RAG DEVELOPMENT
RAG that answers correctly — and proves it.
From document chunking strategy to reranking to citation-grounded outputs, we build RAG systems engineered to the quality bar your business actually needs — with the eval harness to prove it.
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Enterprise-grade delivery. Human-verified outcomes.
Built for teams that can't afford to guess.
From document chunking strategy to reranking to citation-grounded outputs, we build RAG systems engineered to the quality bar your business actually needs — with the eval harness to prove it.
Most RAG systems hallucinate. Often.
The default RAG tutorial works on a demo dataset. Build that same pipeline against your real corpus — messy PDFs, tables, scanned documents, multi-language content — and answer quality drops below the bar your business needs.
Chunks split a sentence in half. The embedding model doesn't understand your domain vocabulary. Top-5 retrieval misses the document with the answer. The LLM confabulates a citation that doesn't exist. And no one has built a golden dataset to even measure the regression. Production RAG is not a vibe — it's a measured system.
The eight-stage RAG pipeline we deploy.
The difference between a 60%-accurate RAG and a 90%-accurate RAG is rarely the LLM — it's how each stage is tuned.
Document parsing
Layout-aware parsers (Unstructured, Docling, Textract) handle real-world PDFs, tables, and scans.
Chunking
Structure-aware, semantic chunking — not naive fixed-size splitting.
Embedding
Benchmarked on your data: text-embedding-3, Cohere, BGE-M 3, Voyage, etc.
Vector store
pgvector, Pinecone, Weaviate, Qdrant, Milvus — chosen for your scale and stack.
Hybrid retrieval
Dense + BM 25 sparse retrieval, combined via reciprocal rank fusion.
Reranking
Cross-encoder reranker (Cohere Rerank, BGE, Col BERT) lifts precision@3 by 15–30 points.
Citation-grounded generation
Strict prompt + structured output: answer only from context, cite the source, refuse if unsupported.
Guardrails + eval
Citation verification, factuality scoring, continuous eval against a golden dataset.
What grounded retrieval actually looks like.
Hybrid retrieval, a reranker, and a citation-strict prompt — the same shape we ship to production.
Every claim is grounded in a retrieved source and the citation is verified against the document before the answer is returned.
- ✓Answers come with citations
- ✓You can trace every claim back to the source
- ✓No guesswork — refuse when unsupported
- ✓Just verifiable, reliable answers
Answer
Our refund policy allows returns within 30 days of purchase with a valid receipt.
Sources
- 1. Refund_Policy.pdf (Page 3)
- 2. Terms_Conditions.docx (Section 4.2)
We measure quality before we ship it.
We measure quality before we ship it.
The difference between a demo and a production RAG system is a golden dataset and an automated eval that runs on every change.
We build that harness first — so prompt tweaks, model swaps, and chunking changes are measured against real questions, and a regression blocks the deploy instead of reaching your users.
Eval harness
Continuous quality on every change
Golden dataset + automated eval — regressions block deploy, not your users.
Six failure modes that kill RAG quality.
Chunks split a sentence or table
Fix: Layout-aware parsing; recursive splitting; tables as structured chunks
Right document isn't in top-k
Fix: Hybrid (dense + BM 25) + RRF; query rewriting; higher initial-k + reranking
Reranker isn't there
Fix: Always deploy a reranker for production; measure precision@3 with vs. without
LLM hallucinates beyond context
Fix: Strict 'answer only from context' prompt; structured output; per-claim citations
Citations point to nothing
Fix: Citation verification — every cited quote checked against the source
System slowly degrades
Fix: Golden dataset + automated eval on every change; regression blocks deploy
How RAG projects engage with us.
Corpus audit + embedding benchmark
Vector DB recommendation + TCO
Golden-dataset starter
Parsing → chunking → embedding → retrieval
Hybrid + reranking + citation grounding
Full eval harness + observability + 30-day support
Weekly eval review + dataset expansion
Embedding-model migrations
Chunking iterations on real queries
Related reading
Pair retrieval with a model you own end to end.
Bring us your corpus.
A 30-minute call: document mix, query patterns, quality bar, latency target. We'll tell you honestly whether RAG is the right pattern and what kind of build it would take.
Chunks split a sentence or table
Fix: Layout-aware parsing; recursive splitting; tables as structured chunks
Right document isn't in top-k
Fix: Hybrid (dense + BM 25) + RRF; query rewriting; higher initial-k + reranking
Reranker isn't there
Fix: Always deploy a reranker for production; measure precision@3 with vs. without
LLM hallucinates beyond context
Fix: Strict 'answer only from context' prompt; structured output; per-claim citations
System slowly degrades
Fix: Golden dataset + automated eval on every change; regression blocks deploy
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.
Frequently asked questions
When is RAG the wrong answer?+
When the answers aren't in your documents. RAG retrieves and grounds; it doesn't reason about content that isn't in the source corpus. We pair it with agents or frontier models when needed.
How big should my chunks be?+
No universal answer. 256–512 tokens with 50-token overlap is a reasonable start; production chunking is layout-aware and semantic. We test 3–4 strategies and pick the one that maximizes precision@3.
Which vector database?+
pgvector if you're on Postgres; Pinecone for zero ops; Qdrant or Weaviate for self-hosted hybrid; Milvus or Turbopuffer at billion-vector scale.
Do we need a reranker?+
For production, yes almost always. Reranking is the single highest-impact lever after chunking.
Hybrid vs. pure-vector search?+
Hybrid wins on real-world corpora. Pure dense misses exact-term queries (codes, identifiers, names). We default to hybrid.
What about hallucinated citations?+
Structured output with per-claim citations, then a verification step confirming each cited quote actually appears in the cited source.