RAG implementation services, built by someone who ships production retrieval systems.
Most RAG quotes cover three components. Production needs nine. The gap is where pilots die: messy documents, no evaluation set, and access control that lets the retrieval layer see more than the user can.
I design and build retrieval systems that answer from your documents, cite the exact source, refuse when they should, and pass a security review.
0 hallucinated citations across 297 cases on the most recent published run, with a 221/297 aggregate retrieval hit rate against public suites. Methodology and raw artefacts are on the proof page, so you can reproduce it rather than take my word for it.
What RAG implementation services actually cover
Retrieval-augmented generation is not a product you install. It is an information retrieval pipeline with a language model at the end of it, and the model is the least interesting part. A production build makes a deliberate decision on nine components: ingestion and parsing, chunking strategy, embedding and index design, retrieval, reranking, grounded generation, evaluation, access control, and observability.
The three that decide whether the system survives contact with real users are evaluation, retrieval quality, and access control. They are also the three most often missing from a quote. If you are comparing proposals right now, the buyer’s checklist for choosing a RAG development partner breaks down what each line item should say.
What gets built
The components that separate a demo from a system people trust with their work:
Ingestion that survives real documents
Scanned pages, tables, footnotes and appendices - the parts that silently return nothing when ingestion was built for clean PDFs.
Hybrid retrieval and reranking
Vector search alone is worst at exactly the queries buyers care about: policy numbers, statute references, part codes. Hybrid retrieval plus reranking fixes it.
Mechanically verified citations
Every reference checked against the retrieved span before the answer returns. Prompting a model to cite is not citation verification.
An evaluation harness you own
A labelled question set built with your subject-matter experts, so every change is measured and no prompt tweak is a coin flip.
How the engagement runs
- Two-week discovery sprint. Sample the real corpus, not the clean one. Build a labelled evaluation set with your subject-matter experts. You leave with an architecture document, the evaluation set, and a delivery estimate - yours to keep, and to take to another builder if you prefer.
- Retrieval build. Ingestion, chunking, index design, hybrid retrieval and reranking, measured against your evaluation set from day one. Weekly demos show the hit rate moving, not slides describing progress.
- Generation and grounding. Answer synthesis, citation verification, and refusal behaviour when retrieval returns nothing good. A system that says “I have no source for that” is worth more than one that always answers.
- Hardening. Access control inside the retrieval query, audit logging, model failover, load behaviour, and cost controls. Unglamorous and non-optional.
Everything runs in your accounts - your cloud, your repository, your vector store - from day one. No handoff cliff at the end of the engagement.
RAG evaluation: how retrieval quality is actually measured
Every claim on this page rests on measurement, so it is worth being explicit about what RAG evaluation involves. Without it, every prompt change is a coin flip and nobody can prove the system improved.
An evaluation harness has three parts. First, a labelled question set built with your subject-matter experts - roughly a hundred questions with known correct source documents is enough to change every decision that follows. Second, retrieval metrics: hit rate (did the correct passage make the context at all), rank (where it landed, since a passage retrieved at rank 14 never reaches the model), and recall across multi-document questions. Third, generation metrics: citation validity checked mechanically against the retrieved span, and refusal behaviour when retrieval returns nothing adequate.
The number most vendors quote is accuracy on their own data, which is unfalsifiable. The number worth trusting is a reproducible run against public suites. On the most recent published run - PatronusAI FinanceBench, CUAD, openFDA and SEC EDGAR - the system returned 0 hallucinated citations across 297 cases with a 221/297 aggregate retrieval hit rate. 74% is a real hit rate on hard public questions, not a marketing figure, and the methodology plus raw artefacts are published on the proof page so you can rerun them.
You should own the evaluation set at the end of an engagement, whoever builds the system. It is the asset that lets you change vendors, models or chunking strategy later without guessing.
Who this is for
- Document-heavy operations in legal, insurance, fund administration and professional services, where every answer must cite its source.
- SaaS platforms adding retrieval features that have to work across tenants without leaking between them.
- Regulated teams in financial services, healthcare and life sciences where SOC 2, HIPAA or sector review shapes the architecture from the start.
- Rescue work - a pilot that impressed everyone and then stalled at the security review or on real documents.
Track record
SureCiteAI is a multi-tenant document intelligence platform I designed and built as sole architect: a twelve-component RAG pipeline running at 96.8% retrieval accuracy in production, with hybrid search, hierarchical retrieval, tenant isolation enforced at the database layer, and citation verification on every reference. It is live, and the code is open source.
An AI legal document analysis platform built for an investment fund law practice processes 150 to 200 page Limited Partnership Agreements, extracting six clause categories with risk analysis inside a Microsoft Word add-in, and eliminated over 70% of manual review time. Reviewers adopted it because every extracted clause points back to its exact location in the agreement.
Is RAG even the right purchase?
Sometimes it is not, and it is cheaper to hear that now. A small stable corpus may not need retrieval infrastructure at all. A house-style or rigid-schema problem is a fine-tuning question, not a retrieval one. And a task that runs multiple steps per case is automation with retrieval inside it - or, if the steps change case by case, an agent build. I will tell you which one you are looking at on the first call.
Frequently asked questions
What do RAG implementation services include?
Nine components: document ingestion and parsing, chunking strategy, embedding and index design, retrieval (hybrid vector plus keyword), reranking, grounded generation with mechanically verified citations, an evaluation harness, access control and tenant isolation, and observability. A quote that covers only ingestion, embeddings and a chat endpoint is a prototype quote. Evaluation, retrieval quality and access control are what decide whether the system survives real users.
How long does a production RAG implementation take?
Six to ten weeks for a focused single-corpus system: two weeks of discovery and evaluation-set construction, three to five weeks of retrieval and generation work measured against that evaluation set, and two to three weeks of hardening covering access control, audit logging, failover and load behaviour. Multi-tenant and multi-corpus systems extend the hardening phase rather than the build phase.
How do you prevent hallucinated citations in a RAG system?
By verifying citations mechanically rather than instructing the model to cite. Every reference is checked against the retrieved span before the answer is returned, and the system refuses to answer when retrieval surfaces nothing adequate. On the most recent published evaluation run against public suites including PatronusAI FinanceBench, CUAD, openFDA and SEC EDGAR, this approach returned zero hallucinated citations across 297 cases, with a 221/297 aggregate retrieval hit rate. The methodology and raw run artefacts are published so the result can be reproduced.
Can a RAG system meet SOC 2 and HIPAA requirements?
The system does not hold a certification, your organisation does, but the architecture either supports your controls or undermines them. For SOC 2 that means authorisation enforced inside the retrieval query rather than in the UI, audit logs covering every query and every document surfaced, and change management over prompts, indexes and model versions. For HIPAA it means keeping PHI out of embedding paths that leave your boundary, a business associate agreement with any model or vector provider in scope, and retention limits on query logs. These are designed in from week one, not retrofitted.
Do we need a vector database for RAG?
Not always. Postgres with pgvector is sufficient for a large share of corpora, and using infrastructure your team already operates is worth real money in reliability. Dedicated vector stores earn their place at scale, with heavy metadata filtering, or when managed hybrid search matters. The choice follows the corpus, never the other way round.
Start with your corpus
Tell me what your documents are, who needs to ask questions of them, and what happens today when someone needs an answer.
I will tell you whether retrieval is the right purchase, and what the first two weeks would produce.
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