The Challenge
Aquascope had a compelling mission — using digital twin technologies to monitor and improve water quality — but needed senior AI leadership to turn that vision into a credible technical strategy. As an early-stage startup, they faced the classic challenge: how do you build an AI/ML capability that’s ambitious enough to attract investors and talent, but practical enough to actually deliver?
They needed help across the full spectrum: technical roadmap, team building, IP strategy, fundraising narratives, and embedding a data-first culture — all without the budget for a full-time Chief AI Officer.
The Approach
I joined Aquascope’s advisory board as Machine Learning advisor, working directly with the CEO and leadership team over a multi-year engagement:
- AI/ML roadmap: Shaped the technical roadmap to align AI capabilities with environmental impact outcomes and investor expectations — ensuring the company was building towards defensible, measurable results
- Fundraising and investor relations: Helped develop the technical narrative for investment pitches, contributed to fundraising rounds, and built relationships with university research partners
- Team building: Mentored leadership on building scalable, mission-aligned data teams with clear career growth frameworks — critical for attracting talent to a purpose-driven startup
- IP and ethical AI strategy: Guided intellectual property strategy and ethical AI governance to build long-term innovation capability and public trust in the technology
- Data culture: Embedded data-first thinking across departments, helping non-technical teams understand how to contribute to and benefit from the company’s data assets
- Technical direction: Set technical direction for the ML pipeline, including model selection, data acquisition strategy, and integration with the digital twin platform
The Result
- Funded and growing: Contributed to successful fundraising, with a technical narrative that resonated with impact-focused investors
- Credible AI roadmap: The company moved from ad-hoc experimentation to a structured AI/ML roadmap tied to clear business and environmental outcomes
- Stronger team: Leadership developed the capability to hire, retain, and grow data talent independently
- IP foundation: Ethical AI governance and IP strategy positioned the company for long-term defensibility
- Data-driven culture: Teams across the organisation adopted data-first decision making, reducing reliance on advisory input over time