RAG systems that retrieve the right passage, then prove where it came from
We build retrieval augmented generation over your contracts, tickets, wikis, lab data and CRM records. Hybrid search, reranking, access control and a citation on every answer, validated against ground-truth benchmarks before a single user sees it.
Almost every failed RAG project failed at retrieval, not at the model
Teams swap the model, raise the temperature, rewrite the prompt and wonder why the answers are still wrong. The model was never the bottleneck. If the correct passage is not in the context window, no amount of prompt engineering will invent it.
We treat retrieval as the engineering problem it is. That means measuring recall before generation quality, testing chunking strategies against your actual documents rather than a blog post, and holding the whole pipeline to a benchmark you can rerun every time something changes.
The result is a system your legal, clinical or support team will trust, because every answer carries a source they can open and check.
Why these projects fail
These five failures account for most of the RAG systems we are asked to rescue.
The build, step by step
Every stage produces something you can inspect. You see retrieval quality numbers long before you see a chat interface.
Corpus audit and ground truth
We inventory your document types, formats and update frequency, then build a labelled question set with your subject experts. That set becomes the benchmark every later decision is measured against.
Parsing and chunking strategy
Layout-aware parsing for PDFs, tables and scanned documents, then a chunking strategy tested against the benchmark rather than assumed. Metadata such as author, date, jurisdiction and department is preserved for filtering.
Hybrid retrieval
BM25 keyword search combined with dense vector search, so exact identifiers and semantic matches both land. Metadata filters narrow the candidate set before scoring.
Cross-encoder reranking
A reranker rescores the candidate passages against the actual query, which is where most of the precision gain comes from. We tune the cut-off against your latency budget.
Citation grounding and refusal
Answers are constrained to the retrieved context, with inline sources. When the corpus does not contain the answer, the system says so instead of inventing one.
Access control and audit trail
Role-based filtering applied at retrieval time, not in the prompt, plus a logged record of every query, the passages returned and the answer generated.
RAGAS evaluation and launch
Faithfulness, answer relevance, context precision and context recall measured against the benchmark. We publish the numbers, deploy, and set up monitoring so drift is visible.
What a production RAG pipeline looks like
The two stages outlined in blue are where most of the accuracy gain lives, and they are the two most often missing from a system we are asked to rescue.
What lands in your repository
A working system with the parts that make it maintainable after we leave.
Tools we build this on
Chosen per project against your data, latency budget and compliance requirements, not out of habit.
Retrieval and Orchestration
Vector and Graph Stores
Models
Evaluation and Monitoring
RAG systems we have put into production
Numbers from live production systems, not projections.
Multi-hop knowledge search at 8,000 queries a day
A single-vector setup had already failed. Multi-agent reasoning over a Neo4j knowledge graph with RAG Fusion lifted retrieval precision 61% and cut knowledge-gap tickets 37%.
Healthcare · USAHIPAA-compliant clinical decision support at 91% factual accuracy
Patient records, lab data and medical literature unified into one queryable layer on GCP Vertex AI, with role-based access across every clinical role and a full audit trail.
RegTech · Germany and EURegulatory intelligence across eight jurisdictions
Agentic RAG over regulatory feeds and internal policy, classifying compliance gaps at 84% accuracy and cutting manual ESG audit effort per cycle by 52%.
RAG questions we answer on most first calls
What clients ask before starting a project like this.
How long does a production RAG system take?
A focused build over one document set usually takes 2 to 4 weeks including evaluation and deployment. Systems spanning several sources, strict access control or regulated environments run longer, typically 6 to 10 weeks.
What accuracy can we expect?
It depends on your corpus quality and question types. Our best production systems run at 91% on legal citation accuracy and clinical factual accuracy. We measure your baseline in the first week and commit to a target you can verify, rather than quoting a number before seeing the data.
Can the system run entirely inside our own infrastructure?
Yes. We deploy open-weight models with self-hosted vector stores inside your VPC when data residency requires it. One private deployment keeps 100% of data inside the client environment with no external API calls.
Do we need a vector database?
Not always. If your corpus is small and mostly keyword-driven, pgvector on the Postgres instance you already run is often enough. We recommend a dedicated vector database when scale, filtering complexity or hybrid search performance justifies it.
How do you stop it from hallucinating?
Three layers. Retrieval is tuned so the correct passage is present, generation is constrained to the retrieved context with inline citations, and the system is instructed and evaluated to refuse when the corpus does not cover the question. After DPO alignment on one legal deployment, hallucinated citations went to zero.
Have documents your team keeps searching by hand?
Send us the document types and a handful of real questions your team asks. We will tell you on the call whether RAG is the right answer and what accuracy is realistic.