AI Evaluation & Assurance

The Challenge

Most teams test their AI by looking at a handful of outputs and trusting their gut or LLM as a judge. Both are terrible. With just these tools, you don't really know how good your AI performance is. Your customers might notice the issues before you do. Without real evaluations, you can't answer the most important question about your AI system: is it actually working?

Our Approach

We build evaluations the way good engineering teams build tests. First we define what "good" means for your specific use case — the accuracy, safety, and cost metrics that matter to your business. Then we build a graded test set from your real data, automate the scoring, and wire it into your workflow so every change is measured. Where a problem can be checked deterministically, we use code; where it needs judgment, we use carefully designed LLM-as-judge scoring. The result is a repeatable, trustworthy measure of quality.

What You Get

• A custom evaluation suite built from your real data and use cases
• Clear, agreed-upon metrics for accuracy, reliability, safety, and cost
• Automated eval runs so every model or prompt change is scored, not guessed
• Safety and red-team checks: hallucination, data leakage, harmful or off-policy outputs
• Regression tracking and monitoring to catch quality drops after launch
• A plain-English report leadership, customers, or auditors can actually trust

Who This Is For

Teams that have shipped (or are about to ship) AI features and need to know they work — product teams under pressure to launch responsibly, leaders who need proof for customers or regulators, and companies burned by an AI system that worked in the demo and failed in the wild.