Lower your dependency on frontier models.
Two services, one pipeline. Fine-tune an open model on your data so it does your job better than a rented one, or deploy a small language model where a large one is too slow, too costly or not allowed.
Fine-tuning
LoRA and preference tuning on open-weight models, evaluated blind against the base on held-out prompts. If it does not win, it does not ship.
- Supervised fine-tuning (LoRA, QLoRA) for domain judgment and voice
- Preference tuning (DPO) to your experts’ standards
- Reinforcement fine-tuning (GRPO) on verifiable tasks
- Evals, fabrication traps and leak tests as part of the deliverable
Small language models
Sub-10B models tuned for one job and served on a single GPU, a CPU or the edge, with semantic guardrails and a verification harness.
- Distilled from larger models, verified against ground truth
- Semantic guardrails and structured outputs
- Serving with vLLM, llama.cpp or MLX
- For latency, cost, privacy or resource limits
Working session to running model.
Working session
Which workflows deserve an owned model, which should stay on an API, and what the cost per task looks like either way.
Tune and evaluate
Blind pairwise judging against the base model on held-out prompts. The eval report ships with the weights.
Deploy and monitor
On compute you control, monitored on one cockpit: cost, output and approvals side by side.
Two shipped adapters, both beat their base blind.
Published on Hugging Face with their evaluation sets. The marketing model drafted the copy on this site; the CFO brain advises the Finance Manager AI employee.
Find out which workflows deserve an owned model.
Bring three real tasks. We will tell you which to tune, which to shrink and which to leave on an API. No pitch deck. Just a technical conversation.