Velocity AI

Velocity is an agentic AI platform that connects critical data points across the product development lifecycle to accelerate hardware product development twice as fast at half the cost. Winner of the 2025 Kearney Global Innovators Day pitch award, Velocity was created by my global team of product developers, software engineers, UX designers, data scientists, and AI experts.

Situation

Product-centric companies are slow to market and lighting R&D funds on fire developing products that consistently miss the mark. On average, it takes a brand 18-24 months and $5-8M to develop and launch a new product, not including the actual products COGS, only to discontinue the product about 2 years later - rarely recouping costs.

Process

We worked with subject matter experts across various product industries to identify key areas where agentic AI is realistically applicable and not just window dressing on our existing client offerings. My team then rapidly iterated on various proof-of-concept prototypes to understand feasibility and feature benefits. After filtering down these POCs and testing them in the field we began networking them together, leveraging the real power of an agentic platform.

Various early mockups, starting with a deck of basic bicycle cards.

Playtesting with other game designers at Protospiel.

Thousands of games playtested with users.

Layers upon layers upon layers of design iteration.

Product

Velocity is an agentic AI platform that connects data across the entire lifecycle of a product. It helps draw real, data-driven insights from R&D, linking them with consumer insights from various marketing agents, then iterating through rapid image generation and refinement, creating a design brief for skilled product development teams to then review and eventually execute. The beauty of the platform is its flexibility. Velocity can be deployed modularly across the lifecycle. In some cases, we reduced the design phase for our clients from 6 months to 6 hours. This allows are clients to rapidly execute this process with immensely higher iteration at lower cost. Granted, the deliverable of these iterations is lower quality, it provides a highly data-driven jumping off point for product teams.