Accuracy & factuality
Verify correctness, sourcing, and factual grounding of generated answers.
Evaluate
AI Output Evaluation.
Rigorous human assessment of your model's responses — accuracy, safety, helpfulness, and alignment with your standards.
Overview
Know exactly where your model holds up.
AI Output Evaluation puts trained human reviewers behind your model's responses, scoring each one against your rubric so you know precisely where quality is strong and where it breaks down. We deliver structured, defensible judgments — not crowd-sourced guesses — with second-level QA on every batch.
What's included
Built for defensible quality.
Safety & policy
Flag harmful, unsafe, or policy-violating outputs against your guidelines.
Helpfulness & completeness
Assess whether responses actually solve the user's request.
Tone & alignment
Check voice, style, and alignment with brand and client standards.
Comparative scoring
Rank and compare model versions or candidates side by side.
Edge-case probing
Stress-test responses on hard, ambiguous, and adversarial cases.
How it works
Managed delivery, powered by Intellego.
01 — Scope & rubric
We define the task, the rubric, and the quality bar with your team before any work begins.
02 — Vetted reviewers
Domain-matched, trained reviewers are assigned and calibrated through the Intellego engine.
03 — Managed workflow + QA
Work runs under managed workflows with second-level QA and client-specific rubrics on every batch.
04 — Reporting & delivery
You receive QA-checked output with measurable accuracy reporting — not just raw labels.
Start with a controlled pilot
See it on your data.
Run a scoped pilot, measure the quality, then scale once it's proven.