Data & Predictive Analytics
RAG for Specific Knowledge
Simple Explanation
AI answers with your documents and data as a base, doesn't make things up.
Technical Explanation
ETL pipelines, vectors + re-ranking, LightRAG-style retrieval, quality evaluations, and privacy per space.
What You Get
- ETL pipelines for document ingestion
- Vector database with semantic search
- Re-ranking for improved relevance
- Quality evaluations and testing
- Privacy controls per workspace/tenant
- Citation tracking and source attribution
Use Cases
- Internal knowledge base Q&A
- Customer support documentation
- Legal/compliance document search
- Research and analysis tools
Technology Stack
Vectors: Pinecone, Weaviate, or pgvector
Embeddings: OpenAI, Cohere, or open-source
Retrieval: LightRAG-style hybrid search
LLMs: GPT-4, Claude, or self-hosted
Evaluation: RAGAS, custom metrics
Implementation Roadmap
Phase 1: Data Prep (1-2 weeks)
- Audit document sources
- Design chunking strategy
- Set up ETL pipeline
Phase 2: Build RAG (2-3 weeks)
- Implement vector search
- Add re-ranking
- Test retrieval quality
Phase 3: Deploy (1-2 weeks)
- Production deployment
- User interface
- Monitor and optimize
90% Answer Accuracy
<2s Response Time
100% Source Attribution
Pricing
PoC: €8,000 - €15,000
Production: €20,000 - €60,000
Monthly: €1,000 - €5,000 (hosting + usage)
Ready to Get Started?
Let's discuss how this solution can work for your business.