Two engagement scenarios written as illustrative composites. They draw on patterns we've seen across engineering organisations adopting or operating AI. Neither describes a specific real client, and the numbers in them are sized to match the patterns rather than lifted from a single engagement.
The two below sit at different stages. The first is a growth-stage company whose AI features have started to outrun their cost line. The second is a larger product company trying to restart an engineering modernisation that had stalled. Engagements cover the full range — teams just starting their AI adoption, teams scaling early wins, teams trying to figure out why the numbers haven't moved yet. Real named-client case studies will replace these as engagements complete and clients allow publication.
Illustrative engagement scenario.
A growth-stage SaaS platform was shipping slower each quarter as the codebase compounded, and inference spend on its AI features had started to outrun revenue per user. We worked with the platform team for four months on test generation, caching strategy, and ticket-flow redesign.
Illustrative engagement scenario.
A roughly 1,180-engineer product company had been "modernising" its core platform for two years with not much to show for it. We ran a six-month engagement focused on process re-engineering and embedded delivery on two of the workstreams that had stalled.
If the engagement shape in either of these studies looks similar to something your team is sitting with, the next step is a half-hour conversation.