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    AI Transformation For Legacy Products

    How Master of Code Global rebuilt Zipify’s development process — and brought new products to market in a fraction of the time

    Zipify Apps creates apps for the Shopify ecosystem. Master of Code Global has built Zipify's product suite — including OneClickUpsell — from day one, for over a decade. Along the way, the tooling around that work aged, and so did the habits built around it.

    Rather than wait for that to become a problem, the same team that built and runs these products turned the lens on itself. We started rebuilding how we build and moved to an AI-first way of working.

    As a result, we launched Zipify Labs — a fast-moving team that could experiment freely with new tools and approaches, without a decade of legacy pulling in the other direction. That's where the real trial and error happened — and where the shift became sharp: products that once took months to ship began shipping in about a week.

    From there, we began rolling the same practices back into our established products, including OneClickUpsell.

    Zipify’s Results After Transformation

    Lauryn Snelson Chief Operating Officer at Zipify

    Master of Code Global are strategic partners who understand Shopify’s ecosystem, merchant psychology, and the business side of software. Their ability to blend AI innovation with practical results ensures that every update drives measurable value.

    Challenge

    How do you turn AI from a handful of personal habits into the operating system of a company — without breaking a decade-old product that thousands of users depend on every day?

    Zipify’s flagship product was more than ten years old, with a large and loyal user base. That success came with a cost. Every change carried risk, so the team moved carefully — and slowly. Even routine features could take a month or more to ship.

     

    Zipify had already adopted AI, but mostly on paper. In practice, it came down to the individual. The split was by depth of use: some engineers leveraged AI tools to their advantage, while others struggled to use the same tools to their full potential. Adoption was genuine but uneven, and the real gap was proficiency. The rollout looked complete, but it didn’t produce results.

     

    When we analyzed the situation, we understood that closing the proficiency gap across the team would be a big part of the work. We also saw that new ideas paid the highest price and hypotheses took too long to test. A new product could sit in development for three months before it ever met a customer. In a market where speed decides winners, that gap was expensive.

    How We Guided Zipify’s AI Transformation

    We ran the transformation as two connected workstreams — one for the whole organization, one for a single fast-moving team.

    1. A company-wide development upgrade

    First, we mapped how Zipify actually built software. Then we helped the team trade its legacy workflow for a modern, AI-first one.

    • One standard toolset. We replaced “everyone uses whatever they like” with a single, secure AI setup across the team — shared configurations, custom agents, and skills tuned to Zipify’s own product.
    • Spec-driven development. We broke big tasks into clear, reviewable stages. A person signs off on each stage before the next begins. That keeps the AI from wandering off course — and leaves behind documentation the team reuses on the next feature.
    • Tech debt, finally paid down. AI tooling helped the team handle routine tasks that used to eat up real time, like updating dependencies, refactoring, and cleanup. This work competes with feature development, and the team was able to reclaim the time to develop features clients asked for.
    • Support handled where it’s cheapest. We built a knowledge base trained on historical tickets, so the non-technical team can now work through technical issues themselves. This allowed them to resolve the majority of issues without escalation, resulting in fewer interruptions for engineers, which is part of why feature work moves faster, too.
    • A shared baseline. We brought every engineer up to the same level, so AI stopped being a personal habit and became the default way to work.
    • Beyond code. We applied the same approach to market research, QA test automation, and design.

    2. A fast, iterative product team

    Second, we helped Zipify stand up something it didn’t have before: a small, senior team built purely for speed.

    We gave it a simple loop:

    • Start with a hypothesis.
    • Shape the interface with Claude Design.
    • Build it with Claude Code.
    • Put a person in the loop to review the result before anything launches.

    The rule was test first, build later. A quick prototype proves the idea; only then does it grow into a product.

    The result felt different day to day. The team had fewer meetings, tighter loops, and every morning standup ended up with something real on screen, not just an update.

    Key Achievements:

    • Faster, more reliable prototype-to-launch cycle
    • One standardized, secure AI toolset across the team
    • Legacy product development is empowered by AI
    • Long-shelved features are achievable
    • AI adoption extended beyond code into research, QA, and design
    • A dedicated fast-iteration team built for speed

    Your Business Vision Meets Technology Mastery Now

    Want to discuss your project or digital solution?
    Fill out the form below and we’ll be in touch within 24 hours.








      How did you find us?











      By continuing, you're agreeing to the Master of Code
      Terms of Use and
      Privacy Policy and Google’s
      Terms and
      Privacy Policy