Anchor is the shared memory every AI session is missing — on every team, not just engineering.
Every AgentOS agent — and every Cursor, Claude Code, or Windsurf session — gets your team's accumulated knowledge injected automatically. No new tool, no workflow to migrate into.
Free 60-day trial · Your data stays on your infrastructure
THE SAME QUESTION. TWO VERY DIFFERENT ANSWERS.
Generic. No memory of your team's decisions. Could be wrong for your stack.
AI is powerful for one person. It breaks down across a team.
Every AI session starts with zero knowledge of your team — your context, decisions, and standards get re-taught by every person, every time.
Dev 2 asks which DB to use — AI gives a generic list: Redis, Postgres, DynamoDB, Memcached...
Sales asks which objection Acme raised last quarter — AI has no idea; it left with whoever took that call.
New hire needs 2 weeks of shadowing just to learn how the team actually works.
Alex leaves. Three years of decisions and context leave with him.
You re-explain your standards at the start of every single AI session.
Dev 2 asks which DB — gets: "Redis. Your team chose it in March because you already run it on AWS."
Sales asks about Acme — gets: "Pricing, raised in March. Lead with the annual-plan discount."
New hire is productive on day 2, not week 2.
Alex leaves. His knowledge stays in Anchor forever.
Every AI session starts with your full team context — zero re-explaining.
AI work that scales with your whole team.
What used to live in one person’s head now compounds across the whole team.
New hires hit the ground running
New hires query the org memory instead of shadowing a senior teammate for weeks. They start contributing on day one, in any department.
Knowledge stays when people leave
Every decision, pattern, and fix a person made stays in org memory. Knowledge transfer becomes a 5-minute query, not a 5-week process.
Your standards, auto-enforced
Define your standards once — technical, brand, or process. Every AI session, from Cursor and Claude Code to your AgentOS agents, gets them injected automatically.
How Anchor captures and injects context.
Two flows run on every AI session — one while you work, one silently in the background. Your team notices neither. (Shown here for coding tools — AgentOS agents write into the same memory directly.)
$ ANTHROPIC_BASE_URL http://localhost:8765 → POST /v1/messages forwarding to Anthropic... ← 200 OK · streamed to cursor
{ "category": "tech_decision", "content": "Use Redis for sessions — already on AWS", "importance": 0.92 }
get_org_standards() ✓ Redis for sessions ✓ No direct DB from frontend ✓ Auth → check JWT expiry first AI now knows your team.
Local proxy on your infra · stored on Nyas (Postgres + pgvector) · injected via MCP into every AgentOS agent and coding tool
Everything runs on your infrastructure.
Anchor doesn't ship its own database — it runs on Nyas, deployed inside your environment. Your proxy, your data, your control — nothing touches our servers.
Intercepts every AI API call silently
Pulls decisions + patterns from conversations
Stores org memory as semantic embeddings — the layer underneath Anchor
Injects relevant context at every session start — the same feed every AgentOS agent reads from
Built for teams that can't afford to leak IP.
Most AI tools send your data to the cloud. Anchor doesn't. The entire memory layer runs on your infrastructure — no Anchor server ever sees your data.
Where did this come from?
Every fact Anchor serves traces back to the session it came from — not a guess dressed up as an answer.
Is it still true?
Correct a decision once and the next session gets the correction. Nothing stale gets served twice.
Who can see it?
Tenant-isolated by default. Your team's memory is never mixed with — or used to train — anyone else's.
Your infra, full stop
The proxy, vector database, and MCP server all run inside your environment. Anchor has no cloud backend that touches your data.
No raw work stored
We never store your raw code, documents, or conversations. The extractor only captures high-level decisions — "use Redis for sessions," "lead with the annual-plan discount" — not the underlying work.
Dedicated instance
Your org memory is isolated. There is no shared multi-tenant database, and nothing you capture is ever used to train another org's model.
Your API keys stay yours
The proxy forwards your existing Anthropic / OpenAI keys unchanged. Anchor never reads, stores, or retransmits them.
Full audit trail
Every memory capture is logged with source tool, team member, timestamp, and session ID. You control what gets retained or deleted.
Corrections update instantly
Flag a decision as outdated and Anchor updates the memory immediately — the next session gets the fix, not the stale answer.
Know exactly how your team codes with AI.
Anchor captures everything that happens in AI sessions — which means it can surface insights no other tool can. Token costs, contribution scores, and personalised learning paths, all from data already flowing through the proxy.
Database optimisation patterns
12 sessions asking about slow queries this month
API security & auth flows
Repeated questions about JWT and session handling
React state management at scale
8 sessions debugging Zustand and prop drilling
Built for teams already leaning on AI.
If your team is already using AgentOS agents or AI coding tools like Cursor, Claude Code, and Windsurf — Anchor is the shared memory layer that makes it scale past a single person, without anyone changing tools.
“We went from spending two weeks onboarding a new dev to having them commit meaningful code on day two. The context was just there.”
— CTO, early access team · 18 developers
We respond same business day · No sales pressure