Any model. Any vector database. Any knowledge graph. No code. Governance and compliance built in.
One core, three products, inside your own cloud.
“What's our current sepsis screening protocol in the ED?”
Your 2026 protocol, quoted, with a link to the page it came from.
Every answer cites where it came from. Nothing leaves your hospital.
A different person in the building uses each one. All three share the same compliance boundary.
The back end: build RAG agents visually, ship them through a full lifecycle, and govern tokens across every model.
The front end: enterprise chat across several models at once, with memory and a supervisor that verifies the answer.
Denial and appeal automation, and CPT coding, against payer policy libraries tracked daily.
Model-agnostic by design, and built on the same HIPAA-compliant core as every end-user application Inference Analytics ships.
Build once, run across OpenAI, Anthropic, Google, or internal models, connected only at the API level.
Govern access, usage, and cost per user, team, or agent, with a full audit trail for compliance review.
Design, test, stage and ship. Built for healthcare change control.
Close the loop between insight and agentic action, instead of handing off between data teams and the front line.
Every agent inherits the same compliance boundary. That is the reason health systems hand us backend tooling, not just apps.
80+ agents run on the same core today, which shows the platform doesn't need a rebuild for every new department.
Most programmes lose a year building governance before the first agent ships. That work is already done.
Typical elapsed time from engagement start to a first agent running in production, based on Inference Analytics deployments and customer-reported timelines for comparable approaches.
More than 80 agents run on InferAgents. These illustrate the range a single compliant core has to cover, from the ward to the back office.
Specialty-specific agentic support, proving the core generalizes across very different clinical workflows.
Request a demo →Helps operational teams manage supply chain workflows on the same platform used for clinical agents.
Request a demo →Surfaces Epic tip sheets directly in clinical workflows, alongside the system of record rather than replacing it.
Request a demo →“UCM’s own IT organization builds agents directly in Dev Studio. It is infrastructure they build on, not a tool they consume.”
The InferAgents Suite is the HIPAA-compliant agentic AI product suite built by Inference Analytics, and the company's only top-level product. It contains the products a health system buys (Inferch and PayerAgents among them), plus Dev Studio, where a health system's own IT and informatics teams design, test, govern and ship agents across any underlying model. Token and user management, a full software development lifecycle and analytics are built into the shared core. More than 80 agents run on it in production today.
No. Dev Studio is the development interface of the InferAgents Suite, the environment where engineering teams build, test and ship agents. It is not licensed, priced or deployed separately, and it always runs on the same HIPAA-compliant core as every product in the suite.
InferAgents is model-agnostic. It orchestrates across OpenAI, Anthropic and Google models, and across a health system's own internal or self-hosted models, connected only at the API level. An agent is built once against the platform's interfaces, so the underlying model can be swapped without rebuilding the agent.
Protected health information stays inside the platform's compliance boundary. Model providers are reached only at the API level under a business associate agreement, data is encrypted in transit and at rest, access is governed per user, team and agent, and every agent action is written to an auditable log. Each agent inherits this core rather than implementing its own controls.
Yes. That is the intended use. University of Chicago Medicine's own IT organisation builds agents directly in Dev Studio. The platform is designed as infrastructure a health system builds on, not a closed vendor tool it only consumes.
Typically about three months from engagement start to a first agent running in production, compared with roughly eleven months on a generic cloud AI platform or sixteen months building in-house from scratch. The difference comes from the compliance core, governance and SDLC already being in place rather than being assembled per project.
See how InferAgents fits into your model environment, compliance requirements, and existing agents.