The AI-Native Data Platform for Healthcare

Stratus Labs is building an AI-native data platform for healthcare that solves the data fragmentation crisis preventing organizations from making timely, preventative, and defensible decisions.

Our thesis is that as healthcare organizations collectively manage over $5 trillion in annual spending across dozens of disconnected systems, point solutions will not scale. We believe the solution is a platform that sits on top of existing infrastructure, connects structured and unstructured data, and enables stakeholder-specific automated workflows and simulations that drive measurable cost reduction and improved patient outcomes.

Why is this Possible?

AI can now unify data that was previously siloed. Advances in language models and agentic systems make it possible to connect to dozens of disparate healthcare systems, parse unstructured clinical documentation at scale, and build a unified queryable model across sources that were never designed to work together.

Overlay architecture eliminates the need for replacement. Many payers operate three or more claims systems due to acquisitions, some dating back twenty or more years. A platform that sits on top of existing infrastructure and unifies it can deliver value in weeks rather than requiring multi-year transformation programs.

Healthcare value creation is data-constrained. Reducing Medical Loss Ratios, improving quality metrics, and automating administrative workflows are primarily constrained by fragmented, inaccessible data rather than by a lack of clinical knowledge or operational intent. Once the data is unified, the workflows and simulations follow.

Our Approach

We deploy lightweight agents that embed into existing data systems and unify structured and unstructured data into a single queryable interface to be acted upon. Everything is kept current as new data arrives, with no migration or replacement required.

Workflows act on unified data continuously: routing high-risk members to care management before costly claims occur, capturing clinical documentation through voice-to-text and injecting it into hospital systems, monitoring populations against quality measures in real time, and eliminating manual reporting across claims systems.

Simulations model organizational impact before deploying. Test how proactive interventions would have affected last year's Medical Loss Ratio. Model labor savings of documentation automation against current staffing. Evaluate risk stratification thresholds against historical outcomes.

Atlas brings it all together with a conversational interface that understands your data, your systems, and your organization. Use it to explore unified data in natural language, build workflows and simulations autonomously and at scale, and surface insights that would otherwise require a team of analysts to find.

Guiding Principles

Deploy without disruption. Our platform connects to what already exists and makes it work together. No migration, no replacement, no multi-year transformation programs.

Healthcare-native by design. The platform understands clinical terminology, payer economics, quality measure specifications, and the regulatory context that makes healthcare data uniquely complex.

Value measured in dollars. Every capability maps to a quantifiable outcome: reduced Medical Loss Ratio, reduced Administrative Loss Ratio, improved Star ratings, or freed staff capacity. If it does not move a number, we do not build it.

Looking Forward

We see Stratus as the data and AI layer that healthcare organizations rely on to operate effectively. We serve third-party administrators, insurance carriers, hospital systems, diagnostic laboratories, and healthcare technology startups.

If you're a healthcare organization dealing with fragmented data, we'd love to talk.