Healthcare AI · Agentic systems · AI governance
Dr. Ravi Teja Thiriveedhi
I treated patients and ran hospitals in Hyderabad for twenty years. Since February 2026 I design, build and operate production multi-agent AI, with the discipline a clinic needs: written protocols, safety gates, audits and measured results.
BDS, dental surgeonLed 3 healthcare facilities (2 founded)Hyderabad, India · IST · open to remote work worldwide
24production AI agents across 2 live platforms
1,026automated tests passing at my AI OS release v1.3.0
−91%AI heartbeat spend on a client platform
20 yrsclinical practice and hospital leadership
Selected work, 2026
Shipped and running
Live · Client
AI operations platform for a real-estate brokerage
A 15-agent WhatsApp and Telegram platform that captures, qualifies, routes and follows up enquiries, with the owner approving anything sensitive. Live since June 2026.
- 460+ leads and 8,688 logged customer interactions
- Heartbeat tokens 12.0 M → 1.0 M a day; ₹0 marginal API cost per lead
- 129 bugs closed, each with root cause, fix and lesson
Live · Own platform
Tejas Empire OS
My self-hosted, governed multi-agent AI operating system: 9 agents for orchestration, research, job search, education, SRE triage and records, on a 2012 Mac mini with 8 GB of RAM.
- 8 releases in 7 days (v1.0.0 → v1.3.0, Oct 2026)
- Release gate: 99 of 99 suites, 1,026 tests, 0 failures
- Gateway CPU 158–209% → 2–4% after a traced fix
Open source · Apache-2.0
Agent governance kit
Autonomy levels, risk classes, approval tiers, kill switches and audit templates, mapped to the OWASP Agentic Top 10, NIST AI RMF and ISO/IEC 42001, with a healthcare overlay for India and a small policy checker.
Open source
LLM token economics
How an agent fleet's heartbeat tokens fell 91%: 18 cost methods, a measured before and after, and an audit checklist anyone can run on their own fleet.
Pilot product · pre-go-live
Dental WhatsApp Front Desk
An administrative-only assistant for small dental clinics: consent-first booking, reminders, recalls and hand-off to a human for anything clinical. It never diagnoses, prescribes or markets.
- Designed toward India's DPDP Act, Telemedicine Practice Guidelines 2020 and Dental Council rules
- 17 automated tests, including a 14-day simulated clinic pilot; go-live waits on dentist, lawyer and clinic sign-off
Try it yourself
Live demos, in your browser
Each demo runs the same rules as its repository, on invented data. Nothing is sent anywhere.
How I work
How I work
AI-native, with brakes
I direct AI coding agents through specs and task cards with acceptance criteria. I own the architecture, review, testing, release and operations.
Governance first
Autonomy is earned. Anything touching patients, money, customers or data needs a human approval and leaves an audit trail.
Cost as an outcome
Measure first. Deterministic checks before model calls, right-sized models, cache-friendly prompts.
Clinical depth
The domain I bring to health tech
- About 200,000 patient encounters since 2010, across three healthcare facilities (two I founded, one I administer)
- 3,000+ root canal treatments and 600+ surgical extractions
- Sterilisation governance that cut reinfection by 90%; EMR documentation compliance above 95%
- CBCT-to-3D-printed surgical models that raised treatment acceptance by 50–60%
The full clinical record
Stack
Stack
- Multi-agent orchestration
- OpenClaw
- MCP
- Claude Code
- Codex
- RAG
- Ollama
- Python
- Node.js
- TypeScript
- React
- SQLite
- WhatsApp Business API
- Telegram bots
- n8n
- Tailscale
- GitHub Actions
- DICOM → 3D print