The model is the easy part. What actually breaks AI projects is the layer underneath it. I focus on wiring agents into enterprise data, identity, security, and governance so they survive production.
The model proposes. Deterministic logic or a person decides.
Three engagements that show the range: platform architecture, business-process automation, and governed access to enterprise data. Every entry states its status.
A deterministic-first procurement automation platform for a precious metals retailer, automating reorder decisions across four metals and five sales channels with AI constrained strictly to an advisory role.
Outcome In production since July 2026, run every Monday across about 21,000 products and operated by the customer.
Read the case →An MLOps and LLMOps platform for a Fortune 500 automotive aftermarket retailer, originated with a working demo, won in April 2025, approved by the architecture review board, and carrying 9 of 27 models in production by September 2026.
Outcome Measured: as of September 2026, 9 of the 27 models were live on the platform, with the rest on a roadmap.
Read the case →A five-zone enterprise agentic-AI platform that lets non-technical business teams self-serve AI-generated insights from governed enterprise data across six business domains.
Outcome Adoption roadmap accepted across six business domains.
Read the case →For CIOs, architects, and platform leads deciding how far to push agents and what has to exist first. Five levels from shadow experiments to an agentic enterprise, four questions that place an organization in minutes, and a reference agent drawn at every level on AWS, Google Cloud, and Azure.
Authored by Sujith K. Surendran. An explanatory model, not a certified assessment instrument. Updated October 2026.
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Independent, unsponsored picks. My 3 highest-conviction right now, with a one-line reason each.
Role discussions, architecture conversations, speaking, or a second opinion on a design.
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