All field notes

Most AI MVPs fail by trying to do too much. How to scope, build, and validate an AI MVP that proves real value in weeks—and can actually reach production.

Building an AI chatbot is easy; building one that's accurate and useful is not. The steps that matter—grounding, guardrails, evaluation, and integration.

RAG is the standard way to build reliable LLM apps—and the most commonly botched. The steps that matter, and why retrieval quality decides everything.

AI agents can take actions, not just answer—which makes reliability and control essential. The steps to build an agent that's useful and safe, not a liability.

Recommendation systems are among the highest-ROI AI—when built right. The approaches, the data you need, and how to measure whether it actually lifts sales.

AI SaaS is booming—and most products have no moat. How to build one that's reliable, cost-controlled, and defensible beyond a wrapper on someone's API.

Document processing is one of the highest-ROI AI use cases. How to build a system that extracts and understands documents reliably—and how to measure accuracy.

Calling an LLM API is trivial; building a reliable application on top is not. The engineering that turns a demo into a dependable production LLM app.

Computer vision demos beautifully and fails on real images. How to build a vision system that survives lighting, angles, and edge cases in production.

A predictive model only matters if it changes a decision. How to build one that's accurate, honestly validated, and actually used—not a dashboard nobody opens.

Exposing AI through an API adds hard problems: latency, cost, versioning, reliability. How to build an AI API that other systems can depend on.

Adding AI to an existing product isn't a rewrite. How to pick the right first feature, integrate without breaking things, and ship something users value.

Internal AI tools are often the fastest ROI—less risk than customer-facing AI. How to build one your team adopts, grounded in your data and workflows.

Voice assistants add latency and audio challenges on top of LLM reliability. How to build one that understands, answers accurately, and responds fast enough.

Most AI projects fail on data, not models. How to build a reliable data pipeline that feeds your AI quality inputs—the foundation nobody wants to fund.

AI chatbot cost ranges widely because scope does. The real cost drivers—grounding, integration, evaluation, and inference—and how to scope for ROI.

A RAG system's cost isn't the LLM—it's retrieval quality, data, and evaluation. The real cost drivers, and how to budget for a system that gives right answers.

Computer vision cost is dominated by data and annotation, not the model. The real cost drivers, and why field testing is the line item you can't skip.

The cost of a machine learning model is mostly data and deployment, not the algorithm. The real drivers, and why 'building the model' is the cheap part.

An AI MVP should be cheap because it's focused. What drives the cost, how to keep it lean, and why over-scoping is the most expensive mistake.

Recommendation systems cost varies with data and integration—but few AI investments have clearer ROI. The cost drivers, and how to budget for a revenue lift.

Generative AI has a cost most teams underestimate: inference. The build, the token economics, and why unit economics—not the demo—decide viability.

The LLM API is the cheapest part of an LLM app. The real costs—grounding, evaluation, integration, and inference at scale—and how to budget for them.

NLP cost depends on whether existing models fit your task or you need custom data work. The drivers, and how to avoid overpaying for language AI.