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Customers

Real businesses running real AI agents on the Be AI-First Framework. Every claim below is backed by a public URL or screenshot.

Healthcare · Pharmacy · India

Hope Multi-Specialty Hospital

Adamrit (the hospital's HMS) now has a pharmacy reorder agent watching 1,071 medications. Every 6 hours, drugs below threshold trigger Slack alerts and draft purchase orders to the right supplier — pharmacist approves with one click.

"From editing a Markdown SOP to a Slack alert against live patient-care medication data — twelve minutes. We never wrote a deploy step. The agent just inherited the rule and started firing."

— cmd@hopehospital.com
1,071
Drugs monitored
~12 min
Edit-to-agent
2 / 2
Test alerts (DRY)
7
Stages

The agent in production

Reads v_pharmacy_low_stock_alert (pre-filtered view we shipped). Drafts PO to SSV Pharmaceutical & Distributor. Audits every decision in agent_audit_log.

What changed for the pharmacist

Stops manually reconciling stock vs paper. Reviews Slack alerts in 30s. Approves with thumbs-up. PO ships within 4 hours of trigger.

What's next

Flip from DRY_RUN to live after 2 weeks of clean dry runs. Then expand: vendor SLA tracking, expiry-date alerts, prescription dispensing automation.

Other Bettroi-built products

Live URLs, public repos, real revenue. Same stack pattern.

Source of truth: github.com/chatgptnotes — every URL maps to a public repo or live deployment.

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