Krista Taylor

AI → Production

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AI → PRODUCTION · INDEPENDENT · 2026

Building a faster path from product strategy to production.

Building a faster path from product strategy to production.

An evolving AI-assisted workflow connecting product framing, prototype exploration, and front-end implementation—without outsourcing product judgment.

An evolving AI-assisted workflow connecting product framing, prototype exploration, and front-end implementation—without outsourcing product judgment.

COMPANY
Independent

YEAR
2026

ROLE
Product Designer / Builder

FOCUS
0→1 · AI-assisted delivery

AMBIGUOUS INPUT → PRODUCT DEFINITION
→ PROTOTYPE → AI BUILD → WORKING PRODUCT

AMBIGUOUS INPUT → PRODUCT DEFINITION
→ PROTOTYPE → AI BUILD → WORKING PRODUCT

01 / Overview
The transformation

01 / Overview
The transformation

The experiment asks a practical question: how much translation can be removed between product thinking and working software while preserving strong design judgment and strategic intent?

02 / Scale
What made it complex

02 / Scale
What made it complex

The opportunity spans requirements, product framing, interaction design, prototyping, code, and production constraints. The workflow is intentionally designed as a connected operating model rather than a collection of AI tools.

0→1

NEW WORKFLOW

AI

CLAUDE + KIRO

Prototype

→ PRODUCTION

03 / Decisions · Pivotal design moves

03 / Decisions · Pivotal design moves

01

Use AI to compress translation, not thinking

Keep product framing and judgment human-led while using AI to accelerate execution and exploration.

02

Prototype the hard part early

Move quickly into working interaction so product questions surface before polished design investment.

03

Keep the path to production visible

Design prototypes with implementation constraints in view so the artifact can move closer to build.

04 / System
From parts to a product system

04 / System
From parts to a product system

The workflow connects strategy, requirements, prototype, and code in a tighter loop. Each stage produces an artifact that is useful to the next rather than requiring a full translation reset.

Manual workflows
Fragmented tools
Operational handoffs

AI-assisted product loop

Reusable capabilities
Shared patterns
Scalable delivery

05 / Impact
What changed

05 / Impact
What changed

This case study is less about a single shipped product and more about demonstrating a modern product-design operating model: broader exploration, faster iteration, and less loss between intent and implementation.

Faster
EXPLORATION

Closer
DESIGN ↔ BUILD

More
WORKING PROTOTYPES