The tension
Three regions, many experiments, no shared production standard.
WireApps engineers were learning quickly, but local experiments had created different vocabulary and tool choices across teams in the UK, Sri Lanka and UAE.
The challenge was organisational as much as technical: give more than 50 engineers a common way to reason about models, retrieval, agents and evaluation without flattening the experience already in the room.
The intervention
Learn together, inside the codebases that matter.
TAI Labs delivered a live cohort across time zones, pairing shared foundations with practitioner-led work on the team's own codebases.
The cohort established common language and an agreed tooling standard, then used both to examine what production-ready AI engineering should look like in context.
The change
From isolated exploration to a team-wide engineering baseline.
More than 50 engineers now had the same conceptual map and a clearer standard for production work.
That alignment made it easier for teams in different regions to compare approaches, review work and carry lessons from one project into the next.
