Transform fragmented organizational knowledge into an intelligent AI-powered knowledge system.
Stop searching. Start asking.
KnowledgeOS is an enterprise-grade AI knowledge intelligence platform that converts scattered company information into a centralized, searchable, and continuously improving organizational knowledge system.
Modern organizations store critical information across multiple disconnected platforms:
- Engineering documentation
- GitHub repositories
- Confluence pages
- PDFs
- Meeting notes
- Slack conversations
- Incident reports
- Product specifications
- HR policies
- Training materials
KnowledgeOS uses Artificial Intelligence, Retrieval Augmented Generation (RAG), Semantic Search, Knowledge Graphs, and AI Agents to create an intelligent company knowledge brain.
Employees can ask questions in natural language and receive accurate answers with:
- Step-by-step explanations
- Source references
- Related documents
- Code examples
- Previous discussions
- Responsible teams
- Recommended actions
Organizations lose productivity because valuable knowledge is:
- Distributed across multiple systems
- Difficult to search
- Not properly documented
- Quickly outdated
KnowledgeOS solves this problem by creating a single intelligent layer over company knowledge.
Example:
Employee Question : How do I deploy the payment service to production?
KnowledgeOS AI Response:
Production Deployment Process:
Create Docker image Push image to container registry Apply Kubernetes deployment configuration
Related Resources:
✓ Payment Architecture Documentation ✓ Kubernetes Deployment Guide ✓ Previous Deployment Incident #442
Owner Team:
Platform Engineering
Centralized enterprise knowledge repository.
Supported sources:
- DOCX
- Markdown
- TXT
- CSV
- Excel
- Presentations
Integrations:
- GitHub
- GitLab
- Bitbucket
KnowledgeOS understands:
- Source code
- README files
- Architecture documents
- Issues
- Pull requests
- Code comments
Connect:
- Confluence
- Google Drive
- Notion
- Slack
- Microsoft Teams
KnowledgeOS automatically transforms raw information into AI-ready knowledge.
Processing Pipeline:
Document Upload
|
Text Extraction
|
Content Cleaning
|
Document Classification
|
Chunk Generation
|
Embedding Creation
|
Vector Storage
|
Knowledge Available
The system extracts:
- Topics
- Entities
- Relationships
- Keywords
- Metadata
- Importance
Employees interact with company knowledge using natural language.
Instead of searching:
authentication documentation
Ask:
How does authentication work in our platform?
AI provides:
- Context-aware answers
- Citations
- Related resources
- Follow-up suggestions
Features:
- Conversation memory
- Context understanding
- Source verification
KnowledgeOS uses RAG architecture to provide accurate enterprise answers.
Traditional AI:
Question
`|
LLM
`|
Generic Answer
KnowledgeOS:
Question
|
Semantic Search
|
Relevant Company Knowledge
|
AI Reasoning
|
Verified Answer + Sources
Benefits:
- Reduced hallucination
- Company-specific intelligence
- Reliable answers
Search by meaning instead of keywords.
Example:
Query:
Why did our application crash yesterday?
KnowledgeOS discovers:
- Database timeout incident
- Production outage report
- Scaling documentation
KnowledgeOS understands relationships between:
Payment Service
|
PostgreSQL
|
Incident #442
|
Payment Team
The AI understands:
- System ownership
- Dependencies
- Related incidents
- Technologies
- Teams
KnowledgeOS contains specialized AI agents.
Creates:
- Documentation
- FAQs
- Tutorials
- Developer guides
Searches:
- Internal knowledge
- Code repositories
- Documentation
- Discussions
Analyzes:
- Error logs
- Incidents
- Monitoring alerts
Provides:
- Root cause analysis
- Similar incidents
- Recommended fixes
Processes:
- Meeting recordings
- Transcripts
- Notes
Generates:
- Summary
- Decisions
- Action items
- Owners
Organizations understand their knowledge health.
Metrics:
Total Documents
25,432
AI Questions Today
14,820
Active Users
3,540
Successful Answers
94%
Analytics include:
- Popular topics
- Knowledge gaps
- Outdated documentation
- AI effectiveness
Security features:
- JWT Authentication
- OAuth2 / SSO
- Role Based Access Control
- Organization isolation
- Audit logging
- API security
- Rate limiting
- Secure file storage
Designed for enterprise organizations.
Architecture:
Organization
|
|---- Users
|---- Teams
|---- Documents
|---- Knowledge Base
|---- Analytics
Supports:
- Multiple companies
- Data isolation
- Subscription plans
- React
- TypeScript
- Tailwind CSS
- Shadcn UI
- React Query
- Zustand
- Framer Motion
- Python
- Django REST Framework
- PostgreSQL
- Redis
- Celery
- JWT Authentication
- WebSockets
- LangChain
- LlamaIndex
- OpenAI / Claude
- HuggingFace Embeddings
- FAISS
- pgvector
- OpenSearch
- Docker
- Docker Compose
- AWS ECS
- AWS RDS PostgreSQL
- AWS S3
- AWS ECR
- GitHub Actions
- Terraform
- Nginx
Users
|
React Frontend
|
Django REST API
|
AI Orchestration Layer
|
RAG Pipeline
|
Vector Database
|
LLM
|
Response + Citations
KnowledgeOS includes an evaluated RAG pipeline built around PostgreSQL + pgvector, semantic retrieval, tenant-aware filtering, local embeddings, and local Qwen3 generation through Ollama.
- 90% correctness / pass rate
- 71.7% answer-point coverage
- 80% groundedness
- 72% source alignment
- 100% correct abstention
- 0% unsupported claims
The evaluation used a controlled 20-case dataset containing direct, paraphrased, lexical, multi-relevant, hard-negative, and unanswerable cases. Alternative retrieval strategies including lexical search, hybrid retrieval, RRF, and query-aware reranking were benchmarked before retaining semantic retrieval as the production baseline.
Performance: semantic retrieval ~5-15 ms; generation ~8.5-10.5 s; total RAG response ~9-13.6 s. Generation is the dominant latency contributor.
Deep dive: Retrieval & RAG Engineering | Interview Guide | Evidence Index
KnowledgeOS/
│
├── frontend/
├── backend/
├── ai-services/
├── infrastructure/
├── docs/
│
├── docker-compose.yml
├── README.md
├── .env.example
└── .gitignore
Install:
- Docker Desktop
- Git
- Node.js LTS
- Python 3.12
git clone https://github.com/anakhas771/KnowledgeOS.gitCreate environment file:
cp .env.example .env
Configure:
DATABASE_URL=
REDIS_URL=
SECRET_KEY=
OPENAI_API_KEY=
AWS_ACCESS_KEY=
AWS_SECRET_KEY=
Start all services:
docker compose up --build
Services:
Frontend:
http://localhost:3000
Backend API:
http://localhost:8000
Database:
PostgreSQL
Cache: Redis
Branch strategy:
main
|
develop
|
feature/*
Example:
feature/authentication
feature/document-processing
feature/rag-engine
feature/ai-agents Testing
Backend:
pytest
Frontend:
npm test
AI Evaluation:
RAGAS
CI/CD Pipeline
Every code change runs:
GitHub Push
|
Linting
|
Automated Tests
|
Security Scan
|
Docker Build
|
Deployment
Production:
CloudFront
|
Application Load Balancer
|
AWS ECS
|
Django API
|
RDS PostgreSQL
|
Redis
|
AI Services
Project documentation:
docs/
├── architecture
├── api
├── database
├── deployment
├── security
├── testing
├── ai
└── decisions
Project Foundation
Repository setup Docker environment Backend foundation Frontend foundation
Identity Platform
Authentication Organizations RBAC
Knowledge Platform
Document management Processing pipeline Vector storage
AI Intelligence
RAG engine AI assistant Agents
Enterprise Features
Analytics Integrations Collaboration
Production Deployment
AWS infrastructure Monitoring Scaling Engineering Principles
#KnowledgeOS follows:
Clean Architecture Domain Driven Design API First Development Test Driven Development Security First Design Cloud Native Practices Continuous Delivery Future Vision
KnowledgeOS aims to become:
The AI Operating System for Enterprise Knowledge
A platform where organizations can capture, understand, and utilize their collective intelligence through artificial intelligence.
Built as an enterprise-grade AI SaaS engineering project demonstrating:
MIT License