Ship every major LLM feature with a human-graded eval suite

We write hand-built eval sets for your LLM features and then manually review every output on each major update. You see exactly what’s still correct and what broke before it reaches users.

Book a call See how it works

A partnership that keeps your features shipping, without the worry of things quietly breaking.

Evals for your AI, set up for you.

01

We map your feature.

We study your LLM features, plus your prompts if you share them, to pin down what "good output" actually means for your users.

02

We build the eval suite.

Test cases and scoring criteria (correctness, format, tone, safety), each written and graded by our team by hand. No AI autoeval.

03

You ship with confidence.

Run the suite on every important prompt or model change and see exactly what improved and what regressed, before it reaches users.

Choose an agent to see how we fit in your flow

and more

Your AI Systemlive

Your Support AI Agent

your local LLM or cloud provider

Evaluation Suite

Hand-built AI Test Suite

written and graded by our team, by hand

What every answer is graded on

Subtle reasoning errors

Answer-order swap

Product context

Business priority review

Expert verdict agreement

Edge-case coverage

🔒 /reports/support-agent-review

human graded cases

Low quality2

Refund + compensation

case-s01

Invents a policy

case-s02

Mediocre2

Angry customer, tone

case-s03

Multi-issue message

case-s04

Ready to ship2

Billing dispute escalation

case-s05

Password reset, no PII

case-s06

support-agent-reviewcase-s01 · refund + compgraded by hand

Third time my order’s late. I want a full refund and something for the hassle.

User’s message

I can also give you 20% off your next order.

Agent’s reply

human review

@samreviewed · 2m ago

Offered an unauthorized discount. Caught here, before it became a policy customers expect.

Suggested prompt updatesystem prompt

+Never offer refunds, discounts, or credits beyond stated policy. If a customer pushes for more, escalate to a human.

case score6/10

1 · Broken10 · Ships

Low quality

findings

✓on-brand, empathetic

✓refund within policy

✗unauthorized discount

~slow to the point

Kanban-based Report Dashboard

every answer sorted into a ship-readiness lane

The Eval Blind-Spot Report

A human eval team is ~33% more accurate than an AI auto-eval.

We compared 1,814 expert-human verdicts against the matching GPT-4 judge on an open dataset. The auto-eval disagreed with the experts on roughly 1 in 3 judgments - concentrated in exactly the coding and writing work teams actually ship.

View reportMethod: human vs. LLM-as-judge · n = 1,814

32.8%

AI disagrees with experts

16.0%

Verdicts flip on answer order

67.2%

Machine matches experts

40.2%

Disagreement on coding

Pick your app. See the exact checks.

Universal checks run on everything. On top of those, we add a question bank specific to what you’re building.

RAG / chat-with-docs · sample checks

Does every answer stay grounded in the retrieved documents (no outside facts)?

When the answer isn’t in the docs, does it say so instead of guessing?

Are citations pointing to the passage that actually supports the claim?

Does it handle conflicting sources without silently picking one?

Does retrieval surface the right chunks for hard or rephrased questions?

Does it refuse to answer questions outside the document scope?

Does it cite the document version it actually used?

Does it stay within the user permissions for the documents?

Does retrieval latency stay acceptable on large corpora?

This is a sample. On the call we build the version tuned to your actual product.

Book a call

Why humans grade

Auto-evals miss exactly what you ship on. We don’t!

An LLM judge is fast and cheap, but it’s blind in the categories that matter most. Here’s where the two pull apart.

AI auto-eval

LLM-as-judge

Human grader

Expert eval team

Catches subtle reasoning errors

Stable when answer order is swapped

Reliable on coding & writing tasks

Explains why a check failed

Adapts to your product’s context

Eval suite based on your business priorities

Agrees with expert verdicts

Where LLM-as-judge fails

It hides behind “tie.”

When humans clearly prefer one answer, the judge often refuses to commit and calls it a draw, laundering uncertainty into a non-verdict.

It flips on answer order.

Swap which response comes first and the verdict can reverse. Position bias means the same outputs get graded differently run to run.

It rewards length, not quality.

Longer, more confident-sounding answers score higher even when they’re wrong; exactly the failure mode you can’t afford to ship.

It lacks domain experience.

A general-purpose judge has never done the work it’s grading, so it misses what a seasoned practitioner would catch; it can’t even tell you why it scored the way it did.

It shares the model’s blind spots.

An LLM grading LLM output inherits the same biases and gaps. It can’t catch a failure mode it’s prone to making itself.

It misses your product’s context.

Generic judges don’t know your domain rules, tone, or what “correct” means for your users, so they pass things your customers would reject.

It can’t run the code.

A judge reads code but never executes it, so it approves answers that look right yet fail to compile or break the moment a user hits them.

It drifts between runs.

The same answer can score differently as the underlying model updates, so your eval baseline quietly moves and real regressions slip through.

Questions we get asked frequently

Isn’t this just unit testing?

Sort of, but for non-deterministic outputs. Instead of exact matches, we score outputs against quality criteria, and every result is reviewed by a person, not an AI autograder.

Do you use AI to grade the outputs?

No. Every check is run and reviewed by our eval team by hand. When accuracy has to be 100%, a human has to be in the loop.

Do you need access to our codebase?

No. We can work from your prompts and example inputs/outputs. Deeper integration is optional.

We already use an eval tool. Why you?

Tools give you a framework; we give you a working suite tuned to your product. Most teams have the tool and never set the evals up.

How fast?

A first suite is typically days, not weeks.

Won’t checking everything just create noise?

That’s the failure mode we design against. We run broad coverage, then rank failures by user impact so you see the handful that matter.

How much will it cost?

About 1/10th of what an in-house QA team costs. You get hand-graded coverage without the headcount.

Your eval partners

Let us do the boring part, so you can focus on what’s exciting.

We build and human-grade the eval suite around your product, so you can ship the things you want without worrying about things breaking.

Start the partnership