Own the intelligence your work creates.
Stop renting intelligence. Own a model specific to the work you do, uniquely. ACTAVA turns the workflows your agents run every day into specialized healthcare models your organization controls.
- HIPAA Compliant
- SOC 2
- 21 CFR Part 11
- Enterprise Ready
Every competitor rents the same intelligence you do.
Frontier models arrive identical. Your closest competitor licenses the same weights, reads the same release notes, and gets the same upgrade next quarter. Whatever advantage sits inside the model, the whole market holds it at once.
Long-horizon, policy-rich healthcare tasks the best frontier agent finishes on the first try.
ACTAVA χ-BENCH v1.0
Of U.S. national health spending consumed by administration.
JAMA
Annual administrative-simplification opportunity in U.S. healthcare.
McKinsey
Frontier capability alone does not finish healthcare's long-tail work.
A frontier model knows an extraordinary amount about medicine and close to nothing about how your organization runs. It has never read your medical policy exceptions, your delegated approval boundaries, or the payer-specific quirks your appeals team carries in their heads. That knowledge decides whether the work lands correctly. It lives inside production, and no amount of pretraining puts it there.
See how we benchmark agentsOwn where the work is uniquely yours. Rent the rest.
Owning a model earns its cost on some workflows and wastes it on others. Five questions settle it, and healthcare answers them differently than most industries do.
Proprietary data
Your policies, exceptions, and expert corrections decide whether the work lands correctly.
The task runs on medical and world knowledge every model already holds.
Cost at volume
Training cost amortizes across thousands of long-tail runs a month.
Volume stays low enough that per-call pricing wins outright.
Speed and latency
Latency sits inside a live call, a queue SLA, or a member waiting on an answer.
A few extra seconds cost nothing downstream.
Performance ceiling
You start behind on one narrow job and climb past every frontier model on it.
You need competence across many jobs today and can live with the ceiling.
Regulatory accountability
You show a reviewer what the model learned, from which evidence, and when.
Harness-level guardrails and audit trails carry the accountability on their own.
You answer this per workflow, not once.
Most healthcare organizations land on both. ACTAVA KORA stays model-independent, so policy-based routing sends hard low-volume reasoning to a frontier model and the proprietary, latency-sensitive long tail to the model you own. Swap or upgrade either side without rebuilding your agents.
See routing and cost controlsBuild → Test → Learn → Own
One lifecycle, four stages, and an asset you keep at every one. Each stage answers a production requirement and leaves behind something that makes the next stage cheaper.
ACTAVA KORA
Context, tools, policies, approvals, human escalation, and runtime observability give agents what they need to finish regulated work. You keep a reliable production workflow.
Explore KORAACTAVA χ-BENCH
Your hard cases, failures, and expert corrections become simulations, weighted rubrics, red-team probes, and regression tests. You keep repeatable evidence of what works and what breaks.
Explore χ-BENCHACTAVA CURA
Authorized, customer-specific evidence teaches and evaluates a specialized model under your control, with Cura 1T serving as the healthcare teacher behind it. You keep intelligence competitors cannot license.
Explore Cura 1TMost organizations stop at the first stage. They deploy an agent, watch it work, and throw away the evidence it produced.
Your specific intelligence already exists. It sits scattered.
What your organization already knows
Medical criteria, plan configurations, prior decisions, escalation paths, system quirks, and the exceptions nobody ever wrote down. This sits across your EHR, core administrative platform, CRM, shared drives, and the memory of people who have been there twelve years.
What your agents learn while working
Every production run creates new evidence. A tool call that failed at step three. A reviewer who corrected the agent's reading of a benefit exclusion. An appeal that cleared on the second attempt and not the first.
Pointing an agent at every record you own moves the problem closer to the work without solving it. The hard part is knowing which context matters for which decision, then proving it with outcomes rather than assuming it.
Swipe to see the full diagram
“Institutional knowledge is a valuable asset. Yet it typically lives only in the heads of people who have been around for a long time and is buried in email chains & PDFs. It doesn’t scale, and inevitably gets lost in the ether when people leave. To solve this, I’m building AI in ACTAVA that captures every aspect of how we work.”
T. Y. Alvin Liu, M.D.
Endowed Professor and Inaugural Director, James P. Gills Jr. MD and Heather Gills Artificial Intelligence Innovation Center; Vitreoretinal Surgeon at Johns Hopkins Medicine
Before asking you to trust the loop, we ran it on our own model.
Cura 1T is a one-trillion-parameter healthcare model, post-trained from Kimi-K2.6 through recursive self-improvement. Each round targets a capability, trains on it, grades the resulting trajectories, reads the failures, and refines the next data mixture from what it finds. The published record includes the rounds we reverted, including one that lifted headline scores while damaging a held-out subset. That gate is the point.
- MedAgentBench
- 94.0MedAgentBench task success as a native tool-caller against a running FHIR serverKIMI-K2.6 (base)84.7CURA 1T94.0
- AgentClinic NEJM cases
- 2×the base model's score on AgentClinic's NEJM cases, the hardest interactive diagnosesKIMI-K2.6 (base)40.0CURA 1T80.0
- Healthcare panels led
- 0 of 6Healthcare benchmark panels where Cura 1T leads every frontier reference
Second on MedXpertQA multimodal
Cura 1T is a research model, not a medical service, and not a substitute for a clinician. Benchmark scores do not establish safety for unsupervised clinical use. Panels comprise HealthBench Professional and Hard, MedXpertQA text and multimodal, AgentClinic, and MedAgentBench.
Read the technical reportThe loop that produced Cura 1T produces specialized models for your workflows. Cura 1T becomes the healthcare teacher behind them.
Your evidence stays inside your boundary.
Private customer learning
Your policies, PHI, prompts, outputs, raw trajectories, expert corrections, and evaluation examples stay inside your authorized boundary. They improve your agents and your models, and nobody else's.
Shared ACTAVA learning
χ-BENCH, synthetic simulations, licensed data, and contract-permitted de-identified failure patterns strengthen the platform for everyone, with no pooling of customer records.
Cura 1T is the teacher in that loop and never the student: it is never trained on your data. Customer-controlled means you retain control over your data, your policies, your permissions, and your authorized learning environment. Ownership, portability, and model IP follow your governing agreement with ACTAVA.
Governance runs across all of it. ACTAVA CHRYSO enforces role-based permissions, audit trails, human approval workflows, and pre-deployment evaluation, so an agent that learns from your work stays traceable while it does. Holding that authority in-house is what AI sovereignty means in practice.
One consequential workflow first. The model follows.
Nobody starts by training a model. You start by putting one hard workflow into production and keeping every piece of evidence it generates.
Start quickly.
- Define one workflow your generic tooling keeps failing.
- Build the first agents alongside your own experts.
- Deploy into your own environment, behind your own controls.
Run safely.
- Evaluate against your own benchmark before production.
- Escalate to a named human at every boundary you set.
- Audit every run, tool call, and approval end to end.
Own the intelligence.
- Capture expert corrections as reusable evaluation assets.
- Prove each improvement against a held-out set first.
- Specialize a model once the evidence justifies the cost.
The future is deeply agentic. Own yours.
Every workflow you run through a rented model produces evidence that vanishes. Run it through ACTAVA and that evidence compounds into intelligence your competitors cannot license, copy, or shortcut. Talk to us about the first workflow worth owning.
From production workflows to customer-controlled intelligence.
Don't give your agentic future away to a single model provider. Don't mistake consumer tools for real, safe Enterprise Agentic Tools. Enable your citizen developers to create and manage the AI Agents they need to run their part of your business.
Build complex agents. Test their reliability. Learn from every workflow. Own your intelligence.