Own your learning loop

To get good results from a model, a company has to show it how the company works. That is the reverse-information paradox: the more you reveal, the more of your know-how sits in prompts, traces and corrections on someone else’s platform. We help you battle it with the 5Cs.

  1. Control

    Your evals, traces and decisions stay in your systems

    We write private evals with the people who do the work, because they define what good looks like in your company. Every agent run, tool call, correction and approval is recorded in your own database, and model calls go through a gateway you control.

  2. Capability

    Training on environments built from your workflows

    We turn your workflows into learning environments with verifiable tasks, and fine-tune open-weight models against them. Training and serving run on dedicated GPUs from our neocloud partners or in your own cloud.

  3. Choice

    Switch models without losing what you’ve built

    Stablehand, our self-hosted agent runtime, runs Claude Agents, LangGraph and PI harnesses behind a gateway across frontier APIs and open-weight models. When a new model arrives, your evals decide whether it takes over, and what you have built stays with you.

  4. Cost

    Each task goes to the cheapest model that passes

    Budgets per agent and per team, with routing decided by eval results. In our document-extraction study, a fine-tuned 27B open-weight model scored higher than a frontier model on the sealed test set and cost a ninth as much to run.

  5. Compounding

    Corrections and outcomes become eval cases and training data

    We extract learnings from frontier-model attempts on your work (the corrections, approvals and outcomes) and feed them back into your evals and your own models, so all the tokens you pay for compound.

What we deploy

Your infrastructure
Your products, workflows and the people who review the work

Stablehand

Self-hosted agent runtime: harnesses, one sandbox per session, tools, approvals, budgets and the event log.

Control · Choice · Cost

Private evals

Task contracts, graders and rubrics written with your experts

Control · Choice

Traces

Every run, tool call, correction and approval, in your database

Control · Compounding

Model gateway

Routes each task to a frontier API or your own open-weight model

Choice · Cost

Learning environments

Verifiable tasks cut from your workflows, for training and testing

Capability

Training

Fine-tuning and reinforcement learning on open-weight models, with versioned adapters

Neocloud partner GPUsCapability · Cost

Serving

Your adapted models, behind the same gateway

Neocloud partner GPUsControl · Cost

Data preparation

De-identification, rights and provenance records for each dataset

Control

Frontier learnings

Corrections, approvals and outcomes from frontier-model attempts, kept as training and eval signal

Compounding
Promotion rule: a new model or adapter replaces the current one only when it wins on your evals, and every round records the score, the cost per task and what changed.
Compounding
Frontier model APIs sit outside, reached through the gateway under rules you set on what may leave.

How an engagement runs

  1. Write the evals

    Define the tasks, the evidence a good answer needs and the failure cases, with the people who do the work.

  2. Run the agents

    Deploy Stablehand in your infrastructure, connect your tools and put approvals where judgment is needed.

  3. Record corrections and outcomes

    Learnings from frontier-model attempts, the corrections and the outcomes become eval cases and training examples.

  4. Fine-tune where the evals show a gap

    Train an open-weight model for the gap, and promote it only when it wins on your evals.

Reference: “The Reverse Information Paradox”, July 2026.

Work with us

Tell us what the work is, what data it touches and what constraints apply. We’ll work out what to build and how to measure it.