AI reliability, from the data up
Your LLM isn't hallucinating. Your data is.
AI assistants rarely fail because of the model. They fail because of the data layer underneath — stale retrieval, no way to measure quality, nothing logged. The demo looks great. Production is a liability. We work on that layer, for fintech teams who have already shipped.
Does any of this sound familiar?
It answers differently every time
Same question, different answer. Teams blame the model and tweak the prompt. It's almost always retrieval pulling different context on each run.
Nobody can tell a fix from a regression
You change a prompt, a model, a chunking strategy. Did quality go up or down? Without a golden set, it's a coin flip you can't see.
It fails with everything green
No exception, no alert, dashboards healthy — and the answer is confidently wrong. The most dangerous failure is the one that looks like success.
It answers a question it was never meant to
Someone types "ignore your instructions" and the assistant reaches for whatever its credential can reach. The rule lived in the prompt, and a prompt can be argued with.
What this looks like in practice
A support assistant at a payments company spent eight months quoting a fee schedule that no longer existed.
The model was fine. The prompt was fine. The rates had been revised in March; the index had been built the previous July and never rebuilt. Every one of those ~2,000 conversations a month was answered from a snapshot of a company that had moved on.
Nobody caught it — not for lack of attention, but because there was nothing to catch it with. No golden set, so a prompt change was a guess. No logged retrieval, so a wrong answer gave no clue whether the model ignored good context or received bad context.
They found out when a customer posted the screenshot.
Their users were the test suite.
Nothing in that story is exotic. It is the default outcome of shipping a retrieval system without the data layer that keeps it honest — and in financial services, the wrong answer is not an embarrassment, it is exposure.
The Reliability Check
A short conversation with an agent that maps your AI system across the four layers where it actually breaks — then tells you which one is your real problem.
- 01
Measurement
Can they tell whether it works?
- 02
Retrieval & grounding
Is the model seeing the right data?
- 03
Observability
Can they see it in production?
- 04
Governance & blast radius
When it goes wrong, how far does it reach — and who answers?