See Truesight in Action

Four workflows that transform how you evaluate AI.

Step 1

Review & Label

Review your AI's outputs

Review your AI outputs one by one and document what went wrong. No predefined categories, just honest observations about failures. This builds the intuition you need before you can measure anything.

Find common error patterns

Group your observations into error categories and see which problems occur most often. Prioritize what to fix first based on real data.

Step 2

Build & Deploy

Build your evaluation

Turn your error analysis into a deployable evaluation. Use labeled data from your review or upload your own dataset. Define what you're judging, add more labels if needed, and deploy. Expert judgment captured and ready to scale.

Use your evaluation anywhere

Truesight fits right into developer workflows with an API you can call from anywhere. A single request returns structured judgments to measure your AI quality, catch regressions from prompt or model changes, monitor production for drift, and block errors before users see them. Your expert judgment, at scale.

The methodology behind these workflows

Before building Truesight, we spent years as consultants building AI products in industries where reliability wasn't optional. We built over 20 production systems, and we learned something that changed how we think about evaluation: dashboards full of green metrics mean nothing when yours users are frustrated with how your AI works.

Every evaluation tool we tried pushed us toward generic metrics because they're easy to implement, not because they work. We watched teams waste months chasing accuracy scores while their actual product quality suffered. The metrics looked good. The users weren't happy.

The workflows you see above are our answer to that problem. Domain experts define what "good" looks like for their specific use case. Truesight captures that judgment and applies it at scale. No more generic benchmarks that miss what actually matters for your AI product.

This is the methodology that leading practitioners in LLM evaluation now teach and advocate. Experts across the industry have converged on a clear consensus: the teams shipping reliable AI products are the ones grounding their evaluations in domain expertise, not generic benchmarks. Truesight makes that methodology accessible to any team, not just those with six-figure consulting budgets.

This is how enterprise teams ship AI products they can stand behind. Now you can too.

Ready to try it yourself?

Join AI teams building reliable products with expert-grounded evaluations.

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