Technology Advisor for B2B SaaS

I get close enough to see the real risks, make the hard decisions, and stay to own them.

During one engagement, a large platform investment was on the table, with an independent Azure consultancy set to deliver it. I was closer to the codebase than they were, and that closeness surfaced risks their plans had missed. The work went to a different supplier.

Deep context is half the value. Knowing the domain, the product, the team, the history, and the code well enough that risks show up before money is committed rather than after.

Fresh eyes are the other half. I sit outside the day-to-day, compare each decision with how the same call has played out in other organisations, and say the things nobody inside wants to be first to say.

The work often starts after a technology assessment has settled what should change and why. I own the how. The stream of decisions that follow, the oversight to keep them honest, and hands-on implementation where that moves things fastest.

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

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

Risk & Governance

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

Guiding principles agreed up front, so later decisions have something stable to be tested against
Risk registers, decision logs, and option matrices, kept current as circumstances change
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

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 engagement is structured as a monthly retainer. As an embedded advisor I provide ongoing decision support, strategic direction, and hands-on implementation where it helps, without the cost and commitment of a full-time hire. Context is retained between sessions, so the work 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 clients, so each gets focused attention and tangible output every month.

Time inside a company also buys trust. During one engagement I overheard that a service was failing at peak load, with the next peak close. I took it on and committed to a fix in two days. The fix worked, and the service ran flawlessly. Nobody needed to know exactly what the fix involved. They trusted me.

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.

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.

arrow_downward

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

arrow_downward arrow_forward

sort

Prioritise

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

arrow_downward

build

Implement

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

arrow_downward arrow_back

monitoring

Monitor

Track system performance and behaviour over time
Iterate and refine based on monitoring and real-world feedback

rotate_left arrow_upward

For companies that first need direction, the technology assessment settles what should change, why, and in what order as a standalone three to six week project. The embedded advisor engagement then works through the how, from decisions to implementation.

Who this is for

An embedded advisor works best for small to medium sized B2B SaaS companies who:

Have an established product and team
Face a continuous stream of product and 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 embedded advisor 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. For companies that want a bounded way to begin, the technology assessment is a natural first project.