Vendor Evaluation Framework for AI/ML Platforms
Most vendor evaluations for AI/ML platforms are theater. You run a demo on clean data, get impressive benchmark numbers, and then spend six months post-procurement discovering all the things that don't work. Here's how to run an evaluation that actually predicts production performance.
AWS HealthLake vs. Google Cloud Healthcare API: Building Healthcare Data Platforms
If you're building a healthcare data platform today, you're almost certainly choosing between AWS and Google Cloud as your foundation. Both
Understanding AI Inference Costs at Scale
At low volumes, AI inference costs are invisible. At scale, they become the biggest line item in your infrastructure budget. The cost levers — token economics, batching, caching, distillation — can reduce costs by 80% without sacrificing quality.
From Enterprise PM to AI PM: A Career Transition Guide
When I transitioned from engagement management at HCLTech into an AI product role, I did not have a computer science degree, I had never trained
AI Product Roadmap Prioritization: Beyond RICE
RICE works fine when you know what you're building. AI products break that assumption constantly. Here's how I've adapted roadmap prioritization for the realities of probabilistic outputs, model drift, and shifting accuracy benchmarks.
Transformer Architectures Explained for Product People
Transformers are the engine inside every large language model you're building on. Understanding how they actually work changes how you scope features, set expectations, and make architectural decisions.
Agentic AI Explained: What It Is, Why It Matters, and How to Build With It
If 2023 was the year everyone discovered LLMs could write surprisingly good text, and 2024 was the year enterprises started asking "but how do
Claude vs GPT-4 vs Gemini: Enterprise AI Platform Showdown
I've spent the last two years building AI products on top of Claude, GPT-4, and Gemini — across healthcare, edtech, and enterprise technology. Here's what I actually learned about when each one wins.
AWS Bedrock vs Azure OpenAI vs Google Vertex AI
If you're an enterprise AI product leader, this is the decision that shapes your entire AI infrastructure for the next 3-5 years. I've evaluated all three platforms for production deployments and I'll give you the framework for making this call — not the one that wins on feature checklists, but the
LLM Evaluation Frameworks: Beyond Benchmarks
A model that scores 90% on MMLU can fail 40% of your production queries. Benchmark performance and real-world performance are weakly correlated. Here is how to build evaluations that actually tell you whether your system is working.
Building in Public: How I Automated My Entire Content Pipeline with AI Agents
I built a system of AI agents that handles ideation, scripting, editing, and multi-platform publishing. Here's the architecture and what I learned.
How I Use AI Agents to Run My Entire Content Operation
I'm an AI product manager who got tired of the gap between knowing how to build AI systems and actually using them in my own work. Here's how I built Jarvis — an agentic content operation that handles drafting, publishing, and distribution while I focus on ideas and strategy.
Fine-Tuning vs Prompt Engineering: A Decision Framework
I spent six weeks at Edxcare building a fine-tuned model for a problem that turned out to be solvable in three days with better prompt engineering. This framework is designed to prevent that mistake.
First Principles of Data Strategy
Most companies have a data infrastructure strategy but not a data strategy. These are different things. Infrastructure is about storing and querying data. Strategy is about building data assets that compound over time and create durable competitive advantage.
AI in Legal Tech: How LLMs Are Automating Contract Review
Legal technology has had false dawns before. E-discovery software in the 2000s was supposed to eliminate the document review associate. Contract management platforms in the