RAG Development
AI that answers from your documents
OmniLabs builds RAG systems that ground AI answers in your own documents and data, with sources your team can check.
Discuss your RAG projectWhy generic AI isn't enough
- 01
Generic AI guesses about your contracts, policies, and products.
- 02
Your knowledge is buried across PDFs, wikis, drives, and databases.
- 03
Answers come without sources, so no one can trust them.
- 04
AI tools ignore who is allowed to see what.
Custom RAG, from your files to production
Grounded answers with citations, built on your data.
- RAG strategy and feasibility
- Document ingestion and indexing
- Hybrid search and reranking
- RAG chatbots and knowledge assistants
- Accuracy evaluation and testing
- Monitoring and index updates
RAG development services
From a single knowledge assistant to enterprise-wide RAG.
RAG strategy and consulting
We check your data and use case, then plan the right RAG approach.
What you get:
- Use case and data assessment
- Feasibility check
- Architecture plan
- Roadmap
Data ingestion and indexing
Your documents turned into a searchable, always-current knowledge base.
What you get:
- PDFs, docs, tables, and wikis
- Chunking and embeddings
- Vector database setup
- Automatic sync from sources
Retrieval optimization
The right passages found for every question.
What you get:
- Semantic and hybrid search
- Reranking
- Query rewriting
- Context assembly
RAG chatbots and assistants
Assistants that answer from your knowledge base, with sources.
What you get:
- Cited answers
- Role-based access
- Real-time data
- Handoff to a person
Evaluation and testing
Accuracy you can measure, not guess.
What you get:
- Groundedness scoring
- Test question sets
- Hallucination checks
- Regression testing
Monitoring and maintenance
RAG that stays accurate as your content changes.
What you get:
- Index freshness
- Query analytics
- Access reviews
- Cost and latency tuning
What RAG changes
Answers come from your documents, with sources anyone can check.
People only see answers from content they're allowed to access.
Accuracy is measured and tracked, not assumed.
How we build RAG systems
Assess feasibility
We review your use case and content, and plan the approach.
Prepare your content
We ingest, chunk, and index your documents into a retrieval-ready knowledge base.
Build and evaluate
We build RAG over your real data and test accuracy against real questions.
Deploy and monitor
We launch to production and monitor quality, freshness, and cost.
RAG questions
What's the difference between RAG and fine-tuning?
RAG retrieves answers from your current documents at question time. Fine-tuning changes the model itself. For most business knowledge, RAG is faster to update and easier to verify.
What data sources can RAG use?
Documents, PDFs, wikis, databases, APIs, knowledge bases, and cloud storage.
How do you prevent hallucinations?
Answers are grounded in retrieved sources with citations. When the sources don't cover a question, the system says so and can route it to a person.
Can RAG respect user permissions?
Yes. Retrieval follows your access rules, so people only get answers from content they can see.
How do you measure accuracy?
With test question sets, groundedness scoring, and regression tests run before every release.
Can RAG work with real-time data?
Yes. We sync sources automatically and can query live systems at question time.
Do we need to clean up our documents first?
No. We need access to the content. We handle parsing, cleanup, and indexing.
Ready to put your documents to work?
Tell us what your team needs answers to, and we'll show you how we'd build it.
