Production reality

Your RAG system worked in demos.Here is why it breaks in production.

Most enterprise RAG failures are retrieval failures in disguise.Whether you are building RAG pipelines, agentic AI workflows, or generative AI applications on your own data, the failure mode is usually the same: the retrieval layer is not production-grade.

See what the audit covers
The scale of the problem

Adoption is accelerating. Production outcomes are not keeping pace.

The gap sits almost entirely at the retrieval layer.

71%

of organisations regularly use generative AI in at least one business function

McKinsey & Company, State of AI
95%

of enterprise generative AI pilots fail to deliver measurable business or P&L impact

MIT / NANDA, State of AI in Business 2025

Adoption is not the problem. Production is.

Most generative AI systems fail when they move beyond demos because the model is not grounded in reliable enterprise context. Without strong retrieval, systems produce fluent answers that cannot be trusted.

Agentic systems amplify the risk. When an agent starts from incorrect context, every step in the chain compounds the error.

The organisations seeing real outcomes share one trait: they built the retrieval layer properly before building applications on top of it. The fix is not a better model. It is a properly designed retrieval architecture.

The diagnosis

The same five failure modes. Every time.

These are not edge cases. They are the standard pattern of enterprise RAG in production.

Scale

Precision collapse at volume

Semantic similarity is not relevance. At ten thousand documents the difference is invisible. At ten million it becomes the gap between a working product and one that users quietly stop trusting.
Fusion

Hybrid search done badly

Vector search misses exact matches. Lexical search misses semantic intent. Most systems layer one on top of the other without proper fusion and inherit the failure modes of both. Results feel inconsistent even when engineers cannot find an obvious bug.
Freshness

Stale context in live systems

Index freshness is an afterthought until it becomes a crisis. A document updated this morning is invisible to a query this afternoon. In regulated industries this is a compliance liability. In agentic workflows it means agents acting on outdated context without knowing it.
Access control

Permissions enforced too late

Access control checked after retrieval, not during, means sensitive documents enter the context window before they are filtered out. In agentic chains this is not a theoretical risk. It is a breach waiting for the right query.
Visibility

No way to see why it is failing

Without ranking explainability, debugging retrieval quality is guesswork. Teams tune prompts. They swap models. The problem does not move because the problem was never in the model. It was in what the model was given to reason over. In agentic workflows this is especially dangerous — an agent that cannot explain its retrieval decisions cannot be audited, corrected, or trusted. Confident wrong answers compound across every step of the chain.
Root cause

The model can only reason over what retrieval gives it.

Large language models can only reason over the context they are given. In a production RAG system, that context is entirely determined by retrieval.

Every hallucination, every confident wrong answer, and every plausible-sounding fabrication starts the same way: retrieval returned the wrong documents, or the right documents in the wrong order.

Improving the model does not fix a retrieval problem. Improving the prompt does not fix a retrieval problem. The only fix for a retrieval architecture problem is a better retrieval architecture.

In agentic systems this compounds. One retrieval failure in step one of a five-step chain produces five steps of wrong reasoning. The agent is not broken. The foundation it is operating on is.

The implication

Teams that diagnose retrieval failures as model failures spend months on the wrong problem.

Agentic and generative AI systems built on weak retrieval do not fail obviously. They fail confidently, at scale, and in ways teams often misdiagnose for too long.

The symptoms look like model problems. The cause is upstream.

The fix

Retrieval architecture is an engineering discipline. Treat it like one.

Hybrid retrieval done right

Lexical and vector search fused at the ranking stage, not layered sequentially. First-phase retrieval for efficiency, second-phase learned ranking for relevance. Results that are both semantically precise and lexically exact.

Freshness and access control by design

Index updates that propagate in seconds. Access control enforced inside the retrieval layer at query time, not as a post-processing filter. Documents that should not be seen are never retrieved, not just never shown.

Explainable ranking

Every result traceable to the signals that produced it. Relevance debugging that takes minutes, not weeks. A foundation for systematic quality measurement rather than anecdotal user feedback.
Right buyer

For teams who have already hit the wall — or can see it coming.

You are in the right place if

  • Your RAG pilot performed well in testing and is degrading in production
  • Your AI agents produce confident wrong answers and you cannot isolate why
  • You are scaling your document corpus and retrieval quality is not scaling with it
  • You are planning an agentic AI programme and want the retrieval layer validated before building on top of it
  • Your engineering team is spending more time debugging retrieval than building product
  • You are building agentic AI workflows or generative AI applications and want the retrieval foundation validated before the agent layer is built on top of it.

What we do about it

We audit the retrieval architecture, identify the specific failure modes in your system, and tell you exactly what to fix and in what order.

Some teams need a retrieval redesign. Some need a platform migration. Some need targeted improvements to hybrid search and ranking. The audit tells you which — and the answer is not always the most expensive one.
Search Stack Audit

Find out exactly what is wrong with your retrieval layer.

The Search Stack Audit is a structured diagnostic engagement. We review your architecture, identify root causes, and deliver a written report with a clear recommended path. Fixed fee. Technology-agnostic. The answer might not be Vespa. It might not require Searchplex at all. The audit is designed to tell you the truth.

See what the audit covers