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Services

Software, AI, and MLOps — built for individuals, small businesses, and startups.

Short, focused engagements. One to four weeks. Based in Vancouver, working remotely with clients across Canada and beyond.

Who this is for

No prerequisites. I've taken on engagements with individuals who have nothing but an idea, small businesses with no technical team, and startups that already ship code. What matters is that you've got a defined problem — or enough of one to have a conversation about.

What I do

POC and MVP development

Turn an idea, proposal, or research project into a working version you can show, test, or ship. Most are one to four weeks. Typical stack: Python, FastAPI or Flask, React or Flutter for UI, GCP or AWS for hosting. I build with deployment in mind from day one.

AI adoption for teams and businesses

For businesses and teams exploring where AI, automation, or LLMs could help. I start with a short audit — what you do, where the manual or error-prone work lives, where AI is actually useful and where it isn't. You leave with a prioritized plan and, if you want, a working prototype of the highest-leverage piece.

Includes agentic AI work — systems that use models and tools together to automate multi-step workflows. Increasingly the highest-leverage shape for small business AI adoption.

MLOps and research-to-production

For teams with working research code that needs to become a reliable service: containerization, CI/CD, model serving, monitoring, and the structural work that makes an ML system maintainable. Typical engagement: two to four weeks of focused setup plus a handoff.

Architecture and best-practices review

For teams that suspect their stack, tooling, or process is slowing them down. I review code, tooling, and development process; identify the bottlenecks; and deliver a written report with a prioritized next-step list. Delivered as a document, not a consulting deck.

Also covers software and ML system design, and advising non-technical founders on structuring and hiring into a technical team.

Short, defined engagements

Most engagements run one to four weeks. Even for startups and tech-savvy teams, keeping scope tight is a feature, not a limitation: it forces the problem to be clearly defined, the deliverable to be measurable, and the project to actually ship. If the work is larger, it's broken into sequenced sprints with a decision point between each.

If you want something open-ended, ongoing, or part-time, I'm not the right fit.

Current industry experience

I bring active experience from the techbio space — hands-on with the kind of ML and MLOps work that's actually in production right now.

Start where you are

Most of the time, people already have enough — enough data, enough process, enough pain points — to start somewhere useful. You don't need a finished plan. Figuring out what to do is part of what I help with.

How an engagement runs

  1. Conversation. Thirty to forty-five minutes. What you're trying to do, what you've got, what success looks like.
  2. Audit and plan. I review what exists and write a short proposal — scope, deliverables, timeline, cost.
  3. Build or advise. The focused work. Weekly check-ins or async updates, whichever you prefer.
  4. Handoff. Final report, code, and setup documentation — what you have, how it works, how to run it.

Start with a conversation.

Arrive with a problem, not a brief. The worst outcome is a short, honest conversation.

Tell me what you're building