Document search & Q&A
Ask questions in plain language across your knowledge base and get answers with citations to the source passage. Each response links to the exact document, so your team verifies it in seconds.
All services
RAG development services that answer from your data
Our RAG development services turn your documents, wikis and databases into a system that answers questions and cites its sources. We placed #2 of 350 teams in the Legal RAG Challenge, and we build retrieval you can trust in production.
What we build
Intelligent document processing
AI document processing reads PDFs, scans and contracts, then pulls out the fields, clauses and figures you need. We handle messy layouts, tables and multi-page files that break simple parsers.
Domain & legal RAG
Retrieval tuned for high-stakes work like law, finance and compliance, where a wrong answer costs real money.
RAG as a service
Managed retrieval pipelines we run and improve for you: ingestion, indexing, evaluation and monitoring. You get a working system and ongoing accuracy tuning without staffing an ML team.
How we work
0101/04
Corpus assessment
We review your documents, formats and volumes, then map the questions users actually ask. That shows us what retrieval quality is achievable and where the gaps are.
0202/04
Retrieval architecture & prototype
We design chunking, embeddings, indexing and reranking, then ship a working prototype on your real data. You see answers on your own content early, not on a demo dataset.
0303/04
Evaluation
We measure retrieval accuracy, citation coverage and hallucination rate against a test set built from your use cases. Nothing goes live until the numbers hold up.
0404/04
Deploy & maintain
We deploy into your stack with monitoring and guardrails, then keep tuning as your documents and questions change. We maintain accuracy rather than assume it.
Legal RAG that placed #2 of 350
In the Legal RAG Challenge we built a retrieval system for legal research that answered complex questions with accurate citations and ranked #2 out of 350 teams.
RAG development FAQ
What is RAG development?
RAG stands for retrieval-augmented generation. RAG development means building a system that first retrieves the most relevant passages from your own data, then uses a language model to answer with that context. Answers stay grounded in your documents instead of the model's general training.
RAG or fine-tuning?
Fine-tuning changes how a model writes and reasons, but it does not give the model your current documents. RAG connects the model to your live knowledge base, so answers stay up to date and cite their sources. For most document and search use cases, RAG is faster, cheaper and easier to keep accurate.
How do you prevent hallucinations and ensure accuracy?
We ground each answer in retrieved passages and return citations, so any claim traces back to a source. We measure retrieval accuracy, citation coverage and hallucination rate on a test set from your use cases, and we add guardrails so the system says "not found" instead of guessing.
What data and formats do you support?
We work with PDFs, Word and text documents, scans, spreadsheets, web pages, wikis and structured databases. Intelligent document processing handles tables, forms and multi-page layouts, and we connect to your existing storage and internal systems.
What does RAG as a service mean?
RAG as a service means we build, host and operate the full retrieval pipeline for you, then keep improving its accuracy over time. You get a production system, monitoring and ongoing tuning without hiring and managing an in-house ML team.
Get in touch
Tell us about your task — we will suggest what AI pilot we can build and what it takes to start.
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