What if rules gained context and judgment? What if a machine earned the right to act? What if decisions were one and done? What if the policy fit the person? What if care continued between visits? What if the $1T spent on admin went to Care.
Kā is the system of record for healthcare's decisions. Frontier models supply reasoning. Kā compounds domain judgment and converts it into evidence-backed, technically enforced, revocable authority to act. Proven decision families compound into standalone companies.
Return $1 trillion from admin and waste to Care.
Coverage decisions, coding, compliance, adjudication, and appeals consume resources that never reach a patient. Kā builds the shared decisioning layer that returns them. Every decision it automates used to cost a clinician's time, a member's attention, or a dollar that never became Care.
Returning that trillion takes more than automating broken processes. It takes reimagining healthcare itself.
The next decade of healthcare will be built on systems that reason and decide in real time.
AI moves from a copilot beside the workflow to the decision system underneath it. The layer the operator runs on.
Rules set the boundary. Reasoning resolves the context. Models interpret; compiled rules execute. Every binding decision is deterministic and replayable.
Population evidence guides care. Individual state determines how it should apply now. AI-native care fits the decision to this person, this episode, this moment.
Care today happens at visits, claims, and reviews. AI-native care thinks in the background, between encounters. Closer to monitoring than to documentation.
Rules are written for populations. Decisions are made for individuals. Expert judgment spans the gap, and no system records it. Kā records both.
Healthcare's systems of record hold the data. Kā is the system of record for the decisions.
A governed layer that turns policies, contracts, and clinical context into decisions that can be computed, audited, and improved. Neutral by design, built to operate across carriers. Every decision emits a trace: the inputs, the rule and authority in force, the human who approved, the outcome. Stitched across members and time, traces form a context graph. Precedent turns searchable. Calibrated abstention is a first-class primitive. Every decision carries the reason it was permitted.
Frontier capability is rented and replaceable. Domain judgment compounds in owned artifacts. Authority is earned, enforced, and revocable.
A shared decisioning layer carries the context that every workflow today rebuilds from scratch.
From fragmented stack to a unified decisioning substrate.
Today's systems are stacked vertically and assemble decisions out of disconnected pieces. The substrate sits beneath them: a single decisioning layer under every workflow, claim, and care plan. Existing tools remain in place as interfaces over it; context stops being rebuilt from scratch.
Each workflow rebuilds its own context.
One governed context. Many controlled workflows.
What if AI had to earn the right to decide?
The autonomy ladder: ceiling is law, rung is evidence.
Every decision family gets an autonomy ladder: five rungs of machine authority under a legal ceiling. The ceiling is what law, contract, or policy permits. The rung is what the system has earned through blind shadow, calibration, and outcome evidence. Every promotion is signed by a certificate; every demotion is automatic.
Authority is earned: evidence promotes a decision family a rung; failure demotes it automatically.
The certificate loop: how a decision family earns its authority.
The ladder defines what a system may do. The certificate loop defines how it gets there. A family enters in blind shadow; human and machine decisions are compared after both lock. Random audit certifies performance; disagreement drives learning. When evidence clears the gates, a certificate grants a rung of live authority, below the ceiling, scoped to a tenant.
We test whether a decision family can be made computational at all. Blind shadow only; no live authority granted.
Is there enough signal, structure, and consequence?
The system runs inside bounded environments under a first certificate. Human-in-the-loop. Full traceability. Measured against gold sets, adversarial sets, and outcome-linked evidence. Calibrated abstention on every uncertainty crossing.
Does it hold rung under drift, under adversarial pressure, under audit?
Recertification runs automatically as models, rules, and authorities change. New model generations translate into expanded authority without re-architecture. Certification lag becomes the differentiation metric.
Does it hold its authority, and can it be recertified faster than the alternatives?
If successive deployments do not make the next safe delegation faster, cheaper, and more trustworthy, the thesis is not working.
We build systems that earn the right to operate.
One substrate holds many decision families. The Studio combines proven ones into companies.
Every category-defining company starts as a question.
Each company begins as a question about a decision healthcare gets wrong at scale. The same question the substrate is built to answer.
We narrow it to a single decision family and fix its legal ceiling.
In blind shadow, the substrate proves it can hold the decision before any live authority is granted.
Proven decision families combine around a healthcare function where shared context and learning compound. Launch requires customers outside Kā, an edge others can't copy, and a clock of its own.
Most questions do not survive the funnel. The ones that do compound: each company sharpens the substrate the next one is built on.
Built alongside healthcare's operators. The substrate develops inside real payer, provider, and risk-bearing environments, where decisions can be shadowed, measured, and eventually delegated.
We start where decisions are dense enough to evaluate, consequential enough to matter, and bounded by a clear legal ceiling.
The substrate proves itself inside specific decision families. Each has its own rules, its own regulatory ceiling, and its own path up the autonomy ladder.
Most admin cost is the same decision fought over and over between parties who don't trust each other. One decision, made with full context and a graded record both sides accept, ends the re-checking. The first call becomes the final call.
Medicine already knows the right care. Delivering it to every n of 1, continuously rather than only at visits, catches problems early: fewer hospital stays, better outcomes, lower total cost.
The same operation costs twice as much depending on where, how, and on whom it is done. Pricing each episode up front, adjusted for patient complexity and provider performance, lets the price itself steer care well. Fair prices for hard cases, so no one is punished for taking sick patients.
Every January, plan switches break care relationships. Carry the member's full picture across plans: providers, prescriptions, network access, out-of-pocket exposure. Disruption surfaces before it happens, and care stays intact.
When judgment stops being the bottleneck, expert attention becomes a routable resource. Calibrated stratification finds the small share of members and episodes where a specific action changes the outcome, and points the effort there: whoever benefits most next, not whoever calls first.
Founders who have built and exited in health tech, run risk-bearing care at scale, and put reasoning models into regulated production.
A founding cohort across frontier ML, clinical operations, and risk-bearing economics.
Chief architect of the substrate. Founded DocASAP and scaled it into a category leader in patient access before its acquisition by Optum, where he went on to build Optum Real. Earlier, McKinsey. Wharton MBA and a computer-science foundation. Leads the certificate loop and the studio's technical doctrine.
Leads partnerships, portfolio, and the operating cadence of the studio. Prior operating leadership at Clover Health and across UnitedHealth Group, spanning payer strategy and risk-bearing care operations. He has scaled regulated healthcare organizations through their sensitive early years.
Six open problems, and the roles that will solve them.
The questions a frontier operating layer for healthcare has to answer.
None of these has a textbook solution. We are hiring the people who want to do the work.
We are hiring engineers, researchers, clinicians, and operators building the AI-native decisioning layer of healthcare.
See open roles