FynloAI — Turning an idea into an investor-ready product in five days.
/Timeline
5 Days
/Platform
AI-enabled Product
/Outcome
Investor-ready Prototype
FynloAI started as an early-stage product idea with little more than a vision. The challenge wasn't simply designing an interface. It was rapidly shaping the proposition, validating the product direction and creating something tangible that could be shared with investors and stakeholders.
Working alongside AI-powered tools throughout the process, we moved from concept to a functional prototype in five days without compromising the quality of product thinking behind the decisions.
The FynloAI prototype — a functional, investor-ready fleet emissions management product built in five days.
Early-stage products often spend weeks discussing ideas before anything exists for people to react to.
Without something tangible, conversations remain theoretical, assumptions go unchallenged and product decisions become difficult to validate.
The challenge wasn't simply working faster. It was creating enough confidence around the product direction to move investment conversations forward.
Early-stage product concept framing the core proposition before any interface work began.
Turning an idea into a tangible product.
Validating the core proposition early.
Accelerating stakeholder and investor conversations.
Demonstrating how AI could support product discovery without replacing critical thinking.
AI should accelerate product thinking, not replace it.
Before exploring screens, the focus was on understanding the problem, defining the audience and shaping the product proposition.
AI accelerated documentation, exploration and iteration, but the product direction remained grounded in customer needs and business objectives.
The Subframe component library established during the sprint — brand tokens and custom components that enabled consistent, rapid interface iteration.
Rather than waiting until requirements were complete, AI-enabled workflows allowed concepts to be explored in parallel with product conversations.
Ideas became interactive prototypes within hours, making it easier for stakeholders to react, challenge assumptions and refine the direction.
Subframe's conversational AI generating interface variants in real time — describing what you need and seeing it built, without leaving the prototype.
The AI assistant at the core of the product — a conversational entry point that lets fleet managers surface insights and get answers without navigating a dashboard.
Natural language in, fleet intelligence out — asking a plain question and getting a structured vehicle summary, without a search or filter in sight.
AI wasn't used to design the product independently. It supported research, content generation, prototyping and implementation, allowing more time to focus on product strategy, user experience and decision making.
One of the key tooling decisions was using Subframe rather than Figma. In a five-day sprint, the gap between a design file and a working interface is a real constraint. Subframe's design-to-code approach meant components could be built once and immediately used in a functional prototype — removing the handoff step entirely and keeping the product moving.
The result was a faster workflow without losing the human judgement behind the product.
Subframe outputs production-ready React code alongside the design — giving developers something to build from directly, without a Figma handoff in between.
A clear product concept that could be understood, explored and validated through an interactive prototype.
An investor-ready proposition that accelerated conversations and reduced the time between idea and demonstration.
Established an AI-enabled workflow that significantly shortened product discovery while keeping design and product thinking at the centre of the process.
A key user flow from the finished prototype — fleet emissions offsetting with AI-recommended partner contributions.
This project changed the way product design with AI can work. The biggest opportunity isn't using AI to generate interfaces. It's using it to remove the delay between thinking, making and learning.
The faster teams can test ideas, the sooner they can make better product decisions.
AI doesn't replace product thinking. It creates more opportunities to apply it.