How a scaling SaaS business steadied delivery and strengthened its data foundation.

Background

I was approached by a company that was expanding its fledgling SaaS business. It was hitting operational and technical ceilings that threatened to stall several years of growth. The size of their dataset was outgrowing the platform originally chosen, and the business was increasingly constrained by slow feature delivery and weak access to usable operational insight.

The core concerns were familiar to many growing businesses: engineering efficiency, delivery pace, and whether the company’s data foundations could support the level of growth it was aiming for. In short, the underlying platform was no longer fit for the business it was becoming.

Assessment

Recommendations

I prioritised two main actions. First, I recommended raising the technical bar through a strong technical lead who could mentor the team, improve standards, and create more consistent delivery practices. Second, I recommended a new approach to the data architecture so it could support current needs and future growth without creating more handbrake.

The proposed direction was an API-first model: a single data gateway to serve existing systems, while allowing third-party integration and a phased migration away from the outdated platform with minimal business disruption.

Implementation

In the delivery phase, I provided technical leadership to the engineering team for several months to improve standards and process. I also drew on my network to recruit contractors who could hit the ground running while helping the team level up. The goal was not just to fix the immediate platform issues, but to build a more stable base for future product work.

The work included improving engineering quality, strengthening data access patterns, and setting up a path that would support better reporting and more useful customer insight. The result was a clearer, more durable foundation for commercial growth and recurring platform improvements.

Outcome

Overall, the business achieved a step-change in capability. Engineering efficiency improved, release confidence grew, and the platform became better able to support a larger dataset and more meaningful customer insight. After six months, a follow-up review showed clear improvements across the original scoring areas.

In commercial terms, the company was also able to secure approximately £1m in additional license revenue over the next two to three years. The new API model also made third-party integration easier and helped lock in longer-term partnerships with customers.