Pretty Fire is a physician-led AI venture building the systems that read, organize, and reason over the messy reality of patient data — so clinicians and their teams can act on what matters, sooner.
Medicine doesn't have a data shortage. It has a data legibility problem. The record of a patient's journey is scattered across notes, messages, images, and systems that never talk to each other — and the intelligence is lost in the gaps.
Pretty Fire closes those gaps. We build AI that assembles a coherent, voice-attributed story of each patient and surfaces the next right action — without ever compromising privacy.
Navigator is built on a layered intelligence model. New information appends; only what's new gets analyzed; profiles re-synthesize incrementally. Nothing is ever reprocessed from zero.
An append-only, chronological record of every voice-attributed input. Never modified, never re-analyzed.
Structured data drawn from each input — incrementally, so only new entries trigger work.
Clinical, financial, and engagement profiles that re-synthesize only when new signals arrive.
Ephemeral, on-demand recommendations generated from the current state — the next right action.
Pretty Fire didn't start with a model. It started with a software company in the 1990s and a stubborn belief that the same engineering discipline could make patient data safe, structured, and genuinely useful. Three decades later, that belief is the foundation everything else is built on.
Founded while its co-founder was still in medical school at Yale, Phaedrus Inc. was a software firm that built websites for marquee clients — the FleetCenter (home of the Boston Bruins and Celtics), the Bruins themselves, and the USS Constitution site for the U.S. Navy. By 1998 it had already received and turned down buyout offers. The instinct was set: build systems that big institutions can trust.
Phaedrus turned its engineering from the web to medicine. CLAY — the Collaborative Layered Application — first ran at Yale as a platform to coordinate research on plastic surgery patients. It became the subject of a published research paper, and the start of a long bet: that clinical images and the data around them belong in one secure, structured record.
CLAY v1.0 was one of the first systems ever built to capture, store, and share clinical visible-light imaging — dermatology, wound care, pathology, plastic surgery before-and-afters — under HIPAA. Developed in consultation with the U.S. Department of Health and Human Services before the Privacy and Security Rules were finalized: encrypted access, full audit logging, hourly redundant backup, and DICOM integration with legacy hospital systems.
CLAY went on to manage 100,000+ image-rich medical records at HMO scale — turning loose photos on memory cards into a compliant, searchable record. Across clinical trial and production: zero unplanned downtime, and not a single security breach.
Pretty Fire brings modern AI to that foundation. Navigator is the result: a system that reads the full, multi-voice record of care and turns it into intelligence a team can act on — privacy-first, physician-led, and built where the work actually happens.
Identifiers live in a separate vault. AI reasons over de-identified data. Privacy isn't a setting we add later — it's the shape of the system.
Built by and for clinicians. The system surfaces signal and proposes action; the human stays in command of the decision.
Every output is conservative by default. We would rather under-claim than overstate — in a clinic, a confident wrong answer is the dangerous one.
We build systems that survive contact with messy, real-world data and daily clinical use — not just the happy path.
The system learns without forgetting and improves a little every day. Trust compounds the same way.
If it doesn't make the next action clearer for a real team, it doesn't ship. Utility is the only metric that counts.
Whether you're a clinic, a health-tech team, or an investor in responsible AI for medicine — we'd like to hear from you.