AI-Native Training Management Platform

Context

The organisation ran training on a legacy internal platform with no environment isolation — changes went straight to production, and a class of avoidable incidents followed. Approval flows were invisible, reporting was manual, and there was no structural link between training and capability. I had the domain expertise from previously delivering an ERP-based training module, and a conviction that an AI-first delivery approach could compress the build dramatically.

Approach

• Built a working mockup independently and pitched it to executive sponsorship — which secured funding, a dedicated development team, and product ownership.
• Ran the product end to end: vision, roadmap, PRD authoring, backlog, sprint cadence, and executive-committee reporting.
• Stayed in the codebase as the leading contributor with 400+ commits, rather than handing specs across a wall.
• Embedded an AI-first SDLC — LLM-assisted specification, requirements, code review, test design, and release documentation — with hallucination risk treated as a first-class engineering concern, mitigated through iterative validation and per-sprint guidelining.
• Introduced proper DEV/UAT environment isolation for the first time, and enforced test gates before merge.

Outcome

• Phase 1 in production three months from first commit, retiring the legacy platform.
• 450+ active users across trainee, coach, admin, and executive personas.
• Enterprise SSO, role-based access across 5 roles, scheduled job infrastructure, notification and certificate workflows, and 4 locales including right-to-left support.
• Phase 2 strategy authored — expanding from 4 to 10 personas and introducing a skills taxonomy that shifts the product from request-and-approval workflow to a closed-loop, measurable learning system.
• The AI-first SDLC developed here is now an internal reference model for how new products get built.

Stack