STUDIO

The job done, audited, and cheaper than the status quo

We take a workflow that used to need people and own it end to end, from the research to the running system. Full-stack AI in healthcare, insurance, lending and tax.

The problem we get hired for

Regulated businesses run on documents and rules. The work is slow, manual, expensive and unforgiving of error. Most AI vendors sell a tool and leave the hard part, making it work inside the business, to the buyer.

We do the opposite. Buyers in regulated markets do not need another dashboard. They need the job done, audited, and cheaper than the status quo.

What you get

Agents that do the job: Read the file, make the call, write the record. Built vertical and end to end, on the workflow that hurts most.
Proof and compliance built in: Agents are validated against business outcomes, not benchmarks, and kept current as the rules change. Full provenance: knowledge graph, lineage, and the reasoning behind every output.
Unit economics by design: Composable small models, fine-tuned and orchestrated four or five to a workflow, on infrastructure we control. That is how AI spend falls by forty to fifty percent against frontier APIs, at a cost per unit you can defend to a CFO.
All the IP: You keep everything we build for you, and we have helped clients file patents on it.

How an engagement runs

A scoped statement of work is the preferred model: defined scope, incremental milestones, verifiable progress. Time and materials is available, and discouraged past one month. We are forward-deployed and fast. Zero-to-one is where we win; red tape is where we do not. De-risking comes early, through rapid prototypes and quick stakeholder feedback.

Infrastructure and AI costs for training, data and inference sit outside the effort. You provide them, or we pass them through.

The pod

The setup flexes with the statement of work. These are the roles it is drawn from.

Lead AI engineer: Product-minded owner of the solution vision.
AI and data engineers: Models, pipelines, safety and assurance.
MLOps / LLMOps: Training and inference deployment, monitoring.
Human-feedback talent: Feedback loops for accuracy, and synthetic data so you do not have to hand over yours.
Forward-deployed AI ops: Integration into existing systems and workflows.

The stack under every solution

LAYER 1 · AGENTS

Do the job

AI agents perform the work a person used to: read the file, make the call, write the record.

LAYER 2 · PROOF

Prove it works

Business validation for agents, from the business owner’s point of view. Audit-grade insight a regulated buyer can act on.

LAYER 3 · RULES

Keep it legal

The rules are tracked as they change and the agents adapt, so a law change is a configuration change, not a fire drill.

Who hires the studio

Chief operating, technology and AI officers at health plans and payers, insurers, lenders, and tax and compliance teams. And private-equity operating partners: portfolio companies in regulated industries sit on document-heavy workflows where small efficiency gains compound straight into earnings. The pitch is operational excellence with a playbook, not an AI moonshot.

What we do not take on

Standing meetings. A committee that wants a vendor in the room every week is buying attendance, not a result.
Work with no owner and no date. Approval chains without a single accountable person do not ship.
“Build us a chatbot.” With no workflow and no number attached, there is nothing to measure and nothing to defend.
Free discovery. The working session is thirty minutes and it is real. Spec work past that is an engagement.