Who you are
Product companies looking for better quality
You shipped early and you're already serving customers. But you need your system to be better, faster, cheaper to unlock the next cohort of customers.
You can't wait a year for foundation models to do everything you need.
Product team exploring a new use case
You're tackling a use case that might or might not work. The opportunity is clear, but you need to know if it's technically feasible.
You need someone to systematically get to the answer, ideally within weeks.
Research team surviving the startup ML stack
You realized that ML was easier at BigCo® than in the startup world.
You need reproducibility, organized data pipelines, sweeping tools, tier-3 GPU prices, and agent-guided experimentation.
What we do
Custom models for your use case
Domain-tuned models and routing that beat generic LLMs on cost and latency without giving up quality.
We define the end goal and work backwards systematically to make a model you own.
Production signals turned into measurable gains
Your users' feedback should be making your AI models better.
We'll help you turn that feedback loop into systematic quality improvements to your model.
Training infrastructure with sane defaults
Reproducible runs, cheap overnight sweeps, checkpoint evaluation that doesn't stop the world.
Let your ML team focus on modelling, not infra.
Typical workflow
Research Sprint
We start by working out whether we can help with the problem and how to scope it.
We then go deeper into the problem, decide how to measure success, analyze errors, and define the levers for improvement.
At the end, we have a repeatable eval, an experimentation roadmap, and a clear target to pursue.
Experimentation & shipping
We experiment in a systematic fashion to learn and make progress on the target.
We sync regularly to discuss insights, better understand the problem, and refine requirements.
We have a big toolkit that ranges from prompt engineering to reinforcement learning, and we'll use the right tool for the job.
Landing
We'll train your team to use the stack to improve the model and keep it running, and make sure everything is documented for the future.
You celebrate another quarterly accomplishment.