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Requirements synthesis.
The fuzzy-to-concrete translation is where most teams bleed weeks.
AI checks that technical specs match product intent before anything gets built.
I help software product teams use AI to ship faster and build better.
Embedded technical leadership for software and product teams. Startups moving fast. Established companies navigating the AI shift.
16+ years shippingCo-founded NextGen Kitchens ($1M/mo)Production AI: RAG · Recommendations · Forecasting · Agent systemsBased in Vancouver
Most of the people I work with arrive through someone they trust. If that's how you got here — welcome.
The Shift
Most software teams feel like AI should be helping more than it is.
Your team has ChatGPT subscriptions, maybe a Cursor license or two, probably a Copilot seat. Someone read the Anthropic economic index memo and someone else watched a keynote. There's a Slack channel called #ai where people share links. And yet — your delivery cycle hasn't meaningfully changed. Requirements still take too long. Specs still drift. Testing is still mostly manual. Alignment between product and engineering still leaks hours every week.
This isn't a you problem. Most teams are here. The tools are real, the hype is real, and the gap between 'we use AI' and 'AI actually compresses our delivery cycle' is also real. Closing that gap is less about picking the right tool and more about rewiring how your team moves from idea to shipped feature — which parts of the cycle AI makes faster, which parts it quietly makes worse, and where to start.
I've done this work on my own team and with others. The pattern is consistent enough that I wanted to write it down.
What Works
Seven places AI actually compresses the SDLC. And three where it doesn't.
What follows is the short version of what I've seen work across my own team and the teams I've advised over the last two years. If you only read one section on this page, read this one. If you want to talk after, the form at the bottom is open. If you don't — take what's useful, leave the rest, and ship something good.
And three where it doesn't.
That's the short version. If your team is somewhere in here, the form at the bottom of the page is the easiest way to talk.
Ways to Work
Three shapes an engagement usually takes.
The right shape depends on where your team is and what you're carrying. Most engagements start as one of the three below and sometimes move between them as things change. Happy to get specific once I understand what you're working on.
Fractional CTO— for teams in the middle of something real.
What you get
Senior technical leader inside your team — owning the engineering function, running the decisions, and shipping alongside you — without the full-time hire.
Embedded technical leadership, part-time, ongoing. I'm in your standups, your PRs, your architecture calls, and your hiring loops. The engagement runs weeks to months depending on what you're shipping and how much momentum you need me carrying.
In practice this looks like: 2–3 sync meetings a week, active sprint participation, code reviews with real feedback, and a monthly sync with your leadership on roadmap health. I work in your tools — Linear, Jira, GitHub, Slack, Notion — and adapt to your cadence rather than importing mine.
Sound like you? That's probably the right shape. Send me what you're working on →
Strategic Advisor— for teams that have the hands but want a second brain.
What you get
Ongoing access to a senior technical partner who is genuinely invested in your decisions — for moments where getting it wrong is expensive.
Lighter touch. A 1-hour strategy sync per month, async availability for the calls that matter, and a standing invitation to pull me in when something gets stuck. You make the decisions and execute; I think with you on the ones that are worth thinking twice about.
This works best when your senior people are strong but there's nobody at the CTO layer to pressure-test architectural direction, hiring calls, or the hard technical trade-offs. I'm a sounding board with skin in your success, not a consultant with a quarterly check-in.
Sound like you? Let's talk about what that would look like. Send me a note →
The Diagnostic— for teams that want clarity before commitment.
What you get
A clear-eyed, documented read on where your tech and team actually stand — and a concrete set of actions you can execute whether or not you work with me again.
A focused two-to-three week engagement. I come in, learn how your team actually works — how decisions get made, where work stalls, what your people spend time on. I map where AI has real leverage in your specific context and where it's noise. You leave with a prioritized plan your team can run on its own.
This is often the right first step when the shape of ongoing engagement isn't clear yet. Some teams take the plan and execute it themselves. Others bring me back once priorities are set.
The output is the same regardless of industry: concrete bets, ranked by impact, with the low-ROI distractions called out explicitly.
Sound like you? This is usually how we'd start. Send me a note →
Case Study
How we cut our delivery cycle from 6 weeks to 2.5 at NextGen.
I co-founded NextGen Kitchens in late 2022 and held CTO + acting CPO for three-plus years. We are processing $1M/month in transactions across six locations including food halls, airports, and malls. What follows is one specific piece of that story — how my team shipped a major product iteration in 2.5 weeks using the framework I now bring into other engagements. I'm including what didn't work because that part is actually more useful than the wins.
The framework isn't magic and it doesn't map identically onto every team. The first thing I do in an engagement is figure out what version of this fits your stack, your team, and what you're trying to ship. Sometimes the answer is 'most of it,' sometimes it's 'one piece of this, hard.' The point is the honesty about what actually compresses the cycle versus what just looks good in a slide.
About
Where the curiosity came from.
I was born curious. I talked my dad into teaching me QBASIC when I was eleven, spent that year writing tiny games and passing floppy disks around the schoolyard, and drew blueprints for a robot that was going to help my mom with her cooking — she cooked a lot, and I was convinced the future would fix it. The robot never shipped. The habit did.
The CV version of the sixteen years since is: full-stack engineer out of school learning every layer I could get my hands on (frontend, backend, databases, networking, devops), consulting for smaller and mid-sized teams, a couple of startups of my own that taught me more than they paid, and co-founding NextGen Kitchens in late 2022 — CTO and acting CPO for three-plus years, zero to a million a month in transactions, production AI across recommendations, forecasting, RAG, and agent orchestration. Accurate. Also somewhat beside the point.
What I actually care about is the overlap between business and tech — how a product with the right instincts can quietly outcompete a company ten times its size. That's what played out at NextGen. Our customers had better-resourced options and chose us anyway, because we were more curious about what they actually needed and more willing to let the product reflect that. That lesson is what I bring into other teams now: curiosity about the user, honesty about what's working, and the patience to build the thing that fits instead of the thing that's in the slide.
Outside the work, I'm a dad and a husband. I live in Vancouver which is as beautiful as people say, and I met my wife backpacking in Southeast Asia — which explains a lot about how I travel and not much about how I work. I love what tech can do and I worry about what it's doing to how we relate to each other and to the natural world. I'm not sure how to hold both of those at once. I suspect the answer has something to do with building the next wave a little more carefully than the last one.
Most of my time right now is fractional and advisory. I'm also open to senior technical leadership roles for the right team — if that's you, write to me directly. If we end up working together, you'll find I care about your team and your users in a way that's probably slightly inefficient from a consulting standpoint. That's the point.
Recent Thinking
What I've been writing about lately.
last week
The model is the smallest lever
Most teams burning runway on AI have tuned the model and left everything else on defaults. Model choice is one lever — context, memory, and rules are the ones doing more of the work.
Read on LinkedIn
about four months ago
From MVP to Production: What Nobody Tells You About Year One
A year-one CTO retrospective. Foundation first, simple beats clever, observability as an investment, AI as a multiplier, and why vendor choice and delegation matter more than the code you write.
Read on LinkedIn
about four months ago
A Christmas Eve reflection — parenting, presence, and building environments where falling doesn't break everything
Christmas Eve, thinking about the parallel between parenting and CTO work: your job is to be present, and to build environments where falling doesn't break everything.
Read on LinkedIn
Community
A quiet project I care about.
Alongside the client work, I'm slowly building something closer to a community than a customer list. Product folks and engineering leads in Vancouver and remote — people working through the same AI shift on their own teams and wanting someone to think alongside. Not a course, not a paid cohort. Occasional conversations, an email list that's more digest than newsletter, and the odd in-person thing in Vancouver when it makes sense.
If that sounds useful, the email form at the bottom of this page is also how you get on the list. Low volume. No pitches. If we work together one day that'd be great; if we don't, I still want to help the people figuring this out to not have to figure it out alone.
Contact
Send me what you're stuck on.
One line or a paragraph, either works. I read every message personally and reply within a couple of days.