RAG consulting: retrieval that makes answers right.
Most RAG failures are retrieval failures. We fix chunking, embeddings, hybrid search and reranking so your assistant finds the right context, and we measure it so you know it's working.
Sound familiar?
- Answers sound confident but cite the wrong document, or none at all.
- It worked in the demo and fails on the questions real users ask.
- Changing the chunk size or embedding model is guesswork.
- When an answer is wrong, you can't tell whether retrieval or generation caused it.
- Latency and cost keep growing as you stuff more context into the prompt.
How we fix it.
Retrieval diagnosis
Separate retrieval errors from generation errors so effort goes where the failures actually are.
Indexing & chunking
Document parsing, chunking and metadata designed around how your users ask questions.
Embeddings
Model selection, domain fine-tuning and evaluation of embedding models on your own data.
Hybrid search & reranking
Keyword plus vector retrieval, fusion and cross-encoder reranking for the precision answers need.
Query rewriting
Rewriting, expansion and decomposition for vague, multi-part and conversational questions.
RAG evaluation
Retrieval recall, context precision, faithfulness and answer quality, with LLM judges calibrated against human labels.
Deliverables your team keeps.
Common questions.
Should we fine-tune a model instead of using RAG?
Often you need both, for different reasons. RAG gives a model fresh knowledge it can cite. Fine-tuning shapes format, tone, domain language and behaviour. We help you decide with evaluations rather than opinions.
Which vector database should we use?
The choice matters less than the retrieval design around it. We usually work with what you already run and change it only if it's the bottleneck.
Do you work on agents?
Yes. Agentic workflows depend on the same retrieval quality, so we evaluate and improve the search and tool-use steps inside agents too.
How do you measure whether RAG got better?
We build a test set from real user questions and score retrieval and generation separately, so every change shows exactly where it helped or hurt.
Often combined with.
Search relevance & ranking
Fix the queries that return the wrong results: query understanding, hybrid retrieval and learning to rank, measured end to end.
Learn moreLLM pre-training & fine-tuning
Make an open model fluent in your domain with DAPT, SFT and DPO, from data curation to a model you can ship.
Learn moreAI & search evaluation
Know whether a change helped: offline metrics, LLM-as-judge pipelines and A/B tests your team can trust.
Learn moreHave a search or LLM problem worth solving?
Tell us what you're building. You'll talk directly with the founder, not a sales team. If we're not the right fit, we'll say so and point you to someone who is.