The situation
Travel Counsellors supports a network of home-based travel agents through its own booking and business platform. Its systems combine Angular front ends with .NET microservices, running across on-premises infrastructure and AWS.
January is the peak of the travel year. Looking at the growth forecast, it was clear the architecture would struggle with another year of growth, and that fixing it during the peak would be far too late.
What I did
- Raised the scaling risk early and had the lead engineers carry out a full technical review of the platform.
- Refocused the teams on the root cause and, with the business's backing, paused most feature work to make room for it.
- Led AI adoption across engineering until every engineer was using AI tools in everyday development and review.
- Set clear guidance on where AI helps and where its output needs a human check.
- Moved QA to automation-first, delegated with support to a lead QA engineer. The team completed courses and gained qualifications faster than planned.
- Built an individual career progression plan with each of 21 direct reports.
- Reworked hiring with the people team to attract stronger engineers and QA.
How it was built
Angular front ends sit on .NET microservices, split between on-premises infrastructure and AWS.
The lead engineers ran the review with my support, so the people who would fix the problem owned the diagnosis.
- Angular
- .NET
- C#
- Microservices
- AWS
- On-premises
- AI coding assistants
- Test automation
The result
Ready for peak, with every engineer working with AI.
The root scaling problem became the engineering priority ahead of the peak instead of during it, and the platform was ready for the peak period.
Every engineer adopted AI tools, and productivity rose across engineering. QA works automation-first, and every member of the 21-person group has a clear, agreed progression plan.