Speed Development | Utah Tech Labs

The process

The method, end to end.

Design through deployment with an iteration loop in the middle and a feedback loop around the whole thing. This is the flow every engagement runs on.

Utah Tech Labs AI-assisted development process — from design to deployment with a feedback loop.

Nine stages

What happens at each step.

01

Design & AI-assisted UI/UX

Figma plus an AI design assistant. Layouts, a real design system, component suggestions and accessibility checks before a line of code exists.

02

Design handed to code, not redrawn

A model-context bridge reads the Figma file directly, so the build starts from the actual design tokens and screens instead of a developer's interpretation.

03

Production-ready generation

The coding agent works with full repository context — existing patterns, data models and conventions — and generates code that fits the codebase it lands in.

04

Engineering-driven instructions

Every feature is specified before it is built: behaviour, edge cases, data contracts and explicit success criteria. Ambiguity is what makes projects slip.

05

Prompt engineering standards

A house standard for how we instruct models — right stack usage, low token waste, easy migration, maintainable and secure output.

06

The iteration loop

Generate, review, validate, test locally, refine the instruction, repeat until optimal. Design updates feed back to stage one when the loop finds a gap.

07

Deployment on AWS / GCP

CI/CD pipelines, infrastructure as code, auto-scaling, logging and monitoring — set up as part of the build, not bolted on before launch.

08

Monitor & feedback

Application performance, error tracking, user analytics and business metrics instrumented from day one so decisions come from data.

09

Continuous improvement

Insights are prioritised into the backlog, prompts and instructions are refined, and the next cycle starts with a smarter system than the last.

The timeline

Three months, in the open.

Environments go live early and stay live. You see the application working every week instead of waiting for a reveal at the end.

Weeks 1–2

Discovery & design system

Scope, data model, architecture, and the UI system in Figma with accessibility built in. Success criteria written down.
Weeks 3–8

Build in weekly increments

Feature-by-feature generation, review and local test cycles. Environments live from week three, so stakeholders use it while it is built.
Weeks 9–11

QA, hardening, deployment

Full regression, performance and security passes. CI/CD, auto-scaling and monitoring finished on the production account.
Week 12

Launch & feedback loop

Go live with analytics and error tracking active, and the first improvement cycle already scoped from real usage.

Key benefits

What the method buys you.

Faster development

Weeks of scaffolding, boilerplate and glue code collapse into days of directed generation and review.

Better code quality

Specification-first work plus mandatory human review catches defects before QA ever sees them.

Lower cost

A smaller senior team covers the ground a large mixed team used to, without the coordination overhead.

Easy migration

Standard stacks, no proprietary lock-in, and infrastructure defined in code you keep.

Scalable & maintainable

Design systems, typed contracts and documented conventions mean the second year costs less than the first.

Visible progress

Working software in the environment every week, not a status deck describing one.

Scope

Everything in one engagement.

No separate QA vendor, no infrastructure contractor, no handoff gap between design and build. One accountable team from first workshop to production.

Architecture & data modelling
Design system in Figma
Full application build
Automated + manual QA
CI/CD pipelines
AWS / GCP infrastructure
Monitoring & error tracking
Analytics instrumentation
Documentation & handover

Data

Models

Build & Run

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Large applications, live in three months. Deployment and QA included.