Internal copilots that learn how your company actually works.
Give each team a governed AI brain — isolated by department, auditable, and able to run where your data has to stay.
Internal copilots that learn your company's processes, stay separated by department, keep an auditable trace behind every answer, and run where your data is required to live.
Internal AI tools forget your processes, so employees re-explain the same context daily. They pool knowledge across departments that should stay separated, produce answers no one can audit, and often can't run where sensitive data is required to live. That's a governance problem, not just a UX one.
What changes with a memory layer.
- Knowledge
Employees re-explain the same context every day.
- Isolation
Knowledge pools across departments that should stay apart.
- Governance
Answers no one can audit or explain.
- Data residency
Sensitive data has to leave your environment.
- Knowledge
Institutional memory compounds inside each team.
- Isolation
A separate brain per department, with no crossover.
- Governance
Every answer has a recorded, source-linked trace.
- Data residency
On-prem / single-tenant — data never leaves.
The same loop — three calls, your model in the middle.
prepare
Builds the brief — memory + written identity + coherence gate. No LLM call.
your model
You call your own model — your provider, your key. Khwan never touches it.
record
Persists the turn and learns from it — so the next prepare is sharper.
Every run compounds. The brief gets tighter, cost drops, and answers sharpen — the memory that thinks, not just recalls.
A brain per department
Isolated cores keep each team's knowledge separate — Finance, Legal, Support, Engineering — with no crossover, from one account. Institutional memory compounds inside each team instead of resetting every session.
Governed and auditable
Every memory write and gate decision is recorded with its source, so you can answer 'why did it say that?' for review and compliance. A written policy becomes the coherence gate the agent is held to.
Runs where your data lives
Deploy hosted, or single-tenant and on-prem, so regulated or sensitive data never leaves your environment. You bring your own model — open weights included — with the controls and SLA your security team expects.
Outcomes, not orchestration.
- Knowledge that compounds instead of resetting each session
- Per-department isolation with no crossover
- An auditable trace behind every answer
- On-prem / single-tenant for data residency
Platform, IT, and AI teams rolling out internal copilots across an organization.
The whole integration — three calls
import { Khwan } from "@khwan/client";
const kw = new Khwan({ apiKey: process.env.KHWAN_API_KEY });
// 1 — Khwan builds the context (memory + coherence). No LLM call.
const turn = await kw.prepare(input);
// 2 — You call your own model. Your provider, your key.
const answer = await yourModel(turn.messages);
// 3 — Hand it back. Khwan persists + learns; next prepare is sharper.
await kw.record(turn, answer);