RAG development services for systems that cite their sources.

RAG pipeline development sorts cleanly by corpus shape, not by industry. A contracts-Q&A pipeline for a law firm reuses the same hierarchical chunking, hybrid retrieval, and citation-enforcement prompts as an enterprise rag deployment over policy documents at a health-tech company. The integrations, freshness rules, and residency posture change — the retrieval pipeline shape doesn't. Sorting by corpus shape lets us reuse the eval harness and reranker tuning across clients. Sorting by industry, the way most competing pages organize themselves, hides where the engineering actually lives.

Heavier columns: legal, fintech, healthcare. The pattern is unsurprising — those are the industries where citations, refusal, and provenance pay back the hardest. Lighter columns: e-commerce and ed-tech, where workloads tilt toward generation and recommendation more than retrieval. The grid isn't a roadmap; it's a record. We'll talk you out of a RAG-shaped engagement if the workload is actually a generation problem (better fit for our generative AI practice) or a tool-use problem (better fit for autonomous tool-use agents).

One more pattern worth naming: about 25% of the engagements that start as RAG end up being rag consulting work instead. The client comes in wanting a built pipeline; the corpus audit reveals that the source data isn't yet in a shape any pipeline can win against. The right next step is a six-week consulting engagement to get the source data structured — header conventions, OCR cleanup, deduplication, freshness pipeline — before any retrieval code ships. We do that work, sometimes hand it to your team, sometimes we run it ourselves. Either way, we say "not yet" on the build until the corpus is ready, because shipping a RAG against a broken corpus is how you end up calling Rescue six months later.

Choosing a RAG development services partner is mostly about choosing the right starting shape. Buyers who walk in with a scoped corpus and one question type ship to production around 75% of the time. Buyers who walk in with "we want a RAG over everything we have" ship around 30% of the time, usually after a re-scope. The four shapes below map onto the starting points we see most often — pick the one that matches what you actually have, not what you wish you had. Every shape is a RAG development service we've shipped 10+ times; the deliverables and gate criteria are locked in by repetition, not invented for your engagement. Each starts by scoping the llm knowledge base boundary — what the system can and can't answer — before any ingest code ships.

A practical decision tree: if the corpus is scoped but the retrieval quality is unproven, start with a Pilot. If you know the corpus works and you need production discipline (CDC, observability, eval gates), start with a Production RAG Build. If you've already shipped RAG and it hallucinates, retrieves wrong, or can't keep up, start with Rescue. If the only thing wrong with the current system is the vector store underneath it, start with Migration. Each of these is a real RAG development service we run on a fixed scope, and our rag consulting practice will tell you to start narrow — moving up is cheap; over-scoping isn't. Week-by-week scope on each, further down →

Two patterns worth flagging on every RAG engagement. First, we benchmark two embedders against the eval set before locking the stack — usually OpenAI text-embedding-3-large against a domain-specific Voyage model. The eval set decides, not the leaderboard. Second, we default to including a reranker on every Production Build. The 150–200ms latency tax is almost always worth the 8–18 point precision lift. Skip it only when latency budget rules it out — voice-RAG, sub-700ms response targets. Our deeper take on chunking — including when fixed-size beats semantic — lives in our chunking strategy deep dive.

Rule of thumb: if the answer needs to cite its source or stay fresh, you want RAG. If the answer needs to speak in your voice or your domain's jargon, you want fine-tuning. Anything in between, the decision tree below walks four diagnostic questions — most projects fit cleanly into one of five outcomes.

Pattern choice matters more than store choice or embedder choice for production retrieval recall. We've watched teams agonize over Pinecone vs Qdrant while running pattern 01 (naive RAG) on a corpus that needed pattern 02 (hybrid + rerank). The store decision was worth 1–3 points of recall; the pattern decision was worth 14. When you build rag system architecture, start with the pattern question — "what shape of retrieval does this corpus need?" — then pick the store and embedder that fit. Reverse that order and you'll rebuild at week 9, which is the call we get on most Rescue engagements. The LLM knowledge base scope — what the system answers vs refuses — belongs in week 2.

A common scoping mistake on enterprise rag projects: clients ask for pattern 03 (multi-step) when pattern 02 + a better embedder would have shipped in half the time. Each retrieval loop doubles latency cost and the eval surface widens. Default to pattern 02 (hybrid + rerank). Move up only when the eval set tells you to. About a third of "we need multi-step RAG" requests we audit end up landing back on pattern 02 once the hybrid search implementation is properly tuned — the deeper take on that call lives in our hybrid retrieval breakdown.

Numbers shown are illustrative target ranges for new engagements until production eval data from anonymised builds is published.

Two methodology notes that matter. We use LLM-as-judge with Claude Sonnet 4.6 as the default faithfulness scorer because it produces the most consistent grades against human ground-truth on the eval sets we've shipped. Hallucination disputes (typically 5–8% of outputs) get human spot-check by your domain expert — we never let LLM-as-judge stand alone for the hard cases. And the eval set grows during production: traces sampled monthly, regression alarms firing when an upstream model swap drops scores. An llm knowledge base scored weekly degrades at a fraction of the rate of one eyeballed quarterly.

Default eval and observability stack we deploy:

All three concerns share a pattern: the discipline is in the spec, not in the build. We name the threat model, the compliance posture, and the cost band during corpus audit. The build executes against those targets — security and cost aren't add-on phases that happen after retrieval recall is green. They're how it gets there.

Use cases below are organised by corpus shape — contracts, tickets, research notes, code repos, regulations, equipment manuals — not by industry. The same hybrid retrieval and citation-enforcement pipeline ships to a law firm and a manufacturer; what changes is the chunking strategy, the freshness pipeline, and the embedder. Below: three flagship engagements (full numbers) plus three function stubs from recent ships. A semantic search implementation is the throughline on most of them; document intelligence services patterns recur across legal and ops.

Patterns across all six engagements: the eval set landed in week 2, before retrieval code; the eval set grew during production via sampled traces; citation enforcement was the headline guardrail, not an add-on. The outcome numbers are what each team measured at 90 days post-launch, not at deploy. The rag solutions that hold up at 90 days are the ones where the eval set was graded by a domain expert before the first chunk was embedded — picking a partner that stays for that work is the most underrated criterion in vendor selection.

Two patterns worth flagging on RAG engagements specifically. The eval set is the deliverable — even more than the pipeline. A pipeline you can rebuild; an eval set is institutional knowledge about what your business considers a correct answer. We hand it over in your repo, with grading criteria documented. About 70% of Pilots convert to Build engagements. The 30% that don't either re-scoped based on what the Pilot revealed or decided the workflow wasn't yet ready for retrieval. Both are legitimate outcomes; we'd rather flag it at week 3 than at week 12.