“I build the operating system for AI inside hyperscale marketplaces.”
I’m a Senior Lead on Agoda’s Commercial Excellence team in Bangkok, leading the commercial-intelligence stream for GenAI — how commercial teams retrieve, understand and act on intelligence, including agentic workflows, across Agoda’s markets worldwide. Before that: 11+ years in hyperscale e-commerce, 8+ at Amazon — a $200M clean-sheet business build and GenAI in production for 5,000+ operators. I run the loop end-to-end: diagnose, build, ship across regions, and codify so the next market inherits the framework.
Agoda · ex-Amazon
I take the messy, high-stakes problem with no playbook — and leave behind an operating system the organisation keeps using.
Eight-plus years at Amazon, the world's largest third-party marketplace, taught me to work where strategy meets execution: identify the inefficiency or control gap through data, author the business case, design the operating model, drive end-to-end delivery from problem definition through impact measurement, and codify it as SOPs and governance so the next vertical or market inherits the framework rather than starting over.
Four of those years were onsite in Munich, running Pan-EU operations across DE, UK, FR, ES and IT with Tier-1 brand portfolios (J&J, Reckitt, P&G). I've partnered with Director-level strategy boards on the high-ambiguity calls, led a 15-person team in Munich and three in Bengaluru — and I care about shipped outcomes, not decks.
Today I’m based in Bangkok — a region where travel, commerce and marketplaces are scaling faster than almost anywhere — supporting Agoda’s commercial organisation across all markets globally, where how an organisation adopts AI is being decided right now.
Frame the ambiguous problem, find the unit economics and the real bottleneck through data, size the opportunity.
Author the business case and design the operating model — SOPs, decision rights, governance, the cost structure.
Drive cross-functional delivery across Product, Engineering, Commercial, Data, Finance and Legal — into production, across regions.
Turn the win into a repeatable, multi-market playbook so it scales — not a one-off.
Representative builds — each a clean-sheet problem turned into a shipped, measurable outcome.
Spotted the whitespace, authored the business case (ROI model, investment framework, P&L forecast), hired 15 specialists and onboarded 25 Pan-EU vendors on a ~$100M portfolio. Scaled to 3 regions and 50+ partners — a $200M annual run-rate with $1M annual savings, in under 12 months.
Led an AI productivity charter end-to-end — POC → production → monitor-and-iterate with Engineering and Data Science — embedding a GenAI toolkit into the daily work of 5,000+ operators across 5 regions in 6 months. Recovered ~50,000 operational hours/week at ~8.6x first-year ROI. Authored the prompt-engineering and LLM-evaluation guidelines that became the org's production standard.
Led a 90-day Commercial Excellence deep-dive ($70M annual, 500+ SKUs): diagnosed unit economics, restored 190 products to healthy margins, drove 65% QoQ growth (6M→9.9M/quarter) vs ~7% category. Codified the playbook across 10 EU partners for $12M annual uplift, and closed $16M+ in long-term agreements.
Designed a global operating model — SOPs, decision rights, certification-gated readiness, and weekly/monthly/quarterly steering routines that surfaced ~$20M/week of risk before P&L impact. Outcomes: escalations down 91%, KPIs up 1,000 bps, onboarding cycle halved from 8 to 4 weeks.
Architected and launched role-based competency frameworks for 8,000+ managers and above — personalised learning paths and coaching ecosystems. Outcome: learner NPS of 85, knowledge-satisfaction up 1,500+ bps, and a Continuous Learning Program that lifted vendor satisfaction 1,000+ bps. Coached one IC to manager in 12 months — fastest in the org.
Delivered $4.3M in peak-season operational campaigns (Prime Day, Spring Deals) through cross-functional coordination across supply, marketing and partner teams. Resolved 610 operationally-impacted SKUs in 5 days pre-launch, recovering $4.1M in incremental revenue.
When I want to understand an operating model, I build it — a working prototype beats a slide about one. Two recent examples, each a single-agent Claude tool-using loop with grounded retrieval — answer only from the source, cite it, escalate rather than invent — the same discipline behind the production RAG rollout I led at Amazon (0→98% adoption). The human owns the goal and the judgment; the agent does the prep.
An agentic copilot for Key Account Managers on a flat team: it does the account prep so the manager spends the hour on the commercial conversation — and steers “what good looks like” across markets from one central engine.
~3–4 hrs of prep → ~2 min — pushing a flat team toward the 80%-commercial-time target.
An account-excellence assistant for Strategic Account Managers: pull a partner's commercial signals, diagnose the top issues, and draft the partner outreach — grounded in SOPs, not generic model opinion.
The orchestrator pattern: the human curates intent and judges the output; the agent does the retrieval and drafting.
Both are deliberately small, learn-by-doing prototypes on synthetic data — single-agent loops on the merchant/commercial side only (they make delivery reliability visible; they don't own logistics). Honest proof of building, not just sponsoring.
Amazon (DE CL2)
Anthropic — AI Fluency: Framework & Foundations (2025) · Great Learning — Cursor AI for Beginners (2026)
Bangkok, since August 2026. I’m a Senior Lead on Agoda’s Commercial Excellence team, working on how a global travel marketplace’s commercial organisation adopts AI — not as a pilot, but as the standard way the work gets done.
It’s the problem I care most about: the distance between a tool that works in a demo and an organisation that reliably operates differently because of it. Governance, measurement and enablement are what close that gap.
If you’re working on AI adoption inside a commercial or operations org — in Bangkok, Singapore, Kuala Lumpur or anywhere else — or just want to compare notes on operating models and grounded RAG, I’d love to hear from you.