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Retrieval-augmented generation with LangChain, LlamaIndex, or bespoke orchestrators grounded in your data. Chunking, embeddings, rerankers, and citation UX are tuned so answers stay faithful and auditable.
Comprehensive solutions tailored to your business requirements
Document parsing, intelligent chunking, and metadata extraction pipelines tuned per content type—PDFs, HTML, code, Slack, and more.
Hybrid search combining dense embeddings with sparse retrieval, plus cross-encoder rerankers for high-precision results.
LangChain, LlamaIndex, or custom orchestrators with tool boundaries, tracing, and multi-step retrieval for complex queries.
Citation UX surfaces, abstention policies for low-confidence answers, and hallucination monitoring dashboards.
Answers grounded in your actual data instead of model guesses
Auditable citations users can verify and trust
Always up-to-date as documents are re-indexed automatically
Reduced hallucination through retrieval-grounded generation
Works with any LLM backend—OpenAI, Gemini, LLaMA, or custom
Scales from thousands to millions of documents
RAG constrains the model to generate answers from retrieved documents rather than relying on parametric memory. Combined with abstention policies (the model says 'I don't know' when retrieval confidence is low) and citation requirements, hallucination rates drop significantly—typically by 60-80% on factual queries.
Yes. We deploy vector stores and LLMs within your infrastructure or VPC. Access controls are enforced at the document level so users only retrieve content they are authorized to see.
We implement incremental re-indexing pipelines triggered by document changes. Stale chunks are removed, new content is embedded, and the index stays current without full rebuilds.
We combine deep technical expertise with a product-first mindset to deliver solutions that work in the real world.
Seasoned engineers across blockchain, AI & web
200+ projects delivered globally
From discovery to production & beyond
