This is a walkthrough of the kind of autonomous product-hunting agent we build for e-commerce teams — one that scans marketplaces, scores opportunities, and reports back without a human driving the research.
How it works
Agents crawl marketplace movers, trending pages, and social commerce signals across multiple sources every run.
Each candidate is ranked on demand trend, competitive saturation, and estimated margin using an LLM-driven scoring model.
The agent cross-references search volume, social mentions, and supplier pricing to filter out noise before anything reaches a human.
A daily digest of high-confidence opportunities lands in your inbox or dashboard — ranked, sourced, and ready to act on.
Sample output
Illustrative dashboard — demo data, not pulled from real marketplaces.
| Product | Score | Trend (30d) | Competition | Margin | Source |
|---|---|---|---|---|---|
Magnetic Cable Organizer Home & Office | 94 | +212% | Low | 48% | TikTok Shop |
UV Sanitizing Phone Case Tech Accessories | 89 | +156% | Low | 42% | AliExpress |
Collapsible Silicone Kettle Kitchen | 85 | +98% | Medium | 39% | Amazon |
Posture Corrector Clip Health & Wellness | 81 | +74% | Medium | 44% | TikTok Shop |
Mini Projector Stand Tech Accessories | 77 | +61% | High | 33% | Amazon |
Capabilities
Runs continuously across Amazon, AliExpress, TikTok Shop, and other sources you plug in — no manual browsing required.
Correlates search volume, social mentions, and sales velocity to separate real demand from noise.
Estimates landed cost, margin, and competitive density so every opportunity is ranked, not just listed.
Delivers a ranked shortlist on a schedule — Slack, email, or a dashboard — so research happens without anyone driving it.
We design and ship custom research and automation agents wired into your real marketplaces, suppliers, and tools — not a demo.
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