When your AI gives a wrong answer, everyone blames the model. Half the time it's retrieval sending the wrong context and the model is just doing its best.
RAG Rescue
Your RAG system worked beautifully until success broke it.
It always feels magical at first. Then volume rises, the document graph gets messy, and the model starts confidently pulling the wrong things for the right questions. I help teams build evals around the failures, diagnose what actually collapsed, and rebuild retrieval for relevance, precision, and scale.
Best fit
RAG Rescue Consulting
- Retrieval architecture review with failure analysis
- Layered search strategy spanning lexical, vector, and structural retrieval
- Reranking and query-routing recommendations tied to cost and latency
- Evaluation plan for relevance, grounding, and answer consistency
25–50K
Docs where many systems start wobbling
3 layers
Hybrid retrieval strategy I implemented
Context aware
graphRAG activation by query intent
Why teams call
The pattern I keep seeing.
As document counts rise, chunking, metadata, embeddings, and reranking choices interact in ways that create semantic confusion at exactly the worst moments.
Teams can waste months tuning prompts when the real fix is retrieval evals, architecture, and query routing.
What changes
What actually gets better.
Identify the specific causes of context collapse, retrieval drift, and semantic confusion in your current stack.
Rework indexing, metadata, reranking, and query planning so the model sees the right evidence more often.
Route only the right queries into heavier graph-based retrieval so quality rises without exploding cost.
Make relevance measurable with evals, debug traces, and failure-class tracking instead of vibes.
Case Study
A company’s knowledgebase integration was wildly successful until it fell over.
At roughly 25,000 to 50,000 indexed documents, they started seeing context collapse, semantic confusion, and degraded answer quality. I rebuilt retrieval around a highly optimized three-layer hybrid search design, with graphRAG only kicking in when the query context justified the extra depth.
The fix was not one more prompt tweak. It was retrieval architecture.
How I work
No mystery, no handoff decks.
01
Debug the failure classes
We look at missed retrieval, wrong retrieval, stale retrieval, over-broad retrieval, and answer hallucination as separate problems with separate remedies.
02
Rebuild retrieval in layers
I combine lexical precision, vector recall, and graph-aware traversal so each query gets the cheapest path that still returns the right evidence.
03
Instrument the system
You leave with clearer evals and retrieval diagnostics so the next scale jump becomes manageable instead of mysterious.
Next step
Ready to stop circling it?
Bring whatever your team keeps putting off — the scary migration, the expensive AI bill, the app that misbehaves in production. We'll figure out what's actually blocking it.
