your first request. Cost and latency climb before any work starts.
Pulling records tool-call by tool-call means the model reads data just to move it. Analysis over millions of rows is impossible when each one costs tokens twice.
Data routed through context is data your model provider sees. For regulated workloads, that's not a cost problem — it's a blocker your security team is right to raise.
Orders in the warehouse. Accounts in Postgres. Events in object storage. Contracts in the CRM. Every question worth asking spans silos — and an agent that can only reach one store at a time can only give you a fraction of the truth. Contextually gives your agent one governed way to bring it all together: compute across everything you have, distill it to what the goal requires, and hand back the ultimate context — everything relevant, nothing else.
It's the reason for the name.
Entitlement-aware connectors for your warehouse, databases, SaaS, and object storage. Row-level policy and least-privilege credentials are enforced in the runtime — the agent inherits the caller's permissions, never more.
One purpose-built program instead of a thousand tool calls — SQL, Python, ML models — executed in an isolated sandbox with filters and aggregations pushed down into the source engines.
The runtime materializes the result — a summary, a table, a chart — and hands back just that. Your token bill stays flat, and raw records never enter the prompt. One MCP server, contextually, is the whole integration.
A dashboard answers the questions someone predicted last quarter. Your agent answers the one you just asked — by writing the exact program the goal requires, running it against your stores, and returning only the output connection to context. Three different goals, three different programs:
Your team prompts the agent they already use — Claude, ChatGPT, Copilot, your own. The agent calls Contextually over MCP, the runtime retrieves and processes the data, and the result goes wherever it's needed: back into the conversation, or straight out through another MCP — Slack, email, your CRM.
The reason enterprises stall on agentic data access isn't capability — it's accountability. Contextually records what code ran, what data it touched, and what left the perimeter. Which, by default, is nothing.
Deploy the runtime inside your own cloud perimeter. Your data connects over private networking and never transits our infrastructure.
Row-level access, masked columns, and read-only enforcement apply to the code the agent writes — not just the tools it's shown.
Each result links back to the exact code, sources, and rows that produced it. When an agent reports a number, you can show your auditor where it came from.
Contextually is delivered as a single MCP server. Add it to Claude, ChatGPT, Copilot, or your in-house agent framework and every connected assistant gains a governed compute lane to your data — no per-tool integration, no schema sprawl, no new agent platform to buy.
Book a demo and we'll walk through your stores, your policies, and a live run of an agent processing data at real scale — then map the path to a deployment in your VPC.