Prototyping at Zyte was slow and centralised. Ideas from product, marketing and design all funnelled through the few people who could build something clickable.

Good ideas died waiting. Stakeholder conversations happened around static mockups when a working prototype would have answered the question in minutes.

The obvious fix was "prototype faster." I didn't build that.

Making myself faster would still leave the bottleneck in place: me. The real problem was that the capability to prototype belonged to too few people. So I reframed it: what would it take for anyone on the team, designer, PM or marketer, to go from idea to a live, on-brand, shareable prototype without waiting on an engineer?

That reframe turned a workflow problem into a platform problem, with four requirements:

• Gen
  Generation

  AI-assisted building so non-developers create real Next.js prototypes, not mockups.
• Brand
  Consistency

  Every prototype on-brand by default, presentable without polish passes.
• Ship
  Deployment

  Zero-friction publishing, so "it works on my machine" never blocks a stakeholder review.
• Find
  Discoverability

  One place to see everything live: shared organisational memory, not dead links in Slack.

A prototype-to-production platform. Push a Next.js prototype to GitHub and it auto-builds into a container, live at its own subdomain in minutes.

A central dashboard lists every live prototype and regenerates on each deploy. Under the hood: Docker, Traefik for routing, a webhook-driven deploy pipeline, and Claude Code integration for AI-assisted generation.

I designed it, built it, and initially ran it at my own cost to prove the concept before asking the business to fund it. Once it demonstrated value, I onboarded designers and stakeholders across the team and wrote the migration runbook to hand the infrastructure over properly.

This is where "AI native" means judgment, not enthusiasm.

• AI
  AI where it multiplies, code where it matters

  AI generates prototypes; deterministic infrastructure deploys them. No LLM in the deploy path, because deployment needs to be boring and repeatable, not clever.
• Cost
  Cost as a design constraint

  API spend budgeted and monitored per use. When I pitched formal funding, I brought a monthly cost model, not a vibe.
• Build
  Buy vs build

  Off-the-shelf tools trap artefacts inside someone else's product. Building meant every prototype was real code on our own infrastructure, one step from production.
• Own
  Maintainability over magic

  Documented to the standard that an engineer who has never seen it can rebuild it. A system only I can run isn't a system, it's a dependency.

The value isn't the prototypes. It's what they cost to be wrong. When testing an idea takes a weekend instead of a sprint, you can afford to test the risky version, and decisions that used to be settled by seniority get settled by evidence instead. That matters most on the work tied to our two commercial numbers, visitor to paid subscriber and visitor to data service enquiry, where the platform is how a conversion idea gets validated before anyone commits build time to it.

Designers and stakeholders now prototype independently, and prototypes became the default way to have product conversations: working software instead of static decks. The platform was formally adopted and migrated onto company infrastructure, and the concept was validated externally when Linear announced a near-identical idea months later, with one difference: Protozyte runs on our own infrastructure and can point at anything we control.