Systems I've built.

Sellers were prioritising accounts by gut and stale lists. Useful signals existed but were scattered across tools, and ranking them by hand didn't scale across APAC markets.

An AI-assisted workflow that pulls signals from across the stack, scores and ranks accounts by likelihood and timing, and hands sellers a prioritised, refreshed shortlist — no manual list-building.

[One honest reflection — what you'd change, where the model needed a human, what you learned. This line is what makes it credible.]

Buying intent was showing up in fragments — across product usage, engagement, and external signals — but no one had a single, trusted view, so moments of intent were being missed.

A system that consolidates and interprets signals, surfaces the ones that actually indicate intent, and routes them to the right person at the right moment instead of leaving insights trapped across tools.

[One honest reflection — e.g. signal noise vs. precision, where trust had to be earned before sellers acted on it.]

Personalised enterprise outreach was either genuinely personal (and slow) or scalable (and generic). There was no way to do both across multiple APAC markets without a big team.

An AI-assisted outreach workflow that grounds each message in real account context, so enterprise outreach stays personal and relevant while running at a scale a manual process couldn't reach.

[One honest reflection — e.g. where automation helped vs. where the human voice still mattered, guardrails for quality/trust.]