For 3–15 person dev agencies

Team memory for AI-assisted client work

Your team already uses AI for delivery. The gap is not another chatbot — it is a shared record of what the client agreed to, what engineering decided, and what agents should treat as current truth across Slack, GitHub, and the IDE.

What breaks without shared team memory

Most agencies lose money on forgotten context, not hard problems.

Decisions live in threads

A scope call in Slack, a constraint in one senior dev's head, a client email nobody linked to the repo — each agent session starts cold.

Every agent reinvents context

Cursor, Claude Code, and Slack bots each guess from whatever is in the window. There is no governed layer they all query before acting.

Standups become re-sync theater

Hours go to reconstructing what already happened instead of shipping. Client updates require manual synthesis from scattered tools.

How Accord Book keeps the team aligned

Self-hosted project memory and change-control — not a wiki, not per-user chat memory.

Ingest where work already happens

Slack, GitHub, uploads, voice, and docs flow into project-scoped memory with timestamps and provenance. No manual tagging sprint.

Surface conflicts before rework

When a new ask fights an old decision, owners get a review surface with evidence — resolve, defer, or reopen. Humans publish truth.

Ground agents via MCP

Coding agents preflight against live constraints and failed approaches before they edit code. One memory layer, every surface.

Client-safe digests

Draft status from actual project memory — owners approve before anything reaches the client.

Also evaluating tools for per-client brand context? See client context memory for agencies. Category map: Accord Book in the AI memory landscape · vs Estratos.

Founding pilot — 3 spots remaining

We deploy on your infrastructure, connect Slack and GitHub with you, and validate on real client work. BYOK — your keys, your data boundary.

See pilot details →