RAG Systems & Knowledge Intelligence
Connect AI to your data. Accurate, grounded, citation-backed answers from your own knowledge base.
45 min → 30 seconds
Retrieval-Augmented Generation is the architectural pattern that makes enterprise AI trustworthy. Without RAG, language models hallucinate. With RAG, every response is grounded in your actual data.
We build production-grade RAG systems handling real-world complexity: thousand-table database schemas, hybrid search combining keyword precision with semantic understanding, and multi-source retrieval across documents, databases, and APIs simultaneously.
For structured data, we build Text-to-SQL pipelines translating natural language into precise database queries. For unstructured data, we build semantic search engines understanding meaning, not just keywords. Every answer comes with traceable sources.
What we deliver
- Custom RAG architecture tailored to your data landscape
- Vector database deployment and optimisation: embedding strategy, chunking, indexing
- Hybrid search: dense semantic retrieval + sparse keyword search + learned re-ranking
- Text-to-SQL pipelines across complex schemas using semantic cataloguing
- Multi-source RAG: unified retrieval across documents, databases, APIs, wikis
- Advanced retrieval: query decomposition, HyDE, contextual retrieval, agentic RAG
- Citation and provenance: traceable references to source documents and records
Use cases
45 min → 30 seconds
Technical Documentation Q&A for 50,000+ Documents
Field engineers ask natural language questions across 50,000+ technical documents. Handles PDFs with diagrams, cross-references related docs, respects classification levels.
2-day turnaround → seconds
Text-to-SQL Over 1,200-Table Data Warehouse
Business users query a 1,200-table warehouse in natural language. Semantic schema catalogue identifies relevant tables per query before generating SQL.
70% reduction in research time
Case Law Research & Precedent Discovery
RAG system across 200,000+ case documents, statutes, and regulatory texts. Retrieves relevant precedents by factual pattern, ranks by jurisdiction.