How To Add AI Without It Making Things Up
Start from a task with a baseline
The projects that stall are the ones where nobody agreed what a good result looked like before the build began. We start from a specific task with a measurable baseline, so you can tell whether the AI genuinely improved it or merely changed it. Evaluation is built before deployment, not bolted on after something goes wrong in production.
Ground answers in your own data
A model asked to recall facts from memory will occasionally invent them with total confidence. The fix is retrieval: answers are grounded in your own documents and data, with citations back to source, so staff can verify a response instead of trusting it blindly. An answer nobody can check against a source is not an answer you should be acting on.
Keep a human on the consequential calls
Not every output needs a person, but the high-consequence ones do. We design review and override steps into the workflow wherever a wrong answer would carry real cost, so a model mistake is caught rather than becoming a business incident. Treating AI output as automatically correct is the single most common and most expensive error we see.
Sometimes AI is the wrong tool
When a task has deterministic rules, when every answer must be auditable and reproducible, or when errors carry a cost human review cannot absorb, conventional logic is the better choice. A rules engine is often cheaper, faster and fully auditable, and we will tell you when that is the honest recommendation rather than fitting AI to a problem that does not need it.
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