LLM & generative AI applications

Most enterprise value from generative AI comes from combining a capable model with your own documents, records and systems. We build assistants and applications that answer questions, draft content, summarize and extract information from documents, and work inside the tools people already use.

Retrieval-augmented generation (RAG) is often at the core. We design the ingestion, chunking, indexing and access-control layers so the model only sees content the user is entitled to, and cites its sources. Where documents are complex, we combine OCR, layout parsing and structured extraction to turn contracts, forms and reports into usable data.

Quality is engineered, not assumed. We build evaluation sets from real examples, measure accuracy and groundedness on every change, and add guardrails for sensitive content, prompt injection and data leakage. We also design for cost and latency through model selection, caching and routing, so the solution stays affordable as usage grows.