MapleBridge.io
AI supplier matching for North American buyers
2 followers
AI supplier matching for North American buyers
2 followers
MapleBridge helps North American buyers source from China without browsing endless supplier directories. Buyers submit one sourcing brief covering product specs, MOQ, destination market, packaging, compliance context, and supplier fit. The workflow helps create a focused manufacturer shortlist before samples, certification checks, contracts, and inspection.
Hey Product Hunt!
I built MapleBridge after seeing North America buyers spend too much time filtering large supplier directories and too little time talking to factories that actually fit.
MapleBridge is a matching-first sourcing platform. Buyers submit one sourcing brief, and we help match them with Chinese manufacturers based on product type, MOQ, compliance context, and communication fit.
If you've sourced from China before, I'd love to hear what slowed you down most.
Platform: maplebridge.io/for-buyers
Small product update:
MapleBridge Open now includes more concrete sample payloads for skincare packaging, smart home electronics, and recycled packaging workflows.
The important part is the match explanation layer: each sample shows why a buyer intent and supplier capability fit, and what still needs human review before an introduction.
Sample payloads:
https://github.com/jinjihuang88-...
I added a few anonymous workflow cases to MapleBridge this week. They are meant to show the layer before supplier matching: turning a vague request into concrete fields, risk checks, and human-review decisions.
One example is a 500-unit FCC Bluetooth earbuds request for Amazon FBA. The hard part is not only finding electronics suppliers; it is checking whether the certification, labeling, packaging, sample/MOQ expectations, and notification flow are clear enough before buyer and supplier are introduced.
Case page: https://maplebridge.io/case-fcc-bluetooth-earbuds-amazon-fba
Quick update: MapleBridge Open is now public.
We published the buyer-agent + seller-agent + match-engine contract layer as a separate open repo and docs set.
Open layer: https://maplebridge.io/open/
GitHub: https://github.com/jinjihuang88-...
Small product update:
I have been tightening MapleBridge’s matching guardrails this week.
One issue with AI supplier matching is that a high semantic score is not always a real sourcing fit. For example, “electronics importer” and “beauty massage device factory” may look related to an AI model, but the product scope is too different for a strong match.
So MapleBridge now treats semantic scoring as only one layer. We also check product domain, product form, MOQ, certification context, target market, and business model before a candidate becomes a strong match.
The goal is to reduce false-positive introductions, not just return more supplier names.
Related resource hub:
https://maplebridge.io/resources