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See vasus.ai in action — inside a research portal

Below is a fictitious research portal showing structured environmental exposure data for a 2,847-subject cardiovascular cohort. Pick any of the cohort's 4 sites to drill into 90-day trends and an Insight narrative for that location.

DEMO · fictitious portal · for illustration only
Powered by vasus.ai
{cohort.name} cohort (n={cohort.n.toLocaleString()})
Window: {cohort.window} Conditions: {cohort.conditions} Sites: {cohort.sites} cities
{subjects.map(s => ( ))}
Subject City Date PM₂.₅ EHSPI Linked citations
{s.id} {s.city} {s.date} {s.pm25} {s.ehspi} {s.cites.map(c => {c})}
⊕ Export CSV ↓ Equivalent API call showing 5 of {cohort.n.toLocaleString()} rows
Site detail
GET /v1/trends {defaultSite.name} · 90-day series
{defaultSite.pm25_mean} PM2.5 mean
{defaultSite.pm25_p95} PM2.5 p95
{defaultSite.pm25_max} PM2.5 max
{defaultSite.days_who} Days > WHO
Feb 1Mar 1Apr 1May 1
Linked PMIDs (raw signals)
{defaultSite.pmids.map(pmid => PMID: {pmid})}
POST /v1/insight {defaultSite.name} · narrative
RISK: {defaultSite.insight.risk_level}
Synthesis
{defaultSite.insight.summary}
Key mechanism
{defaultSite.insight.mechanism}
{defaultSite.insight.cite_journal} {defaultSite.insight.cite_pmid}
Recommendation {defaultSite.insight.recommendation}
Both panels update together when you switch sites above.
Use case · Research institution

Cohort exposure & mechanism research

Pull longitudinal environmental exposure for any cohort site, with PMID-traceable signals — without manually merging WHO, ECMWF, and PubMed.

90+ days
Saved per cohort vs assembling exposure history manually from raw sources.
The problem

Researchers studying environment-disease relationships spend weeks merging air quality data, weather records, and PubMed-traceable mechanistic signals. The data engineering eats the science budget.

The solution

vasus.ai exposes GET /v1/trends for daily-resolution exposure series at any location and time window, plus POST /v1/insight for the synthesis layer with PMID-grounded mechanistic narrative. Python-friendly clients with batch endpoints for cohort-scale pulls.

The outcome

Researchers move from data-engineering to analysis in days, not weeks. Reproducible exposure inputs that anyone can re-pull. PMID-traceable mechanisms that survive peer review. Citable methodology section ready out-of-the-box.

Integration steps

Four steps to ship

1
Pull historical exposure
GET /v1/trends with cohort site lat/lon, variables list, window_days. Daily-resolution series.
2
Aggregate to your unit of analysis
Subject-day, subject-week, or whatever your study design needs. Response includes pre-computed aggregates.
3
Cite the mechanistic synthesis
POST /v1/insight — risk_level, summary, key_mechanisms with PMID-traceable citations for your methods section.
4
Document and reproduce
Citable methodology block referencing API version, weight vectors, and exact request parameters. Reviewers can re-pull.
Relevant sensitivities
cardiovascular respiratory migraine sleep allergies
Relevant for: epidemiology research groups, public health institutions, longitudinal cohort studies, environmental health PhDs.
Try it live ↗ Discuss this integration →
Want to go deeper?
Read the methodology
Weight vectors, citations, and the EHSPI scoring methodology — all transparent.
→
See the API reference
All endpoint params, response fields, auth patterns, and integration examples.
→
Talk to the founder
For research collaboration, methodology questions, or institutional partnerships.
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