As seen on Y Combinator
AI evals engineers
for LLM products.
Eval harnesses, golden datasets and judge models, so every model or prompt change is measured before it ships.
75+
Client work
15+
YC Companies
70+
Head count
Golden datasets, configurable judge models and control documents, so every model or prompt change gets a score.
Tests that judge whether the AI completed a real task correctly, and feed the result back into the product.
Side by side evaluation across models for each output type, against one bar: output an engineer would ship.
Latency, caching and evaluation work that keeps an AI agent fast and accurate in production.
We match on your stack, and you meet the engineer on an intro call before anything starts.
They join your Slack, codebase and standups. Not happy with the first week? Lyra refunds it in full.
Add engineers as the roadmap grows. Clients have scaled between one and ten engineers on one engagement.
An engineer who builds the tests for an AI product: datasets, graders and the pipeline that runs them on every change, so quality is a number you can track.
Both. Lyra engineers have built task-level grading for an AI audit agent and golden-set evals for an enterprise compliance pipeline.
Whatever is in your stack. One engagement ran observability through Langfuse and Grafana next to a custom eval harness.
.avif)
