What we learn building production AI for startups and enterprises. Agent design, evals, voice, infra, and the messy details between a demo and a live system.
A practical decision guide for choosing between hiring an internal AI engineer, using a senior AI consultancy, or combining both.
Read Should you hire an AI engineer or use an AI consultancy?A practical AI ROI model for production systems: count time reclaimed, error reduction, risk, adoption, and the engineering work needed to keep the system reliable.
Read How do you calculate AI ROI for a production system?An eval harness pays back by catching regressions before release, shortening model-change cycles, and giving teams evidence instead of vibes.
Read What is the ROI of an AI eval harness?RAG ROI is strongest when high-value teams repeatedly search trusted internal knowledge and need cited answers faster than a human can assemble them.
Read When does RAG or internal search produce ROI?Voice-agent ROI comes from answered calls, qualified leads, cleaner handoffs, and fewer repetitive operator interruptions, not from replacing every human conversation.
Read How do voice agents earn ROI in real operations?Notes on AI-assisted development, rendering patterns, and what changes in web engineering when half the work is being done by agents.
Read The Future of Web Development: Our Predictions for 2023A short retrospective on what changed when a remote team moved back to a shared room, and which of those things were worth the move.
Read 3 Lessons We Learned Going Back to the OfficeNaming components is half the job. A short take on patterns that hold up as a codebase grows, and the shortcuts that look smart and bite you later.
Read A Short Guide to Component NamingA principal engineer reads every inbound. We reply same day on weekdays, with an honest read of whether we’re the right team for the work.