RAG Architecture Development with Claude
Production retrieval-augmented generation built on Claude — chunking strategy, embedding choice, vector-store sizing, retrieval evals, hybrid search, and a hardening pass that survives real-world data.
The short version
RAG development services from NINtec deliver retrieval augmented generation services beyond the demo. Most Claude RAG projects work well in a notebook and crumble in production — not because of the model but because of the retrieval design. Our RAG architecture practice solves the hard parts: chunking strategy that respects document semantics, embedding model selection grounded in eval data, vector-store sizing that handles your real corpus, hybrid search where keyword retrieval rescues the cases dense retrieval misses, retrieval-quality evaluation as a CI signal, and citation discipline so users can trust what Claude says. We have shipped RAG implementation across legal document corpora, medical-imaging metadata, automotive parts catalogues, financial filings, customer-service knowledge bases, and engineering wikis. The deliverable is a production system with documented eval-bar, a corpus-update pipeline, and a maintenance posture for the long tail.
What's in scope
Chunking Strategy
Document-semantics-aware chunking — section-based, table-aware, code-block-aware. Chunk-size tuned to retrieval performance, not arbitrary token counts.
Embedding Selection + Eval
Embedding model selected on retrieval evals against your corpus, not on benchmark numbers. Cohere, OpenAI, Voyage, and open-source candidates compared in Discovery.
Vector Store Architecture
pgvector, Pinecone, Weaviate, Qdrant, or Chroma — chosen on corpus size, query rate, and operational posture. Hybrid setups when dense + keyword is required.
Hybrid + Re-Ranked Retrieval
Dense retrieval rescued by BM25 keyword retrieval, with cross-encoder re-ranking on the merged candidate set. Recall and precision tuned together.
Citation + Grounding Discipline
Every Claude response cites the chunks it used. Hallucination-detection layer flags when generated text is not grounded in retrieved context.
Corpus Update Pipeline
Automated re-indexing on document updates, deletion-aware vector store hygiene, and corpus versioning so changes can be rolled back.
How NINtec delivers
RAG engagements typically run 8–14 weeks. Discovery includes a corpus walk-through and retrieval-eval scoping; Build delivers chunking, indexing, retrieval, and Claude-side prompt engineering iteratively; Hardening tests against the long tail (rare queries, adversarial inputs, document-update churn).
Read the full AI Engineering MethodHow we compare
| Dimension | Generic agency | Big consulting | NINtec |
|---|---|---|---|
| Claude engineer certification | Ad-hoc, unverified | Generic AI training | 4 internal NINtec Claude Academy tracks |
| Production deployments | 1–3 pilots | Case studies, few production | 11 platforms · 15 countries · live |
| Engagement response | Days–weeks | Weeks via BD layers | Architect on call in 48 hours |
| Listed-company posture | Private | Private partnership | NSE & BSE Main Board (NINSYS) |
| Regulated-industry coverage | Rare | Enterprise-grade | SOC 2 · ISO 27001 · HIPAA · GDPR · PCI DSS |
Where this lands first
300+
Claude-trained engineers
11
Platform products on Claude
6
Delivery phases — Claude in every one
48 hrs
Architect response time
How an engagement runs
RAG Discovery
1–2 weeks
Corpus walk-through, query taxonomy, eval-set construction (golden questions + expected citations), and a retrieval-quality target negotiated up-front.
Build + Eval Cycles
6–10 weeks
Iterative build with weekly retrieval-quality readouts. Embedding selection, chunking strategy, and re-ranking tuned against the eval set. Claude-side grounding prompts integrated by week 4.
Hardening + Launch
1–2 weeks
Long-tail adversarial testing, document-update churn drills, and graduated launch with feature flags. Operational handover with a documented retrieval-eval CI.
Ready to talk to a Claude architect?
48-hour response from a senior architect. No BD-layer delay. The Readiness Assessment scopes the work and proposes named engineers.
RAG Architecture Development with Claude — FAQ
Do I need RAG, or can I just put the documents in Claude's context window?
If your corpus fits in context (Claude's 200K-token window), a context-stuffing approach can work — and we have shipped clients on context-stuffing with prompt caching. RAG becomes necessary when the corpus exceeds context, when documents update frequently, or when retrieval needs to scope to user/tenant. Discovery makes this call with data, not opinion.
How long does RAG development take?
Single-corpus RAG systems ship in 8–10 weeks. Multi-corpus RAG with tenancy and re-ranking takes 12–14 weeks. The eval set built in Discovery is the throttle on speed — better evals make later iterations cheaper.
Which vector database should we use?
Depends on corpus size, query rate, and operational posture. pgvector for smaller corpora and Postgres-shop clients; Pinecone for hosted simplicity; Weaviate for hybrid search ease; Qdrant for performance at scale; Chroma for pre-prod and prototyping. We are not religious; the Discovery phase recommends one based on your constraints.
What chunking strategy works best?
There is no universal best — it depends on document structure. Legal contracts chunk by clause; medical guidelines chunk by recommendation; engineering docs chunk by section. We start with a structure-aware chunker and tune chunk size against retrieval evals. Token-count-only chunkers are a starting point we always move past.
How do you measure retrieval quality?
Golden-set retrieval evals — for each query, the expected chunks (citations) are pre-marked, and we score recall@k and precision@k. We also track end-to-end answer quality with a judge-LLM scoring rubric. Both metrics block CI for prompt or index changes.
Can RAG combine with tool use and agentic workflows?
Yes — and frequently does. A common pattern is a router agent that picks RAG, tool calls, or both per query. RAG retrieves grounding context; tools fetch live data; Claude synthesises the answer. See /agentic-ai-development for the orchestration side.
How do you keep the index in sync with the source documents?
Event-driven re-indexing on document create/update/delete. The indexing pipeline tracks document versions, vector-store rows, and chunk-level diffs. Stale-vector cleanup runs on a configurable schedule. We have shipped this pattern at scale and have the playbook.
What about hallucinations?
Defence in depth — citation-strict prompting (Claude must cite or refuse), grounding checks that compare answer claims to retrieved chunks, hallucination-detection layer that flags ungrounded outputs, and judge-LLM scoring as continuous monitoring. We do not eliminate hallucinations; we make them visible and rare.
Adjacent engagements
Claude API Integration Services
Targeting: claude api integration
Agentic AI Development Services
Targeting: agentic ai development
MCP Server Development Services
Targeting: mcp server development
Claude Development Services
Targeting: claude development services
Claude AI Engineering Practice
Flagship — 300+ engineers, 11 platform products, 4 academy tracks
Talk to a Claude architect
Senior architect on the call in 48 hours. Walk away with a written assessment whether or not you engage.
Talk to a Claude Architect