We have spent our careers building, securing, and running real systems, and we still do. This firm exists to share what we have learned with organizations figuring out where AI actually fits. We will not overpromise, and we will only put AI where it belongs.
Why we call ourselves practitioners
A practitioner is someone who does the work. That is the whole definition, and it is why we chose the word.
Every member of our team has held an operating role. We have signed off on deployments, owned the outcome when something broke, and made calls that had real consequences. That experience shapes how we work with you.
We care about your system running tomorrow, your engineers being calm about it, and your CFO being unsurprised by the bill.
Who is behind the firm
Alan has spent more than 30 years in software engineering, including 15 years as a CTO for private equity-backed companies. He now focuses on making AI work in production, not in demos.
He has built agentic AI workflows that generate production-ready code, stood up an AI pipeline that processes millions of images a day, and led the build of a full ERP system powered largely by autonomous AI agents. That sits on decades of engineering judgment: growing a two-person team into more than fifty, and leading technical due diligence that changed the outcome of live deals. He is the author of the Amazon bestseller Think Like a CTO and a recognized Java Champion, credentials earned well before AI made this kind of work fashionable.
Ryan has spent over 15 years in cloud and platform engineering. He now brings that same reliability discipline to how AI gets built into production systems.
He has rolled out AI-assisted engineering tools like Cursor and Claude across his development team for day-to-day delivery, reviews, and operations, so his recommendations come from what has actually worked at scale, not theory. As VP of Engineering, he took a 24/7 marketplace handling 200 million daily requests to 99.9% uptime, reduced cloud costs through infrastructure and governance improvements, and personally led a zero-data-loss recovery from a production database failure. That same discipline, protecting uptime, data, and cost, is what decides where AI belongs in a client's stack, and where it does not.
Some context
Selected work from recent engagements. Clients described by stage and industry.
We are deliberately small, and we take on a limited number of engagements each quarter so we can do the work properly. It starts with a free call.
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