Gnosys Labs
Gnosys autonomously improves your prompts and classifiers when ground truth is too sparse for conventional optimization. Starting from a handful of expert-reviewed examples, it builds a calibrated objective, searches for better systems, and validates every improvement before deployment.
The insight
In production AI systems, you usually don't. Ground truth is sparse, experts disagree, and the failures you care about are rare.
Before you can optimize the model, you have to engineer the objective. That is what Gnosys does, automatically.
How it works
Gnosys is an autonomous model engineer. Give it your existing model and your reviewed production runs, and it goes to work like a teammate.
Your classifier today, plus ~50 reviewed production runs.
Investigates where your model fails.
Forms hypotheses about why.
Writes improved prompts and classifiers.
Tests them and keeps the winners, validated against human labels and calibrated so a small reviewed set speaks for the whole.
Runs an online experiment on the winner against your live system.
Measures the real business impact, the same trustworthy way.
The same classifier, catching the cases it was missing, with its impact proven in production.
The result is a better model, not just a better evaluation.
Where we fit
Today's tooling splits three ways, and every part of it assumes the objective is already solved.
Evaluate & track
Measure a system and log the experiment. They don't improve anything, and the number is only as good as the labels behind it.
LangSmith · Braintrust · Humanloop
Search & optimize
Automate the search for better prompts. Strong machinery, but it needs an objective to search against. Gnosys Labs builds on this layer.
DSPy · GEPA
Autonomous engineering
Iterate toward a target you already have. They assume the metric exists and is trustworthy.
Devin · Weco
Gnosys
When ground truth is sparse, the objective isn't solved. Gnosys engineers it, then improves the model. Its job is to make the model better, not just measure it.
Outcomes
Gains where they count
Improvement on the metric you actually deploy against, at the operating point you choose, not a headline average that hides what matters.
Less human labeling
A valid answer from a fraction of the labels, so your review budget goes where it moves the model.
Decisions that don't degrade
Performance that holds across the subsegments you care about, not just in aggregate.
Case studies
On a public safety benchmark under realistic label scarcity (~200 verified labels, only ~8 harmful), Gnosys beat the industry-standard GEPA optimizer on the metric safety teams actually deploy against: harm caught at a fixed false positive budget.
0.777
0.731
0.702
Headline run, 3,000 held-out messages the system never saw. Higher is more harm caught at the same false positive cost. Gnosys beat both in a second run too, 0.909 against 0.788 and 0.848.
Why us
We built and owned the large-scale ML experimentation infrastructure for hundreds of researchers across integrity and fintech at a large technology company, where we independently proved a multi-year measurement program had no predictive value. Trustworthy measurement under sparse truth is the problem we have spent our careers on.
Design partners — open
If you are optimizing a classifier or prompt where the ground truth is sparse, ambiguous, or expensive to label, we want to work on it with you. Hands-on and reproducible.
Or email us directly at founder@gnosyslabs.com
