Manage your AI like staff. Measured, every conversation.
Give each employee an objective and a handful of weighted KPIs. An AI judge scores every single conversation against them and rolls the result into a scorecard you read like a performance review — so “is the AI doing a good job?” finally has a number.
From objective
to a number you can defend.
Four steps turn an AI from a black box into an accountable team member — no manual QA, no analytics pipeline to build.
Set an objective
Tell the employee what it's for, in plain language — book more jobs, qualify better leads, deflect repetitive questions.
Weight the KPIs
Pick the handful that move the objective and weight them, so priority is explicit rather than implied.
An AI judge scores
An LLM evaluator reads every conversation and grades it against your rubric — the whole week, not a 2% sample.
Read the scorecard
KPIs roll up into a per-employee scorecard you read like a performance review — and the gaps it surfaces tell you what to fix.
A score that can't
be cheated.
A measurement system you can game is worthless. So in NeoMind, the shortcuts an AI might take to look good are precisely what the rubric penalises — they score negative and pull the number down.
- Hallucinating — making up an answer instead of grounding it in your knowledge.
- Binding promises — committing to something the business hasn't authorised.
- Going off-scope — wandering outside its remit to seem more helpful than it should.
Escalating to a human at the right moment does the opposite — it raises the score. The bright line between routine work and human judgement isn't a gap in the product; it's the thing that makes a measurable AI employee safe to deploy. Anything binding stays with your team.
The full framework
Our flagship guide walks through objectives, weighted KPIs, the AI judge and un-gameable scoring in depth.
About measurement
An AI judge — an LLM acting as evaluator — reads each conversation and grades it against your weighted KPI rubric automatically. Every interaction is scored, not a small manual sample, and the scores roll up into a per-employee scorecard.
No. Guardrail violations — hallucinating, making a binding promise, going off-scope — score negative, so an employee can't inflate its number by cutting corners. The measurement is designed to be un-gameable.
No. You set an objective in plain language, pick a handful of weighted KPIs, and the AI judge does the scoring. There's no manual QA queue, dashboards to build, or analytics pipeline to maintain.
A good KPI is tied to the employee's objective and observable in a conversation — for example lead-capture rate, answer accuracy or groundedness, appropriate escalation, and resolution. Pick the handful that matter and weight them so priorities are explicit.
Hire an AI employee you can actually measure.
Set an objective, weight the KPIs, read the scorecard. See our plans.