Try the Diagnostic Recommendation Engine

This is a working demonstration of the engine described in the CEO deck. Generate a synthetic canine patient case, run it through the engine, and see the ranked diagnostics a clinician would receive in the exam-room workflow — with reasoning, priority, and projected revenue for each.

How This Demo Works

The production engine would integrate with the clinic's practice management system and ingest real patient records. For this public demo, we generate a synthetic canine case using the same profile schema (signalment, history, presenting signs) and pass it to Claude — prompted to act as the recommendation engine — via the Anthropic API. The engine returns ranked diagnostic recommendations aligned with AAHA preventive-care guidelines, each with reasoning and a projected revenue contribution.

What you need to run this: an Anthropic API key. The key is stored only in your browser for the session and is sent directly to api.anthropic.com. It never touches a PawStyle server. Details below.
Stored in this browser tab only. Cleared when you close the page. Get a key at console.anthropic.com/settings/keys. The demo costs approximately $0.02 per case.

1. Generate a Patient Case

Generate a synthetic case with the persona generator, or hand-tune the fields below. Twelve realistic archetypes are built in — senior dogs, young dogs, recurring patients, overdue seniors.

2. Engine Output

Awaiting case input. Generate or enter a patient profile on the left, then click Run Engine.
About the demo: This runs Claude (claude-sonnet-4-20250514) against the Anthropic Messages API directly from your browser, using a structured prompt that instructs the model to act as a clinical decision-support engine aligned with AAHA preventive-care guidelines. The output is illustrative — it demonstrates the shape of the production engine's recommendations (ranked diagnostics with reasoning and revenue projections), not definitive clinical guidance. A production engine would run on the clinic's own patient dataset, integrate directly with the PMS, and be validated against clinician acceptance data over time. This demo does not store patient data and cannot reach real medical records.

What You're Seeing vs. What We'd Build

This Demo

  • Synthetic patient cases generated from twelve archetypes
  • Single API call per case — returns structured JSON with diagnostics, reasoning, priority, and revenue estimates
  • Runs entirely client-side with your API key
  • Illustrative; no real patient data, no PMS connection

Production Engine

  • Ingests real patient records from the clinic's PMS
  • Retrained on the clinic's own accepted/declined recommendation history (the compounding data advantage)
  • Served from the clinic's own infrastructure with HIPAA-equivalent data governance
  • Clinician-in-the-loop: the engine recommends, the clinician decides and acts
  • Measured against acceptance rate, revenue per visit, and diagnostic capture lift

Once You Have 30 Cases, Patterns Emerge

Running one case proves the engine works. Running thirty proves the engine learns. The Cohort Insights page runs k-means clustering and a decision-tree risk classifier in your browser — live — on a 30-patient cohort, to show what compounds.

See Cohort Insights Read the Full Business Case