Custom RAG development
We build retrieval-augmented systems that answer questions from your contracts, manuals, case files and knowledge bases, citing the exact passage behind every answer. Users only see content they are permitted to see, and retrieval quality is measured on every release.
What it is and when it fits.
Retrieval-augmented generation (RAG) finds the most relevant passages in your own content and gives them to a language model to write the answer. Most of the quality comes from the parts users never see: document parsing, chunking, hybrid retrieval, reranking and permission filters. We treat those as engineering problems with test sets, and we measure retrieval and answer quality separately.
RAG is the right choice when answers must come from specific, changing documents and users need to verify the source. It is less suited to tasks that depend on a consistent style or format across millions of outputs, where fine-tuning may help, or to questions that are really database queries. We often combine RAG with structured queries for exactly that reason.
What we build.
Document Q&A chatbots
Chat interfaces that answer from policies, contracts, manuals or case files, with clickable citations to the source passage.
Ingestion pipelines
Parsing for PDFs, scans, tables and spreadsheets, with metadata extraction and scheduled re-indexing when sources change.
Hybrid retrieval and reranking
Vector and keyword search combined, followed by reranking, tuned on questions your users actually ask.
Permission-aware access
Document-level and field-level access rules applied at query time, matching your identity provider and source systems.
Retrieval and answer evals
Test sets that score recall, citation accuracy and faithfulness, run automatically before each deployment.
How it works.
- 01
Collect sources and questions
We gather representative documents and a set of real questions with expected answers from the people who will use the system.
- 02
Get retrieval right
We build parsing, chunking and search, and measure whether the correct passages are found before a model writes any answers.
- 03
Build the answer layer
We add generation, citations, permission checks and the user interface, and score faithfulness against the same eval set.
- 04
Monitor in use
Feedback, unanswered questions and low-confidence answers are tracked, and the test set grows with every issue found.
Related work.
Built with.
All technologiesFurther reading.
How to build a production RAG chatbot: a practical guide
What it takes to turn a promising RAG experiment into a chatbot people trust: ingestion, hybrid search, citations, permissions and evals.
5 min read
How to evaluate RAG: retrieval metrics, faithfulness and golden sets
How to measure a RAG system properly: separate retrieval from answers, check faithfulness claim by claim and build a golden set you can trust.
4 min read
RAG vs fine-tuning: which does your business actually need?
RAG gives a model your knowledge at answer time; fine-tuning shapes its behaviour. How to choose, when to combine them and what each costs.
4 min read
Common questions.
The variety and quality of your documents, the number of source systems, how complex the permission model is and how accurate answers need to be. Scanned documents and complex tables take the most work. We scope it after reviewing a sample of your content.
By making retrieval reliable, instructing the model to answer only from retrieved passages, requiring citations and checking faithfulness with evals. When no good source is found, the system says it does not know.
Yes. The vector store, documents and logs can run in EU regions, and we can use EU-hosted models from Mistral, Azure OpenAI or AWS Bedrock, or self-host open models on your own infrastructure.
Often pgvector inside Postgres, because it keeps vectors next to your data and permissions. For very large or heavily filtered collections we use Qdrant or Elasticsearch. The choice follows your volume and existing stack.