Correctness
Is the answer right according to the rules that actually govern this market, product, or contract?
Service
GenAI & Agentic AI Evaluation
Know it works before your customers do.
LLM features fail differently from software. We build the data, the scoring, and the release gate that tell you whether your model is safe to ship — automated, repeatable, and running on every change.
The same input can give a different answer.
Fixed assertions don’t work
There’s no single correct string to compare against. Correctness becomes a judgement, and a judgement has to be measured, not assumed.
Confidently wrong is the dangerous case
A crash is visible. A well-phrased, wrong answer reaches the customer as a promise the business has to keep.
Spot checks don’t scale
Someone trying twenty questions before a release isn’t a gate — it’s a hunch that quietly degrades with every model update.
Five dimensions, scored on every answer.
Faithfulness
Does the answer stay within the retrieved context, or go beyond what the sources support?
Hallucination
Is anything asserted that no source supports? This is the metric that worries your legal team.
Answer relevancy
Does it answer the question asked, or a nearby one it would rather answer?
Tone
Does it sound like your brand under pressure — even when the customer is angry or the answer is no?
A gate, not a report.
Golden dataset
Built from your real rules and real tickets, versioned in your repository, and owned by your team.
Automated evaluation suite
Built on DeepEval, running in CI on every change to the prompt, the model, or retrieval.
Adversarial sets
Jailbreaks, prompt injection, and the edge cases your users will find in their first week.
A release verdict
A pass or fail you can gate deployment on — not a dashboard nobody opens.
Conversational AI at FedEx.
The real cost
A text and voice assistant answering shipping questions across markets where every country has its own rules. One confidently wrong answer reaches the customer as a promise the business has to keep.
What we built
- 1Golden datasets built on each country's actual shipping rules
- 2Every answer is measured against those rules before anyone sees it
- 3Adversarial suites guard the release — the model ships only if it holds up
What it achieved
- 1Every answer is checked against that country's rules before the customer sees it
- 2No release ships unless it holds up against the adversarial suites
- 3FedEx ships conversational AI to customers with confidence
Shipping an LLM feature you can’t fully vouch for?
Tell us what it does and who sees it — we’ll reply within a day.
Get in touchBefore you ask.
How do you test something non-deterministic?
You stop asserting and start scoring. Every answer is scored against your rules on several axes, and the gate is a threshold over a dataset — not a match against one expected string.
Who decides what the right answer is?
You do. We extract the rules from your real policies, contracts, and tickets, and your domain experts sign off on the dataset. We build the machinery; the definition of correct stays with you.
Does our data leave our environment?
Not necessarily. The suite runs inside your CI against the model endpoint you already trust. When we need a judge model, we use the provider you’ve already approved.
What if the results are bad?
Then you found out before your customers did — which is the whole point. The first baseline usually comes in lower than the team expected, and that number is exactly what makes improvement measurable.
We already have prompt tests in our repo.
A good starting point. The usual gaps: they aren’t versioned against a dataset, they don’t score hallucination or faithfulness, and they don’t gate deployment. We close all three.