Train LLMs That Actually Work
Reinforcement finetuning for production. We help you build models that pass your tests, hit your metrics, and improve over time—not just sound good.
Generic finetuning doesn't cut it anymore.
You've tried prompt engineering. You've tried SFT. Your model still hallucinates, fails edge cases, and doesn't improve from real-world feedback.
The frontier labs use reinforcement learning to close this gap. Now you can too—without building the infrastructure yourself.
RL Finetuning as a Service—End to End
We take your base model and make it better at your task. Not with vibes. With verifiable outcomes.
Simulator-Verified Training
Your model learns from rewards that matter—test passes, valid outputs, task completion. No noisy human labels.
Context Graph Training
Teach your model what to remember, retrieve, and forget. RL for memory-aware agents.
Production Inference
Optimized serving for your finetuned models. Low latency, high reliability.
Continuous Monitoring
Track drift, catch regressions, trigger retraining. The loop never stops.
From Messy Data to Deployed Model
in Weeks, Not Months
Forward Deployed Playbook
We embed with your team. We sit with your business people, your domain experts, your engineers. Together, we define what "good" looks like—not in abstract terms, but in testable conditions. We leave you with a reward specification and a curated dataset.
Simulator & Reward Design
We build the environment that scores your model's outputs. Code execution, schema validation, business rule checks, API sandboxes—whatever your task needs. This is your ground truth.
GRPO Training Loop
We run reinforcement finetuning with group-relative policy optimization. Your model generates candidates, the simulator scores them, and the policy improves. No reward model needed. Just outcomes.
Serve + Monitor
We deploy your model with production-grade inference. We track performance in the wild, detect drift, and feed learnings back into training. Your model gets better the longer it runs.
We're Not Another Finetuning API
What You Get When You Work With Us
Smaller, Faster Models
RL-finetuned models often outperform larger generic models on your specific task. Less compute, lower latency, same results.
Task-specific accuracy
Continuous Evaluation Pipeline
Automated testing against your ground truth. Know exactly when performance changes.
Performance over time
Drift Detection & Alerts
Catch distribution shifts before they become production incidents. Automatic retraining triggers.
Forward Deployed Engineer
A dedicated engineer embedded in your Slack, your standups, your codebase. Not a ticket queue.
In your Slack every day
No Reward Model Needed
GRPO training means you don't need expensive human preference data. Just verifiable outcomes.
Labeling cost saved
Verifiable Outcomes
Every improvement is measured against your actual success criteria, not proxy metrics.
IP Ownership
You own the finetuned model weights. Deploy anywhere. No vendor lock-in.
Deploy anywhere
Built for Teams That Ship
AI Teams
Shipping LLM-powered products that need to work, not just demo well
Enterprises
With domain-specific tasks where off-the-shelf models fall short
Agent Builders
Who need models that call tools, plan, and recover from errors
Ops Teams
Tired of babysitting prompts that break every week
Real Problems, Real Solutions
Code Generation
Train models that pass your test suite, not just produce plausible syntax.
SQL & Data Pipelines
Models that write queries that execute correctly against your schema.
Structured Output
JSON, API calls, forms—validated against your business rules, every time.
Agentic Workflows
Multi-step task completion with credit assignment across tool calls.
Let's Make Your Model Actually Work
We'll start with a 2-week pilot. You bring the task. We bring the RL.