Your knowledge. One shared memory.
A second brain for your people and AI agents. Connect your documents, conversations, records and other knowledge, or upload it directly. It ingests what you choose so useful context stays available across tools, people and sessions. Coming soon, being rewritten in Rust and planned as open source.
- Connect or upload
- Ingest with sources
- Retrieve knowledge
- Use shared context
Institutional knowledge compounds
The problem
- The answer exists somewhere: a document, a conversation, a record or someone's experience.
- Every new person, task or AI session needs the same context explained again.
- Changing tools or losing a team member means losing part of what you know.
Many sources. One institutional memory.
You choose the sources. The memory keeps useful knowledge with its origins. People and MCP-compatible agents draw relevant context from the same memory, wherever the next task starts.
Planned product · source examples, not a connector list
What you connect or upload
Documents
notes and knowledge bases
Conversations
discussions and messages
Records
decisions and background
Your uploads
material you choose
Kanerva
Your institutional memory
- Knowledge and context
- Sources and evidence
- Access boundaries
Use the same memory
Your people
Find context and its sources
Your AI agents
Relevant context through MCP
New information + accepted correctionsContext for the next task
Ask about what you already know
Illustrative questions for the planned product. The answer depends on the information you connect and the access you allow.
Why did we choose this approach?
Find the discussion, the reasons behind the decision and the original material that supports it.
What do we know before this customer call?
Bring together relevant notes, previous conversations and agreed next steps.
What should a new teammate know?
Find the practices, background and decisions that usually take repeated explanations to pass on.
Who it is for
| Who | The pain | What they get |
|---|---|---|
| Teams with scattered knowledge | Information lives in many tools and people's heads. | Shared memory from the sources they choose to connect. |
| People working with AI agents | Each task requires explaining the same context again. | Knowledge that remains available across sessions and tools. |
| Organisations preserving expertise | Context disappears when people change roles or leave. | Institutional knowledge kept with its sources and access boundaries. |
What it does
A second brain for people and AI agents. Ingest the knowledge you connect or upload, keep it with its sources, and use the same memory across tools and sessions. Coming soon.
Ingests connected sources and uploaded information
Connect the knowledge you choose or upload material directly. Specific connectors and formats will be documented at release.
Keeps knowledge with its sources
Keep information, context and decisions attached to their origin and supporting evidence.
Serves memory to people and AI agents
Find institutional knowledge as a person or let an MCP-compatible agent retrieve relevant context from the same memory.
From scattered information to a grounded answer
Illustrative example: a question about a regional launch brings back a planning note, a customer conversation and an approved decision. Those sources explain the launch scope and when it should be reviewed. This shows the intended workflow, not a live product result or customer data.
Illustrative example · not customer data or a live product result
The question
Why did we choose a regional launch?
Asked by a person or an AI agent
Relevant knowledge
Planning note
The proposed launch scope
Customer conversation
Needs and expectations
Approved decision
The reason and review point
An answer you can check
Start with a region the team can support. Review the scope after the first launch.
Follow the answer to its sources
Planning note · Customer conversation · Approved decision
Connect or upload
Choose the information you want in memory: documents, conversations, records, knowledge bases or other material. Connector and format availability will be documented at release.
Ingest with context
Keep useful knowledge, its origin and the evidence behind it together, with boundaries on who can use it.
Retrieve what matters
Ask a question or start a task. People and agents use the relevant knowledge and can follow it back to its sources.
Keep the material and the meaning
The original sources
Documents, conversations and records give the memory its grounding. An answer should lead back to the information behind it.
What the organisation knows
Decisions, practices, lessons and context make that information useful beyond the moment it was created.
Relevant context for the next task
Retrieve the knowledge that helps with the question in front of you, rather than re-explaining the organisation's history each time.
Knowledge that carries forward
The goal is a memory you can keep building: new sources add context, and accepted corrections update what people and agents find next time.
Add what you learn
Bring new information into the same memory so the next conversation can build on it.
Correct what has changed
Keep corrections with their basis and evidence. A changed decision should not leave yesterday's answer looking current.
Change tools, keep the context
The memory belongs to you. MCP gives compatible agents a way to use it without rebuilding your knowledge for each tool.
Your memory. Your boundaries.
Planned product · memory under your control
Intake
You choose what goes in
Selected connections and uploads feed your memory.
Access
Private knowledge stays scoped
Widening access takes a review. Credentials are redacted when stored.
Outbound
Check what leaves
Text fails closed without a deny list, is scrubbed, and only its fingerprint is logged.
Choose what goes in
You choose the connected sources and uploaded material. Its service listens only on your machine.
Choose who can use it
Private knowledge stays scoped; widening its access takes a review.
Check what goes out
Outbound text fails closed without a deny list, is scrubbed, and only its fingerprint is logged. Credentials are redacted when stored.
Keep data as data
Instructions inside connected documents or messages are information, never commands.
Why Kanerva?
The name honours Pentti Kanerva, whose Sparse Distributed Memory was published by MIT Press in 1988. His theory explores associative memory: how a partial cue can retrieve relevant experience through similarity.
Start with a cue, find the context
You may remember a topic or a reason without remembering the exact document. The product idea is to connect that cue to relevant knowledge and its sources.
An institution remembers
People change roles and agents start fresh sessions. Knowledge should remain available to the organisation. The research inspires this goal; it is not a claim that the product implements the book's memory architecture.
Related services
Company Memory
An AI brain for your company: context engineering that keeps decisions with their sources, from your documents, runbooks and code, and serves them to your people and agents.
Frequently asked questions
Can we install it today?
Not yet. Kanerva is open source and coming soon. Until it is public, Company Memory is delivered as a service.
What can we connect or upload?
The scope is the knowledge you choose: documents, conversations, records, knowledge bases and other information. Multiple connectors and uploads are planned; specific supported connectors and formats will be documented at release.
How do agents use it?
Over MCP: compatible agents retrieve relevant knowledge and its sources from the same memory.
Does the memory grow automatically?
It grows through the information you choose to ingest and the corrections accepted into it. That means richer stored context, rather than a promise that every interaction improves an AI model.
How is sensitive information handled?
Access scopes limit who can use private knowledge. Outbound text is checked against a deny list, and credentials are redacted when stored.
Do you publish prices?
No. Scope and commercial terms are agreed with you.
Can my AI assistant read this page?
Yes. This page is ordinary HTML that needs no AI call, and it is also served as Markdown. Your assistant can read the published catalog or connect through F200.ai’s MCP interface and retrieve this offering by its stable identifier: product.engineering-memory.
Start with the problem.
We’ll start with a conversation.