A Long-Term, Pragmatic Technology Partner

Calm, structured progress through complex product, platform, and AI decisions.

B2B SaaS companies often face important technology decisions before the path is fully clear. A product may need modernising, a new opportunity may need validating, customers may be asking for more, or AI may be creating pressure to act.

The challenge is knowing what to do next, what to delay, what to test, and what risks need managing before serious time and budget are committed.

I work with a small number of medium sized B2B SaaS companies on an ongoing basis, focusing on three areas:

01

Product & Technology Strategy

I work with the team to shape product and technology direction, clarifying the options and where to focus.

  • Understanding the product, market, customers, and operational practices
  • Identifying opportunities to improve, modernise, or extend the platform
  • Exploring where AI genuinely creates value within the business
  • Filtering out noise and avoiding unnecessary complexity
  • Prioritising initiatives based on value, feasibility, risk, and timing

02

Risk & Governance

I help make risk visible and manageable, using proportionate governance that supports confident decisions without slowing delivery down.

  • Practical risk registers, decision logs, and option matrices
  • Clear ownership and accountability for important technology decisions
  • AI governance where relevant, including policies, documentation, and responsible adoption
  • Assessment of organisational readiness, delivery risk, and operational impact
  • Ongoing review of assumptions, risks, and outcomes

03

Applied Research & Prototyping

I use focused research, experiments, and prototypes to reduce uncertainty before full-scale implementation.

  • Proactively identifying meaningful product, platform, and AI opportunities
  • Designing and validating selected initiatives before formal roadmap commitment
  • Building proof-of-concepts and early prototypes to test feasibility before committing to full implementation
  • Exploring legacy systems, workflows, data, and technical constraints
  • Producing structured findings to guide decisions and next steps

About me

With a background in software development and business, I take a practical, applied approach to product, technology, and AI strategy.

I've spent years building and supporting real software systems, so I approach strategy with implementation in mind. I'm interested in what can actually be built, adopted, maintained, and improved, not just what sounds good in theory.

Since 2021, I've focused deeply on machine learning and AI, combining practical experimentation with a solid understanding of the underlying concepts and limitations.

I like to immerse myself in the product, market, users, business problem, challenges, and roadmap.

How it works

The partnership is structured as a monthly retainer, where I provide ongoing decision support and strategic direction without the cost and commitment of a full-time hire. Context is retained between sessions, so the partnership builds momentum over time rather than starting from scratch each month.

I work remotely but stay in regular contact via email, messaging and video calls, with the occasional on-site visit, where appropriate.

I intentionally work with a small number of partners, so each gets focused attention and tangible output every month.

My approach is supported by practical documentation, decision tools, and structured research methods, helping teams move from discovery and assessment through to recommendations. Where AI is involved, I align with established frameworks such as the NIST AI RMF and Microsoft Responsible AI principles.

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Discover

The discover phase is the foundation for everything that follows. A deep contextual understanding of the market, product, and operations is essential for making informed decisions.

I spend time onboarding through documentation review, hands-on use of the product, codebase or system exploration where relevant, and conversations with key stakeholders.

I use AI tools to accelerate this discovery work, helping me build context faster and surface useful questions earlier.

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The operating loop for continuous improvement

analytics

Assess

  • Evaluate opportunities, risks, constraints, and expected impact
  • Separate real value from noise and focus on what matters
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sort

Prioritise

  • Prioritise options by value, urgency, risk, effort, and readiness
  • Build a phased roadmap aligned with commercial and technical reality
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build

Implement

  • Support selected initiatives through design, prototyping and technical planning
  • Integrate new capabilities into existing products, systems, and workflows
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monitoring

Monitor

  • Track system performance and behaviour over time
  • Iterate and refine based on monitoring and real-world feedback
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Who this is for

This partnership works best for small to medium sized B2B SaaS companies who:

  • Have an established product and team
  • Are facing important product or technology decisions
  • Want to modernise, explore, or build without overcommitting too early
  • Need senior judgement, but not necessarily a full-time technology leader
  • Value practical research, clear communication, and evidence-led decisions
  • Want to use AI where it genuinely adds value, without being distracted by hype

Rather than committing to a full-time hire or a large consultancy engagement, they value access to a long-term strategic partner who can understand the business deeply while bringing outside perspective from seeing how AI adoption, product decisions, and technology change play out across different organisations.

Getting started

Exploring whether this is a fit starts with a short, no-obligation conversation.