Your AI Demo Worked. Your Production System Will Not. Here Is Why.
80% of enterprise AI projects fail after the demo. The problem is never the model—here's the five-pillar framework that actually closes the gap.
Read on MediumAI, backends, integrations—reliability first.
Not just the demo.
Agents, backends, integrations.
Ambiguity into systems.
From robotics data infrastructure to AI hiring—systems I've founded or helped build from the ground up.
Founder
The standardization & trust layer for robotics AI data.
Turns raw, multi-source robot recordings into validated, schema-conformant, train-ready datasets—so data suppliers sell faster and model teams train without custom cleanup.
Founding AI Engineer
AI interviews that surface hire-ready candidates.
Conversational AI video interviews tailored to each role—assessing real skills and authenticity, then scoring every candidate to deliver a justified, hire-ready shortlist.
Founding Member
Production AI systems for enterprises.
Fine-tuned LLMs, RAG pipelines, multi-agent workflows, and MLOps/governance infrastructure built to survive real enterprise constraints.
Founder · Side project
A consumer event aggregator with an AI concierge.
Surfaces what's actually worth your time—cutting through the noise of scattered event listings with an AI concierge that curates for you.
A few end-to-end systems shipped to production—built for reliability, not demos.
Reduced review time from minutes to seconds with agent-assisted triage.
In production. Used daily. Evaluated continuously.
View project detailsLet agents research, qualify, and route leads without manual follow-up.
In production. Used daily. Evaluated continuously.
View project detailsAnswer complex documents with traceable, production-grade retrieval.
In production. Used daily. Evaluated continuously.
See architectureIntelligent systems that solve real business problems.
Multi-agent systems, RAG, and intelligent automation.
End-to-end AI products from concept to deployment.
A sophisticated AI system that automates lead discovery and qualification across 12+ data sources, delivering 3x more qualified prospects with 80% reduction in manual research time.
Notes from building systems that didn’t work the first time.
80% of enterprise AI projects fail after the demo. The problem is never the model—here's the five-pillar framework that actually closes the gap.
Read on MediumQuantization, caching, and distillation aren’t three research ideas—they’re one composable stack. Together they just hit real-time diffusion.
Read on MediumA frontier planner, a swarm of cheap workers, and a 10x cost reduction—the first production-ready multi-agent architecture that holds up.
Read on Medium