RAG Pipeline Audit
We audit all six layers of your RAG pipeline, rank what's causing the quality failure, and tell you exactly what to fix. 5-day audit. Written report. Quantified.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Your RAG system is retrieving. It’s not retrieving the right things.
Every company that built an internal knowledge base, document Q&A system, or AI support agent used RAG. Most of them are underperforming. The team tuned chunk size, changed overlap, swapped the top-k parameter. The system still gives wrong answers. The root cause is almost always in one of six places — and it’s rarely chunk size.
Common complaints that signal this buyer: “our retrieval isn’t finding the right chunks,” “it’s hallucinating even when the answer is in the docs,” “we changed the chunk size and it got worse,” “re-ranking didn’t help.”
What We Audit
| Layer | What We Assess |
|---|---|
| Chunking strategy | Chunk size, overlap, splitting method (fixed, semantic, structural). Are chunks preserving meaning or splitting across logical units? |
| Embedding model | Is the embedding model appropriate for the domain and query type? Retrieval accuracy test vs. alternatives. |
| Retrieval pipeline | Vector search configuration, similarity metric, top-k tuning, hybrid search (vector + keyword). Are the right chunks being retrieved? |
| Re-ranking | Is a re-ranker in place? Is it calibrated to the domain? Does it improve or degrade precision? |
| Context assembly | How are retrieved chunks assembled into the prompt? Is there deduplication? Is the context window being used efficiently? |
| Generation and validation | Is the final answer validated against retrieved context? Is there a hallucination detection step? |
We construct a golden dataset from your own failing queries and test retrieval precision at each layer. Every finding is quantified — not an impression.
Common Failure Patterns
| Pattern | Symptom | Root Cause | Fix |
|---|---|---|---|
| Semantic split | Splits a sentence across chunks | Fixed-size chunking ignores structure | Semantic chunking |
| Wrong embedding model | Generic queries retrieve better than domain queries | Model not trained on domain vocabulary | Domain-specific or fine-tuned model |
| Top-k too low | Correct answer in corpus but not retrieved | k=3 misses relevant chunk at position 4 | Increase k, add re-ranking |
| Re-ranker miscalibrated | Re-ranker moves correct chunk lower | Cross-encoder not fine-tuned for domain | Fine-tune or swap re-ranker |
| Context window stuffed | LLM sees too much context, loses the answer | No deduplication or relevance threshold | Context window optimization, dedup |
| No output validation | LLM hallucinates despite correct retrieval | No grounding check on final output | Hallucination detection gate |
What you leave with
Written audit report:
- Root cause assessment: which layer is causing the failure
- Ranked remediation table: fix, projected quality improvement, effort
- Quick wins implementable in <1 week
- Sprint-worthy items requiring AW implementation
Best Fit
- Production RAG system is not meeting quality expectations
- Users complain the system gives wrong answers
- Engineering team tuned chunk size, overlap, and top-k, then ran out of ideas
- Leadership is asking why the system is not as good as the demo
For teams looking for a RAG pipeline audit, the work centers on concrete RAG quality problems and retrieval accuracy improvement.
Not a Fit
- There is no failing query sample to test
- The system is still a concept, not a working RAG pipeline
- The only ask is vector database selection before the team has mapped retrieval, re-ranking, context assembly, and validation
How We Engage
| Engagement | What You Get |
|---|---|
| Tier 1 — RAG Pipeline Audit: $3,000-$6,000 | 5 business days. Fixed fee. Written report + findings call. |
| Tier 2 — RAG Fix Sprint: $10,000-$25,000 | Requires audit first. Implements top-ranked items. Includes evaluation harness with golden dataset for ongoing quality measurement. |
| Tier 3 — RAG Quality Retainer: $3,000-$6,000/month | Monthly quality assessment pass on evolving corpora. Drift detection and monthly report. |
Related
Also see: Production AI Audit — for broader system-level forensic review.
Deployments in this area
Codebase Analysis Agent: 30 Seconds to First Answer
Language-aware chunking with Tree-sitter, FAISS vector retrieval, and LLM reasoning. 30 seconds from upload to first contextual answer on any codebase.
Competitor Intelligence Agent: 8 Hours to 5 Minutes
Multi-agent system with parallel execution. Automated competitive analysis across pricing, features, and positioning with structured Pydantic-validated output.
Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives
How we built a real-time anomaly detection pipeline processing 2.4M events/day using Kafka, Isolation Forest, and foundation models. False positive rate reduced from 68% to under 20%.
Related articles
The RAG Pipeline Audit: How We Diagnose Retrieval Quality Problems in 5 Days
A structured 5-day RAG pipeline audit methodology: architecture review, retrieval testing, ingestion analysis, hallucination mapping, and a priority remediation matrix.
RAGVector Database Selection for Enterprise RAG: Pinecone, Weaviate, Qdrant, and the Operational Reality
A practical comparison of Pinecone, Weaviate, Qdrant, pgvector, Milvus, and Chroma across the dimensions that matter in production: filtering, multi-tenancy, cost, and migration paths.
RAGChunk Strategy Failures in Production RAG: When Your Chunking Works in Dev and Breaks in Production
Why RAG chunking that passes dev tests collapses in production: document diversity, table handling, size failures, overlap traps, and how to build quality metrics.
Discuss your RAG Pipeline Audit path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.