Building production AI systems that work
when stakes are real.
Head of Engineering & Product with 28 years delivering AI/ML platforms across Fortune 100 companies. Inventor of the G-ARVIS framework G-ARVIS 93.9% ↗ pip install argus-ai ↗. Founder of Ambharii Labs — building production-grade AI products and agentic systems for enterprises.
I started my AI career at ISRO, training neural networks before transformers existed. What has stayed constant across three decades is a belief that AI systems are only as valuable as their reliability in production — not their performance on a benchmark.
Across Tech, Techstartups, Healthcare, Lifesciences, Energy & Utilities, Insurance, Banking, and Fintech I have led platform transformations that generated over $4B in measurable business outcomes. The work I am most proud of is not the technology — it is the trust that business stakeholders placed in AI systems I built, because those systems told the truth about uncertainty when it mattered.
I co-founded the CAIO Circle Tri-State Chapter to build the executive AI leadership community that I wished had existed when I was navigating these decisions alone. I publish and speak to share what the journey actually looks like from inside enterprise AI — not the demo, the production.
Production-grade AI platforms built from real requirements at Ambharii Labs. No demos. No prototypes.
11 Autonomous AI Agents powering denial prevention and revenue recovery for healthcare revenue cycle management. Built with G-ARVIS Observability and ARGUS Self-Correction to predict claim denials before submission and optimize A/R workflows at scale.
Enterprise LLM observability and scoring platform. Monitors six dimensions of production LLM health — Groundedness, Accuracy, Reliability, Variance, Inference Cost, and Safety — to ensure AI systems perform when stakes are real.
Enterprise AI readiness and assessment platform. Computes DQ Score, ROI projections, and organizational readiness evaluations to help companies make informed decisions before investing in AI implementations.
Enterprise MLOps framework for production model lifecycle management. Covers experiment tracking, model registry, automated evaluation pipelines, drift detection, and governance dashboards built for regulated industries.
Enterprise AI perception platform combining computer vision, NLP, and multimodal document understanding. Powers intelligent document processing, anomaly detection, and real-time data extraction at scale across unstructured enterprise data.
Agentic genomics intelligence platform purpose-built for healthcare and life sciences. Brings AI-driven intelligence to genomics workflows — from variant interpretation to clinical decision support — designed for regulated environments where accuracy is non-negotiable.
Risk assessment, compliance, fraud detection, and trading automation — agentic AI for financial services.
Six dimensions of production LLM health — distilled from building AI systems that govern billions in capital decisions across regulated industries.
Read the Full Article →From data infrastructure to model deployment to business translation — the complete stack of skills required to ship AI that actually works in production.
Why benchmark scores are a distraction — and the 8 measurements that will make or break your AI system when real money is on the line. Introduces the G-ARVIS framework for production LLM observability.
Single-turn accuracy metrics break down when your LLM is taking multi-step actions. What needs to change in your observability stack before you ship agentic workflows to production.
What the CloudmedAI integration at R1 RCM taught me about AI platform architecture, technical due diligence, and the engineering decisions that make or break an acquisition thesis.
Most RAG failures I have diagnosed in production have nothing to do with the retrieval algorithm. They are groundedness failures, chunking failures, and context window management failures in disguise.
Open to conversations about engineering leadership, AI platform architecture, speaking engagements, and advisory roles at companies building AI that has to work when stakes are real.