Home / Digital Health Startup Scopes MVP with Lean AI Discovery Workshop

Validating a Digital Health MVP in a Lean AI Discovery Workshop

A Lean AI Discovery Workshop Aligning Value and Feasibility

A digital health startup approached us with an ambitious idea: to use eye tracking to support mental healthcare. Although they had initial funding and strong research expertise, they lacked clarity on how to develop their product concept into something that was both technically feasible and valuable to users.

They needed support defining how AI and computer vision could realistically be applied, what data would be required, and how to plan for development without overbuilding too soon. We proposed a Lean AI Discovery Workshop to help them clarify what was possible and valuable and how to prioritise their next steps.

Challenge

Unclear about AI and CV for eye-tracking in mental health

No internal tech capacity to assess feasibility

Unsure how to validate wasteless time or resources

Solution

A Workshop tailored to mental health and neurotech

Evaluate use cases against clinical value & feasibility

Define MVP & clarified data, hardware and model needs

Outcome

Prioritised viable product based on actual constraints

Identified missing data and prototype requirements early

Clear next steps towards validation and PoC planning

A Focused Discovery to Assess CV and AI Potential in Mental Health

We designed a Lean AI Discovery Workshop to help the team assess the feasibility of incorporating AI into their digital health solution. We tailored the workshop to focus on the relevant technologies, eye-tracking data and computer vision and to align with the current product vision, addressing the technical, clinical and regulatory questions.

Through guided discussions and a feasibility analysis, we examined areas where AI and computer vision could provide tangible benefits for mental health diagnostics. We explored various concepts, including gaze-based interaction analysis and emotion detection. Based on data availability, clinical relevance and development complexity, the most promising avenues for future development were identified.

Defining the Right Scope: From Data Gaps to Technical Feasibility

To ensure clarity on what is realistically possible, we mapped the data they currently have against what would be needed to develop the most relevant AI and CV features. We also reviewed their initial ideas in terms of technical feasibility, complexity and user value.

This helped the team distinguish between short-term opportunities and longer-term ideas requiring further data collection or validation. We provided clear guidance on where to focus first and which AI ambitions were not yet viable, avoiding wasted effort further down the line.

Aligning AI with Business Strategy & Team Capability

Beyond just the feasibility of the proposed AI use cases, we examined how they aligned with the company's product roadmap and internal team capabilities. We also discussed the technological foundations required, such as cloud architecture or analytics layers and identified areas where external support might be needed.

The result was a set of realistic, well-prioritised next steps that matched the team's current level of maturity and the resources available to them. This gave the team the confidence to move forward without overextending themselves. It also clarified where to focus limited resources and which initiatives could be postponed.

What does the Client has to say?

“In just one workshop, Punktum helped me bring clarity to my product, align on technical strategy, and prepare for funding conversations. Their support was a game-changer.”

What Comes Next: From Planning to Execution

With a clear understanding of what’s feasible, valuable and realistic, the team is now preparing for implementation. Their next steps include confirming the availability of user interaction data, setting up the back-end infrastructure and defining the success criteria for their AI prototype.

By translating abstract ideas into concrete tasks and requirements, the Lean AI Discovery Workshop helped the team reduce uncertainty and move forward with confidence. They now have a clear plan for testing and launching their first AI-enhanced feature.