AI Prototyping Services

In 2026, a prototype is not a wireframe. It is a working product with real data, real logic and a real URL, built in days, that a customer can use badly enough for you to learn something true.

That change matters more for growth teams than for design teams. The old prototype answered "does this look right?" The new one answers "will people actually complete this flow?" — which is the only question that moves activation.

What AI prototyping means now

A modern prototype has authentication, a database, business logic, a deployed front end and analytics on the critical events. It is not production-hardened: no load testing, no full error-state coverage, no compliance review. But it is real enough to put in front of ten users and watch where they stall.

The toolchain

• Claude and GPT-class models for logic, data modelling, edge cases and the unglamorous work of turning a vague brief into a specification.
• Lovable / v0 for UI generation and iteration — full application scaffolding rather than screens.
• Supabase for data, auth and row-level security, so the prototype has real user accounts and real records from day one.
• Vercel or equivalent edge hosting for deployment, so there's a shareable URL within hours, not a local demo.

The tools change every few months. The pattern doesn't: model-generated logic, generated UI, managed backend, instant deploy.

What fits in 48–72 hours — and what doesn't

Realistic in 48–72 hours: a calculator or quoting tool with real math, a multi-step onboarding or application flow with validation and save-and-resume, a lead qualification system with scoring and routing, an internal dashboard over an existing dataset, a landing page plus functional demo for concept testing.

Needs longer: anything requiring third-party integrations with certification (core banking, credit bureaus, payment rails), regulated data handling with an audit trail, real-time or high-concurrency behavior, native mobile, or migration of meaningful existing data.

Use cases that pay off

• Calculator and quoting tools — rate estimates, savings calculators, eligibility checks. These are activation events in disguise and they rank in search.
• Onboarding and application flows — test the sequence, the field count and the verification placement before engineering builds the real one.
• Lead qualification systems — capture, score and route, with the scoring model visible and tunable.
• Internal dashboards — get an operating team off spreadsheets in a week.

How prototypes feed PLG strategy

This is the part that justifies the spend. Activation design is the highest-leverage work in PLG strategy, and it is normally validated last — after a quarter of engineering has committed to a flow.

Prototyping inverts that. You build the activation flow first, run ten users through it, and find out that the verification step placed at position two loses 40% of them. You move it to position five in the prototype, test again, and only then write the ticket. The prototype becomes the specification, and the specification is already evidence-backed.

The same applies to pricing pages, upgrade prompts and PQL scoring thresholds: cheap to prototype, expensive to guess wrong.

What the engagement looks like

1. Brief — a 60-minute session to define the user, the single decision the prototype must inform, and what "learned something" looks like.
2. Prototype — 48 to 72 hours to a deployed, instrumented, shareable build.
3. Feedback — you and your users work it; drop-off and confusion get logged against specific steps.
4. Iterate — one or two fast cycles, then either a hardening path to production or a documented decision not to build it.

Most engagements end with a working artifact plus a short memo on what the usage data says. Some become the front end of a longer experimentation program.

Have a concept? Let's prototype it.

Bring a flow you're unsure about and we'll have something clickable in a few days. Start here.