WorkAgents

Custom models and agents as a service

We build sovereign AI: agents and models you own, trained on your data and wired into your tools. They keep memory across runs, pause for a human before anything consequential, and get better from the work they already did. The intelligence stays in your company.

Advantage

Your data, working

Years of cases, notes, and systems of record are a lead a public model cannot copy. We turn them into agents and models that make the next hour faster than the last.

    “They want to know they own the means of production, and it’s not being transferred to someone else.”

    Alex Karp

    The specialization layer

    Train it. Serve it. Improve it.

    We train custom models on your data, put them into production, and keep improving them as your product and users evolve.

    Post-train tool-using, long-horizon agents.

    Train across text, images, code, and structured data on a state-of-the-art stack using your own harness and graders.

    Optimize your models towards your evals.

    Calibrate rewards to your product KPIs and domain expertise. Inspect what your model is learning with rollout-level observability.

    Stay model-flexible.

    Train from the best available base models, then upgrade as stronger ones ship without changing your harness, data pipeline, or deployment stack.

    Method

    How we train with LoRA

    We do not retrain the whole model. We use LoRA — Low-Rank Adaptation — so your data becomes a thin, owned layer on a strong open-weight base. The base keeps its general intelligence. The adapter is the part that knows your business.

    What it is

    LoRA freezes the base model and trains a small set of adapter weights — a few million parameters instead of tens of billions. Same job, a fraction of the compute.

    How it works

    We inject low-rank matrices into the layers that matter, then train those on your cases, notes, and evals. The adapter is the proprietary model. The base can be swapped as stronger open weights ship.

    Why it fits

    Full fine-tunes are slow, expensive, and easy to overfit. LoRA is fast enough to iterate, cheap enough to serve, and small enough to keep several specialists on one base — review, routing, voice — without a cluster.

    Your data, an open base, LoRA adapters, a model you own — and in practice, it beats frontier models on the jobs that matter to your business.

    Open source

    Models and agents

    Models that get better the more they are used. We build on open models so your business can own the intelligence layer—train on your data, serve in your stack, and keep improving as stronger open bases ship.

    Open models, owned outcomes

    We start from open-weight models so you keep control: swap bases, inspect weights, and avoid lock-in to a single closed API.

    Your data stays yours

    Fine-tunes and adapters are trained on your workflows and domain judgment, then deployed as models you own—not rented intelligence.

    Compounding with every run

    Production traces become the next training set. Models get better the more they are used, with evals and human gates where judgment matters.

    Work

    Agents we’ve shipped.

    Next step

    Book a call

    Tell us what work the agent or model should own. We’ll reply with a concrete approach.