Build AI systems that know your data. We develop production RAG pipelines that retrieve relevant information from your documents, databases, and knowledge bases, delivering accurate, cited answers instead of hallucinated guesses.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
End-to-end retrieval-augmented generation pipelines — document ingestion, chunking strategies, embedding generation, vector storage, retrieval, re-ranking, and LLM generation with citation extraction.
Combine semantic search (vector similarity) with keyword search (BM25) for superior retrieval accuracy. Metadata filtering, faceted search, and query understanding for complex information needs.
Transform unstructured documents into searchable knowledge bases. PDF parsing, table extraction, image OCR, document hierarchy preservation, and incremental indexing for growing data.
Systematic evaluation with RAGAS, custom metrics, and human-in-the-loop feedback. Measure retrieval precision, answer faithfulness, and relevance — then optimize chunk size, embedding models, and prompts.
Multi-tenant RAG systems with document-level permissions, user role filtering, and audit logging. Employees only see answers from documents they are authorized to access.
RAG systems that go beyond simple retrieval — query decomposition, multi-step reasoning, tool-use for structured data, and self-correction when initial retrieval is insufficient.
We build production RAG, not demo RAG. Hybrid search, re-ranking with Cohere/cross-encoders, query expansion, and chunk optimization that achieves 85-95% accuracy on real enterprise data.
Every RAG system ships with evaluation pipelines. We measure retrieval precision, answer faithfulness, and relevance — then iterate based on data, not vibes.
RAG across Confluence, SharePoint, Google Drive, Slack, databases, and APIs. Unified search across all your knowledge sources with proper access controls.
Index refresh pipelines, embedding drift monitoring, query analytics, and cost optimization. RAG systems that stay accurate as your data grows and changes.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
End-to-end retrieval-augmented generation pipelines — document ingestion, chunking strategies, embedding generation, vector storage, retrieval, re-ranking, and LLM generation with citation extraction.
Combine semantic search (vector similarity) with keyword search (BM25) for superior retrieval accuracy. Metadata filtering, faceted search, and query understanding for complex information needs.
Transform unstructured documents into searchable knowledge bases. PDF parsing, table extraction, image OCR, document hierarchy preservation, and incremental indexing for growing data.
Systematic evaluation with RAGAS, custom metrics, and human-in-the-loop feedback. Measure retrieval precision, answer faithfulness, and relevance — then optimize chunk size, embedding models, and prompts.
FAQ
Tell us about your knowledge sources and accuracy requirements — we'll design a RAG architecture that delivers reliable, cited answers from your data.