AI reliability lab
Ship AI that actually works
in production.
cruq.ai converts production runs into evidence, replays edge cases in safe environments, and trains compact private models that do the job - at a fraction of the cost.
Capture
Record what actually happens in production
Replay
Turn hard cases into safe, repeatable training scenarios
Optimize
Distill repeat work into a private, purpose-built model
Why cruq.ai
Production reliability is an engineering practice, not a better prompt.
Start from evidence
Every improvement begins with real traces captured from live runs - not assumptions or synthetic benchmarks.
Practice safely
Replay failure modes and edge cases in isolated environments before they hit your users again.
Own the output
Train specialized models on your data so you're not dependent on general-purpose APIs forever.
Observability
See exactly what your AI did - and why it failed.
The cruq.ai observability layer captures every trace, input, and output in production. You get a complete audit trail you can filter, annotate, and replay - so you always know what happened and where to improve.
- Full trace capture across every agent step
- Failure replay with exact context
- Eval set creation from production samples
- Regression monitoring on every deploy
RL Environments
Practice the hard cases before they cost you.
cruq.ai wraps your production failures into structured RL environments. Your model practices the edge cases that actually happen in your business - scored against real outcomes, not synthetic rubrics.
Each environment is built from your traces, tuned to your scoring criteria, and isolated so nothing reaches production until it passes.
Private Models
Stop renting intelligence you could own.
Once we've captured your patterns and validated behavior in simulation, we distill that knowledge into a compact model trained specifically on your domain. It runs faster, costs less, and behaves exactly the way your business expects.
You keep the weights, the training data, and full control - no vendor lock-in, no data leaving your stack.
10x
lower inference cost vs. frontier models
<2s
average latency on fine-tuned tasks
100%
data stays on your infrastructure
Products we offer
Production AI agents, shipped across industries.
The same reliability and evaluation tooling we build at cruq.ai powers a growing family of vertical AI products.
Writing
From the lab
Why RL environments beat prompt engineering for edge cases
Prompts are instructions. Environments are practice. Here's why the distinction matters when your agent keeps failing on the same class of inputs.
The hidden cost of frontier models in enterprise workflows
Most teams don't realize how much of their API bill comes from a small set of repetitive tasks. We traced the pattern across 12 deployments.
Trace capture without slowing down your agent
Observability shouldn't be an afterthought. Here's our async capture architecture that adds less than 5ms overhead to any LLM call.
What we learned building private SLMs for three different verticals
Finance, legal, and ops all have different failure modes. Here's what we found when we trained domain-specific models for each.
Get started
Ready to make your AI work in production?
We work with a small number of teams at a time. Tell us what you're building and we'll set up a 30-minute call to see if we're a fit.
Book a call ->