Skip to main content

What Is Memora? How It Works, What It Connects and Who It's For

Memora connects your team's Slack, GitHub, Jira, docs and meetings, and answers questions with links to the sources. Learn how it works, what it costs and where it fits.

What Is Memora? How It Works, What It Connects and Who It's For
⚡TL;DR

Memora is a shared memory for teams. It connects the tools where work happens (chat, code, tickets, documents and meetings), links the pieces that belong to the same decision, and answers questions in plain language with a link to every source it used. It is built mainly for engineering and product teams, has a free plan, and also offers AI pull-request reviews and an MCP server for AI coding assistants.

What is Memora?

Memora

Memora is an AI knowledge product from memora.company. It connects a team's conversations, code, tickets, documents and meeting transcripts, and answers questions about that work with links back to the original sources.

Most teams already know the answers to their questions. The trouble is finding them. A single decision can start in a Slack thread, be argued over in a pull request, be tracked in a Jira ticket and end up summarized in a document. Months later, anyone who needs the full story has to search each tool separately or find the one person who remembers.

Memora works on top of those tools. Nobody has to move their work or write things up a second time.

Key Takeaways

  • Works with your existing tools: Slack, GitHub, GitLab, Jira, Linear, Confluence, Notion, Google Drive, Gmail, Microsoft Teams, Zendesk, Figma and Zoho, plus meeting transcripts from Google Meet, Zoom and Teams.
  • Answers you can check: every answer links to the threads, pull requests, tickets and documents it came from.
  • Respects existing permissions: answers only draw on content the person asking can already open in the connected tools.
  • Built for engineering and product work: it links related work, so the reasoning behind a decision stays attached to it.
  • Private by default: data is encrypted in transit and at rest, and your content is never used to train AI models. See the security page.

How Memora works

1. Connect your tools. An administrator connects each tool and chooses which workspaces Memora can read. Memora follows the access rules already set in each tool.

2. Memora links related work. Conversations, pull requests, tickets, documents and meetings about the same topic are connected. A decision stays with its history: who discussed it, what it was based on, and what changed afterwards.

3. Ask in plain language. Ask the way you'd ask a colleague, for example "Why does checkout retry payments three times?" or "What did we decide about the onboarding redesign?" You get a direct answer, with each source linked underneath so you can open it and check.

Like any AI system, Memora can get things wrong. Showing the sources for every answer is how we make errors easy to spot instead of hidden.

What you can do with it

  • Find the reasoning behind a change. Engineers can see the discussion, ticket and pull request behind a piece of code without hunting through each tool.
  • Help new people get up to speed. New hires can answer many of their own questions from past discussions instead of interrupting senior colleagues.
  • Keep context when people move on. Decisions and their reasoning stay with the team, not just with the people who were in the room.
  • Review pull requests with past decisions in mind. Memora's AI PR reviews are included in every plan.
  • Give AI coding tools your team's context. Memora's MCP server lets Claude Code, Cursor and other MCP clients use the same context your team sees.
  • Bring meeting decisions in. Transcripts from Google Meet, Zoom and Teams are linked with the rest of your work.

What Memora is not

  • Not a wiki. You don't write pages in Memora. It works from what your team already produces. If you want a curated, hand-maintained knowledge base, a wiki is still useful alongside it.
  • Not company-wide enterprise search for every system. Memora focuses on the tools engineering and product teams use. If you need one search layer across hundreds of systems, see our Glean alternatives guide.
  • Not self-hosted. Memora runs on AWS in the USA and EU, with dedicated single-tenant infrastructure on the Enterprise plan. It doesn't hold SOC 2 or ISO 27001 certification today; we share our security documentation and DPA on request.

Pricing

Memora has a Free plan (one user, 25 questions a month), Starter at $39 a month (up to 2 members), Pro at $99 a month (up to 10 members) and a custom Enterprise plan with dedicated infrastructure. Full limits are on the pricing page.

Frequently asked questions

What is Memora?

Memora connects your team's chat, code, tickets, documents and meetings, and answers questions about your work in plain language, with links to the original sources.

How is it different from searching in Slack or Google Drive?

Built-in search looks inside one tool and returns a list of matches. Memora looks across all your connected tools at once, connects the conversations, pull requests, tickets and documents that belong together, and gives a direct answer with the sources attached.

Does our team have to write documentation for Memora?

No. Memora works from the conversations, code reviews, tickets and documents your team already produces.

Can people see answers from content they don't have access to?

No. Answers only draw on content each person can already open in the connected tools.

Is Memora the same as Memora Health?

No. Memora (memora.company) is an AI knowledge product for software and product teams. Memora Health is a separate, unrelated digital health company. If you're looking for patient care tools or Memora Health careers, please visit their website.

Next steps

Essential Organizational Memory & AI Architecture

Explore Memora's foundational guides on Graph RAG, persistent AI memory, and automated knowledge discovery:

⚡ Token Cost & Savings Calculator →
Calculate 1M token context window waste vs Graph RAG
What is Organizational Memory? →
The complete enterprise context framework
Top 7 Glean Alternatives (2026) →
Compare enterprise AI search & Graph RAG platforms
MPC vs MCP in AI Explained →
Multi-Party Computation vs Model Context Protocol
LLM Memory Management Guide →
4-tier memory hierarchy for autonomous coding agents
Slack & Jira KM Automation →
Capture decisions passively with zero workflow friction
Model Context Protocol (MCP) Hub →
Connecting IDEs & AI agents to enterprise memory
Knowledge Loss ROI Calculator →
Calculate annual engineering context loss costs
MCP Server Security & CISO Guide →
Prevent prompt injection & tool privilege escalation
AI Screen Memory & Ambient Context →
Privacy-first local OCR capture for enterprise teams
Corporate Memory Glossary Definition →
Explicit vs tacit context & corporate amnesia prevention
Quick Knowledge Check

Why do standard vector search systems fail on complex technical context?

Was this article helpful?