Intent Routing
The layer that reads each incoming request, figures out what the person actually wants, and sends it to the right system, tool, or team — safely.
Think of the mail room in a big building. Every envelope that arrives gets read just enough to know where it should go — accounting, legal, the third floor — and lands in the right mailbox instead of a pile marked everyone. Intent routing does that for every request your AI receives: understand first, then send it to the right place.
The problem, in plain words
An AI assistant that can do many things has a new problem: knowing which thing to do. Someone types why did revenue drop last week — is that a question for the finance dashboard, the sales database, or a person? Someone else types cancel my subscription — that is not a question at all, it is an action, and a risky one. When the system guesses wrong, it searches the wrong data, triggers the wrong workflow, or gives a confident answer built on the wrong source. Most AI failures in real companies are not the AI being dumb — they are the request landing in the wrong place.
What we set up
We put a routing layer between the people asking and the systems answering. When a request arrives, it is read for what the person is actually trying to accomplish — not just the words, but the context and what this user is allowed to do. The router then picks the right capabilities for the job: which data sources to consult, which tools or workflows to run, whether an AI agent can handle it or a human should. Every request runs inside guardrails — permissions, validation, and an audit trail (a record of who asked for what and what happened) — and the router learns from outcomes over time, so requests that once got misrouted start landing in the right place.
How it works, step by step
- A request arrives
Typed into a chat, a form, or an internal assistant — in whatever words the person naturally uses.
- The router works out the goal
It reads the language, the context, and the user's permissions to decide what is really being asked for: information, an action, or an escalation.
- The right capabilities are picked
The request maps to specific tools, data sources, and workflows — a revenue question goes to analytics, a subscription change goes to the billing workflow.
- It runs with guardrails
Permissions are checked, inputs validated, and everything logged. Actions with consequences still wait for a human yes.
- Routing improves with feedback
Misrouted requests get corrected, and the corrections teach the router. Accuracy grows on your real traffic, not a demo script.
What changes for you
Before: your AI is a clever chatbot that answers questions but stalls, guesses, or does the wrong thing the moment a request needs a real system behind it. After: a support question routes to identity checks and ticketing, an analytics question routes to the right dashboards and databases, and each request lands with the agent or team that can actually handle it — with a log explaining every decision. What it won't do: it won't take consequential actions on its own — routing decides where a request goes, and risky steps still stop for human approval.