The problem
Enterprise helpdesks drown in repetition. Agents spend most of their day re-answering questions that have been answered before, and employees wait on tickets that never needed to exist. With 700+ client organisations varying wildly in size, industry, and HR maturity, “just add AI” wasn’t a strategy — the AI had to produce outputs agents could actually trust and send.
What I did
Shipped AI-powered creation workflows. I designed and shipped the AI Assistant — it auto-drafts contextual responses, generates structured diagnoses, and surfaces resolution suggestions — and the Super Agent, which resolves employee queries before they ever become tickets.
Built the knowledge layer that makes the AI trustworthy. I designed Helpdesk’s knowledge/context graph — 16 entities and 80 evaluation metrics — the structured context layer that grounds every AI-generated output. Generic LLM answers become context-aware, client-specific ones.
Earned the right to ship by listening first. I ran the module’s largest-ever client engagement: 11 city visits and 100+ product gaps surfaced, then used usage metrics and feedback loops to drive prioritisation. That research also powered a UX revamp to make a complex enterprise tool effortless for non-technical users — while managing conflicting priorities across 39 client organisations.
Pushed how we build, not just what. I ran an AI dev POC where structured, PM-authored PRDs directly generated functional end-to-end code — compressing the gap between product definition and working software.
Ran the pod. Led a cross-functional pod (engineering, design, QA, client success) to 100% on-time, zero-defect releases, balancing CPO/CTO direction against enterprise client needs and engineering velocity.
The impact
The AI workflows are validated to save 40% of human agent time — 2 in 5 support interactions eliminated entirely, at the scale of 50,000+ end users. Adoption is tracked and iterated continuously across 700+ clients.
What this says about how I work
I don’t treat AI as a feature checkbox. The 40% number exists because of the unglamorous parts — the knowledge graph, the 80 evaluation metrics, the 11 city visits — not despite them. I build the trust layer first, then the magic.