Practical guides to putting AI in production
What we've learned building and running AI agents, apps and infrastructure, written for the people who fund, build and operate them.
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7 articles shown
Strategy2 min read
AI readiness checklist for mid-size companies
A practical checklist to see whether a workflow is ready for AI: business case, data, systems, security, people and budget. Score yourself before you spend.
Read articleModernization2 min read
How to add AI to WordPress, Drupal or a legacy app without rebuilding it
You don't need a rewrite to use AI. Five proven patterns for adding AI search, assistants and automation to WordPress, Drupal and older custom software, step by step.
Read articleTeams1 min read
Nearshore vs. offshore vs. AI-augmented teams: the 2026 math
Hourly rates are the wrong comparison. How to compare nearshore, offshore and AI-augmented development teams by cost per shipped outcome, with a formula you can use.
Read articleInfrastructure2 min read
Private LLM vs. public AI APIs: what regulated companies should choose
Public AI APIs are fast to start with; private LLMs give you control over data and cost. How to choose, and why most regulated companies end up using both.
Read articleInfrastructure2 min read
RAG explained for business leaders: making AI answer from your own documents
Retrieval-augmented generation lets AI answer from your documents, with sources. How it works in plain language, what makes it good, and the questions to ask before you build.
Read articleCloud security2 min read
The cloud bill that almost killed a startup: 10 guardrails every team needs
Budget alerts don't stop spending. Ten practical guardrails for Google Cloud, AWS and Azure that prevent runaway bills from leaked keys, abuse and AI workloads.
Read articleAI agents2 min read
What is an MCP server and why your business systems need one
The Model Context Protocol gives AI assistants like Claude, ChatGPT and Cursor a standard, permissioned way to use your business systems. What it is and how to build one safely.
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