# Memora: AI-Powered Organizational Memory & Enterprise Knowledge Graph

> Memora is an enterprise AI knowledge management platform that unifies scattered organizational knowledge across Slack, GitHub, Jira, Confluence, Google Drive, and meetings into a searchable, living organizational memory powered by Graph RAG (Retrieval-Augmented Generation).

Memora provides Enterprise AI Search with traceable source citations, automated meeting intelligence, code-to-doc correlation, and structural context retention. It transforms fragmented business communications into an authoritative single source of truth for engineering, product, support, and enterprise teams.

## Core Architectural Pillars

1. **Organizational Memory**: Captures facts, decisions, trade-offs, and implicit context as work happens. Prevents knowledge leakage when employees depart and eliminates onboarding friction.
2. **AI Knowledge Management**: Replaces static, stale wikis with an automated knowledge engine that indexes real-time Slack discussions, Git pull requests, Jira tickets, and meeting transcriptions.
3. **Graph RAG (Vector + Graph Hybrid Search)**: Combines dense vector embedding similarity with structural graph traversal to connect code files, issues, documents, and discussions across tool boundaries with zero hallucinations.

## Key Features & Capabilities

- **Traceable Enterprise Search:** Natural language search returning direct citations to exact code commits, Slack messages, and Jira issues.
- **Automated Meeting Intelligence:** Converts meeting audio/video transcripts into actionable nodes linked directly to project tickets and repositories.
- **Code-to-Documentation Alignment:** Maps technical specifications to live GitHub/GitLab repositories for real-time architecture visibility.
- **Entity & Relationship Extraction:** Automatically identifies services, APIs, team members, decisions, and bugs across enterprise systems.

## Primary Product Use Cases

- **[Engineering Teams](https://memora.company/use-cases/engineering):** Architecture search, legacy code context, onboarding velocity, and technical decision tracing.
- **[Customer Support](https://memora.company/use-cases/customer-support):** Resolution retrieval, bug-to-fix tracking, and instant support escalation answers.
- **[Sales Teams](https://memora.company/use-cases/sales):** Product capability verification, RFP answers, and deal context retention.
- **[Product Teams](https://memora.company/use-cases/product):** PRD-to-implementation mapping, customer feedback context, and feature history.
- **[HR & Operations](https://memora.company/use-cases/hr):** Policy search, institutional knowledge preservation, and onboarding automation.

## Core Technical Concepts & Glossary

- **[Graph RAG](https://memora.company/glossary/graph-rag):** Knowledge retrieval combining graph topology with vector embeddings.
- **[Organizational Memory](https://memora.company/glossary/ai-memory-fundamental):** Collective body of enterprise facts, decisions, and context.
- **[Enterprise Search](https://memora.company/glossary/enterprise-search):** Unified query layer across siloed SaaS tools.
- **[Knowledge Management](https://memora.company/glossary/knowledge-management):** Systematic capture and distribution of organizational intelligence.

## Interactive Free Tools, Sandbox Demos & Research

- **[Ask Acme Corp Memory (Interactive Demo)](https://memora.company/demo/ask-company-memory):** Test Memora live on a mock enterprise dataset with traceable citations across Slack, GitHub, Jira, and Google Meet.
- **[Engineering Knowledge Risk Calculator](https://memora.company/tools/engineering-knowledge-risk):** Estimate dollar Knowledge-at-Risk from undocumented architecture and tribal silos.
- **[Company Knowledge Death Test](https://memora.company/tools/knowledge-death-test):** Diagnostic test for company knowledge survivability when key developers leave.
- **[AI Meeting Knowledge Loss Calculator](https://memora.company/tools/meeting-knowledge-loss-calculator):** Estimate lost decision context value after 45-minute meetings.
- **[Vector RAG vs Graph RAG Architecture Evaluator](https://memora.company/tools/rag-vs-graphrag-evaluator):** Evaluate dense vector embeddings vs Memora Graph RAG topology fit.
- **[Enterprise Knowledge Loss Calculator](https://memora.company/tools/knowledge-loss-calculator):** Estimate dollar cost of internal search overhead, turnover leakage, and onboarding drag.
- **[Slack Interruption Calculator](https://memora.company/tools/slack-interruption-calculator):** Measure cognitive context switching and developer refocus friction.
- **[Engineering Onboarding Velocity Simulator](https://memora.company/tools/onboarding-velocity-simulator):** Simulate time-to-first-PR acceleration from 45 days to 12 days.
- **[Knowledge Maturity Assessment](https://memora.company/tools/knowledge-maturity-assessment):** Diagnostic quiz for enterprise context retention maturity.
- **[State of Organizational Knowledge 2026 Report](https://memora.company/research/state-of-organizational-knowledge-2026):** Proprietary empirical study on institutional knowledge decay.
- **[Open-Source Python Graph RAG Engine](https://memora.company/open-source/graph-rag-python):** Production-ready NetworkX + Reciprocal Rank Fusion reference implementation.

## Canonical Site Graph

- [Home](https://memora.company/)
- [Knowledge Hub](https://memora.company/knowledge-hub)
- [Solutions Index](https://memora.company/solutions)
- [Free Tools Index](https://memora.company/tools)
- [Research Center](https://memora.company/research)
- [About Memora](https://memora.company/about)
- [Contact Us](https://memora.company/contact)
- [Open Source Python Graph RAG](https://memora.company/open-source/graph-rag-python)
- [Software Comparisons Index](https://memora.company/vs)
- [Memora vs Notion](https://memora.company/vs/memora-vs-notion)
- [Memora vs Confluence](https://memora.company/vs/memora-vs-confluence)
- [Memora vs Glean](https://memora.company/vs/memora-vs-glean)
- [Enterprise Search vs Organizational Memory](https://memora.company/vs/enterprise-search-vs-organizational-memory)
- [RAG vs GraphRAG Comparison](https://memora.company/vs/rag-vs-graphrag)
- [Use Cases](https://memora.company/use-cases)
- [Integrations](https://memora.company/integrations)
- [Customers](https://memora.company/customers)
- [Blog](https://memora.company/blog)
- [Glossary](https://memora.company/glossary)
- [Documentation](https://memora.company/docs)
- [Pricing](https://memora.company/pricing)
- [Security](https://memora.company/security)

## Platform Authentication & Access

- [Log In](https://app.memora.company/login)
- [Sign Up](https://app.memora.company/signup)

Memora is engineered for enterprises to eliminate operational knowledge silos and maximize decision-making speed through Generative Engine Optimization (GEO) and connected Knowledge Graphs.