$31.5B lost annually to poor knowledge sharing — IDC

Your organisation's memory

When someone leaves, their knowledge walks out the door. Memoria captures decisions, context, and rationale as a byproduct of AI-assisted work — so institutional knowledge compounds instead of disappearing.

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Memory that compounds

Traditional knowledge management asks people to document. Memoria captures knowledge as work happens.

Knowledge captures itself

With Memoria

Decisions, rationale, and lessons are captured as a byproduct of normal AI-assisted work — available to any agent in your stack, next session or next quarter.

Without

Someone has to stop, write it down, file it in the right place, and hope people find it.

Ask a question, get an answer

With Memoria

Semantic search with recency weighting surfaces what's relevant, not just what matches keywords. Yesterday's architecture decision ranks higher than last year's — unless you ask for history.

Without

Guess the right search term, scroll through pages, and piece together the answer yourself.

Decisions linked to outcomes

With Memoria

A knowledge graph connects decisions to their rationale, trade-offs, and downstream impact.

Without

Flat documents with no links. Context lives in someone's head or a Slack thread from six months ago.

Works with your stack, not instead of it

With Memoria

Model-agnostic and MCP-native. Claude, GPT, Gemini, Llama — if it speaks MCP, it gets memory. No SDK lock-in, no vendor dependency.

Without

AI bolted on as a chatbot that searches your docs. No real integration, no persistent context.

Built for how you work

Different teams lose knowledge in different ways. Memoria speaks your language and fits your workflow.

For Engineering Teams

Stop re-litigating decisions. Every architectural choice, every trade-off is captured and searchable.

The decision was made six months ago but nobody knows why. Memoria maintains a persistent decision trail — context, rationale, and trade-offs captured during normal work. New starters query the team's accumulated memory from day one.

  • Complete decision trail for every architectural choice
  • Persistent context that survives team changes
  • Semantic search — ask why, not just what
  • Progressive retrieval: recent context first, deep history on demand
For Councils & Government

When a works manager retires, their 20 years of knowledge is queryable by their replacement from day one

Institutional knowledge retention shouldn't depend on handover documents nobody reads. Memoria captures decisions, compliance context, and operational know-how as a byproduct of AI-assisted work — ready for the next person before the last one leaves.

  • Institutional knowledge preserved through staff turnover
  • Compliance-ready audit trails with full decision history
  • Seamless handover — no documentation drives required
  • Self-hosted for data sovereignty and residency requirements
For AI-Native Teams

Your agents are only as good as what they remember. Give them organisational context that persists across sessions.

Most AI tools forget everything between sessions. Memoria is the context layer that gives your agents organisational memory — decisions, lessons, and rationale flow between tools automatically via MCP. Agent-native, not another database.

  • MCP-native — any compatible agent gets memory
  • Organisational context, not just individual chat history
  • Deploy via Docker in under five minutes
  • Local embeddings via Ollama — your data stays yours

Common questions

Is my data sent to the cloud?

No. Memoria is self-hosted on your infrastructure. Your data never leaves your network. Embeddings are generated locally via Ollama — there are no external API calls for core functionality.

Do I need to change how I work?

No. Memoria captures knowledge as a byproduct of your existing AI-assisted workflow. If your team already uses AI coding assistants or chat tools, Memoria slots in via MCP with no process changes.

What AI models does it use?

Local embeddings via Ollama (nomic-embed-text) for semantic search. No external API calls for core functionality. You bring your own LLM for generation — Memoria is the memory layer, not the model.

How is this different from Confluence or Notion?

Those are knowledge bases you have to maintain. Memoria is a memory system that maintains itself. Knowledge is captured during normal work, not written up after the fact. And it's designed for AI agents, not just humans browsing pages.

Does it work with my existing tools?

Memoria is MCP-native. It works with Claude Code, and any MCP-compatible agent or tool. If your stack speaks MCP, it gets organisational memory.

How long does it take to set up?

Deploy via Docker in under five minutes. Memoria ships as a single Docker Compose stack — Qdrant for vector storage, Ollama for embeddings, and the Memoria service itself. No complex infrastructure required.

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