How to trust an AI score

Asking a language model to score a lead, a sales call or a contract takes an afternoon. Getting a sales team to trust that score, and act on it, takes engineering. Without it, the first strange result ends the project: someone sees a hot lead marked cold, and nobody looks at the scores again.

We recently built an AI-assisted sales platform that brings the CRM, call transcripts, documents, spreadsheets, chat and email into one place, and scores what matters. These are the pieces that made the scores worth reading.

1. Structured outputs, not prose

The model returns a fixed structure: a score, the reasons, and the evidence it used, each in its own field. Prose is for people. Software needs fields it can validate, store and compare.

2. A deterministic fallback

Models time out, return something malformed, or are simply unavailable. When that happens, a plain rule-based score takes over and is labelled as such. The team never sees a blank, and never mistakes a fallback for the model’s judgment.

3. Human review where it counts

Scores that drive an action (a deal moved, a lead dropped) go through a person first. Their corrections are recorded, which shows over time where the model is right and where it is not.

4. An audit trail

Every score keeps the inputs it saw and the output it produced. When someone asks “why is this lead a 3?”, the answer is a click away, not a guess.

5. Model and prompt provenance

Each score records which model and which version of the instructions produced it. When either changes, you can tell old scores from new ones, and compare them.

6. Regression tests

A fixed set of real examples, with scores people agreed on, runs before any change to the model or the instructions goes live. If the new version disagrees on cases that used to be right, the change waits.