I help regulated enterprises ship LLM systems that pass audit.
Healthcare-grade AI governance is my specialty — the controls that satisfy HIPAA, the FDA, and a hospital's security review. The same patterns transfer cleanly to financial services, aviation, and federal, where AI failure is just as expensive. I architect the governance layer, evals, and infrastructure that make production GenAI trustworthy at scale.
6 years building production AI · Founder, ClarisMD · Building enterprise LLM systems for a Fortune 100 airline (contract, via KForce) · ex-Senior Data Scientist, 4CRisk.ai
Numbers from systems already in production
Every figure below comes from shipped, client-verified work — not a pitch deck. Hover for the context.
Three ways teams work with me
The wedge is healthcare-grade governance. The patterns — multi-tenant LLM gateways, audit-defensible evals, risk registers tied to controls, agent and RAG observability — transfer cleanly to financial services, aviation, federal, and any enterprise where AI sits in front of a regulator, a P&L, or a customer who can sue.
Compliance-blocked AI launches
You've built a GenAI product and legal, security, or compliance is blocking the ship. I architect the governance layer — risk register mapped to NIST AI RMF, ISO/IEC 42001 controls, HIPAA/HITECH PHI handling, EU AI Act Article 9–15 conformity — and the eval suite that lets you defend the decisions.
LLM systems that scale & stay safe
RAG that hallucinates in production. Agents that loop. Costs that won't predict. Evals that don't catch regressions. I redesign the system end-to-end — gateway, retrieval, prompt strategy, eval pipeline, observability — so it runs at audit-friendly scale.
Fractional AI architect / advisor
Pre-Series-B startup or mid-market enterprise that needs senior AI leadership without a full-time hire. I sit in your weekly architecture review, vet vendor decisions, mentor your ML/AI team, and produce the board-ready AI risk posture and evidence pack.
Shipped in regulated, high-stakes environments
All client engagements anonymized to NDA scope. Outcomes verified by the engaging team.
Enterprise LLM platform for a Fortune 100 airline
RAG for FedRAMP / CMMC compliance automation
Production RAG scaled to 10K+ users in regulated finance
Multilingual voice AI for 9 Indian languages
Non-invasive vital-sign monitoring from smartphone video
ClarisMD
clarismd.com · Self-funded, solo-built · Live in production
A healthcare AI governance platform that secures and governs enterprise interactions with OpenAI, Anthropic, Gemini, and AWS Bedrock through a unified LLM gateway. I built it because the controls my consulting clients kept asking for didn't exist as a packaged product.
What it does
- Multi-provider LLM gateway: tenant isolation, encrypted key management, semantic caching, rate limiting, cost attribution, budget controls.
- Healthcare-grade safety: PHI/PII detection & redaction, prompt-injection defense, toxicity validation, constitutional-AI evaluation, automated red teaming.
- Governance for RAG & agents: hallucination detection, retrieval monitoring, agent-loop detection, memory governance, audit-ready evidence generation.
- Controls mapped to 17+ frameworks: HIPAA, HITECH, GDPR, EU AI Act, NIST AI RMF, ISO 42001, SOC 2, FDA SaMD AI/ML, ONC HTI.
- Enterprise infra: RBAC, MFA, audit trails, OpenTelemetry, Prometheus, Grafana, Sentry, automated CI/CD.
Why it matters for clients
ClarisMD is proof I've operationalized these frameworks end-to-end — not just read the PDFs. When you hire me to architect your governance layer, I bring the same patterns I've already shipped, debugged, and stress-tested in a production product.
The full toolkit I bring to an engagement
Governance is where I lead, but it sits on top of six years of hands-on ML. Every technique below is one I’ve shipped in production — across agents, retrieval, classical ML, vision, and speech — not a syllabus.
Capability areas
Agentic AI & orchestration
RAG & retrieval
Prompt engineering
LLM safety & AI security
Evaluation & observability
LLM gateway & serving
Supervised learning
Unsupervised learning
Deep learning & fine-tuning
Computer vision
Speech & voice AI
MLOps, data & cloud
Not just read — shipped, debugged, and audited
Framework categories
Healthcare
AI-specific governance
Privacy & general
Government / Federal (US)
Production-AI patterns I ship
Research & stage
- ICCIT 2025 — 4th International Conference on Creative Communication and Innovative Technology (presenter).
- IEEE — AI Analysis of Cultural Narratives Shaping Emotional Responses to Infertility · paper
- IJAST — Analysis of Deep Learning algorithms on COVID-19 Radiography Database · code
- IJAST — AUTHEER: A Voice-Based Speaker Authentication System · code
Available for keynotes, panels, and podcast guesting on AI governance, healthcare AI compliance, and production LLM systems.
Field notes from production GenAI
I write and speak about LLM systems design, AI governance in regulated industries, and what actually breaks in production GenAI. Topics I go deep on:
AI architect, Bengaluru — working across timezones
Six years in production AI: started as an MLE shipping NLP and computer-vision systems (US Tech Solutions, CRMNext), spent 2.5 years scaling regulated-industry RAG at 4CRisk.ai, and now run consulting engagements alongside building ClarisMD.
Most enterprise AI projects don't fail on the model — they fail on governance, eval discipline, and the boring infrastructure that makes the model trustworthy at scale. That's the part I'm good at.
Outside enterprise work, I co-founded Rigetnest Innovation Labs, advising early-stage AI product teams.
Industries shipped in
Education
B.Tech, Computer Science (CGPA 8.79). Computer Vision Nanodegree (Udacity), LLMOps (Udacity), NPTEL Deep Learning I & II.
People who've worked with me
Senior collaborators vouching for the work and how I operate. Reach them directly on LinkedIn.
I led Aman early in his career at CRMNext. He took ambiguous ML problems and shipped them into production — careful, fast, and dependable under deadline. He thinks about how a system behaves in the real world, not just in a notebook.
Aman pairs real research depth with the discipline to put models into production safely. On our work together he's the one who insists on evaluation rigor and governance before scale — exactly what regulated teams need and rarely get.
Common questions
How I scope, run, and protect client work.
What kind of work do you take on?
I focus on LLM and ML systems that have to pass audit — most often in healthcare, finance, aviation, and federal contexts. That spans hands-on builds (RAG, agents, evaluation harnesses) and the governance layer around them: HIPAA, EU AI Act, NIST AI RMF, ISO 42001, SOC 2, and FedRAMP.
How does an engagement usually start?
Send a short email describing what you're building, what's blocking you, and your timeline. I reply within 48 hours on weekdays, and if we're a fit we'll do a 30-minute scoping call before any commitment. I take a small number of new engagements each quarter.
What engagement formats do you offer?
Three: a focused 4-week governance sprint to get a system audit-ready, a hands-on build engagement to ship the system itself, or fractional advisory where I produce your board-ready AI risk posture and evidence pack on a recurring basis.
Do you work with teams outside India?
Yes. I'm based in Bengaluru and work remotely with clients across the US, Europe, and India, scheduling synchronous time around your working hours. Most collaboration runs async with regular checkpoints.
Which frameworks and regulations do you cover?
HIPAA, the EU AI Act, NIST AI RMF, ISO 42001, SOC 2, and FedRAMP, among others — 17+ frameworks mapped to live controls rather than treated as paperwork. I translate regulatory requirements into technical controls your engineers can actually implement.
Is client work kept confidential?
Always. I work under NDA, and every engagement on this site is anonymized to NDA scope. Specifics about clients, architectures, and data stay private.
Let's scope your engagement
I take a small number of new engagements each quarter. The fastest path is a short email — tell me, in 4–5 lines:
- What you're building (or trying to ship).
- What's blocking you — compliance, scale, evals, hiring, vendor decision.
- Your timeline.
- Whether you need a 4-week sprint, a build engagement, or fractional advisory.
I reply within 48 hours on weekdays. If we're a fit, we'll do a 30-minute scoping call.
Get in touch
Book a 30-minute call, or email me directly.