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Try vasus.ai for yourself

Hands-on tools and demos. No signup, no data captured.

Side-by-side

See vasus.ai in action — inside a connected-care app

Two views of the same migraine companion app. On the left, the dashboard as it ships today — a polished tracker with a generic weather widget. On the right, the same dashboard with a vasus.ai Environmental Health Insight added: cited, condition-specific, and grounded in peer-reviewed evidence.

Without vasus.ai

A connected-care migraine app as it ships today.

Weather data is shown — but never explained

9:41 Demo · fictitious app
Hello Lisa
Here's your 30-day summary
80%
Schedule success
2
Acute meds
Today in London, Greater London
14°   16° / 9°
Pressure 1009 mbHumidity 78%
Air quality Good
via a third-party weather feed
HomeMedicationSymptoms
✓ Tracks medication, symptoms and adherence well
✓ Familiar, polished patient experience
✕ Weather data shown but never linked to condition
✕ No evidence or citations behind the numbers
✕ No risk level, no synthesis, no trigger context

With vasus.ai

The same app, with a vasus.ai insight layer added.

A cited Environmental Health Insight on every check-in

9:41 Demo · fictitious app
Hello Lisa
Here's your 30-day summary
80%
Schedule success
2
Acute meds
London, UK
Migraine & Weather · Next 24h
Moderate

A rapid frontal passage is driving a 6.2 mb barometric pressure drop over the next 24 hours — a pattern repeatedly associated with migraine onset in the weather-sensitivity literature.

1Prince et al. (2017) — Barometric pressure and migraine. Headache, 57(8).
2Hoffmann et al. (2015) — Weather sensitivity in migraineurs. J Neurology, 262(4).
vasus.ai EHSPI v1.6 · 5 signals · 2 citations
HomeMedicationSymptoms
Everything the app already does, plus:
Cited environmental factors, every check-in
Specific trigger candidates, not guesses
Peer-reviewed sources behind every claim
Better personalised user experience
Integration

One API call powers the entire insight layer.

POST a condition and location. Receive risk level, signals, synthesis paragraph, and inline citations — all in one structured JSON response.

Request
POST /v1/insight  ·  X-Api-Key: your-key

{
  "profile": {"sensitivities": ["migraine"]},
  "location": {"lat": 51.5074, "lon": -0.1278},
  "exposure_window_hours": 24,
  "mode": "agentic",
  "enrichment": {
    "condition_subtype": "episodic migraine",
    "known_triggers": ["barometric pressure change"]
  }
}
Response
200 OK  ·  1.4s

{
  "insight": {
    "risk_level": "Moderate",
    "summary": "A rapid frontal passage is driving a 6.2 mb
barometric pressure drop — a pattern repeatedly
associated with migraine onset.",
    "recommendations": [
      "Surface the insight ahead of symptom onset"
    ]
  },
  "top_matches": [
    {
      "pmid": "28332220",
      "title": "Barometric pressure and migraine...",
      "journal": "Headache",
      "year": 2017
    }
  ]
}

Four steps to ship

1
On app open or daily check-in
POST /v1/insight with condition + stored home location + window=24
2
Display risk chip in dashboard
risk_level from response. Green/amber/red badge. No custom logic needed.
3
Surface synthesis paragraph
summary field. Plain English. Cite the top_matches citations inline.
4
Optional: enable push alerts
POST /api/alert/subscribe once. Hourly monitor fires Web Push when threshold met.
Relevant sensitivities migrainerespiratorycardiovascularsleepallergies
Try it live ↗

Live research tool

The same engine as a free, public web app. See cited health intelligence in action.
API reference

Full API docs

All endpoint params, response fields, auth patterns, and integration examples.
Founding partner

Talk to the founder

For founding-partner conversations, pricing, or technical integration questions.