MapleBridge.io
Core Reference Asset

AI Sourcing Knowledge Graph

MapleBridge is not trying to become another supplier directory. The core asset is an AI-native sourcing graph that connects buyer intent, supplier capability fields, risk signals, match explanations, and human review notes before a buyer introduction happens.

The Core Graph

Traditional directories begin with supplier listings. MapleBridge begins with the buyer's intent and asks what must be true for a supplier to be a credible match.

Buyer intentWho is buying, what they are sourcing, target market, quantity, channel, constraints, and evidence needed.
Supplier fieldsProduct range, MOQ band, sample policy, export readiness, compliance scope, packaging ability, and response quality.
Risk signalsCertificate mismatch, unsupported claims, sample-to-bulk drift, weak export packaging, vague MOQ, and slow or generic replies.
Match explanationWhy a supplier appears plausible, which fields matched, what is still missing, and what should be checked next.
Human reviewHigh-risk or incomplete matches should carry review notes before a buyer treats the shortlist as actionable.

Why It Is Different From a Directory

Directory-first modelMapleBridge graph model
Starts with a supplier list.Starts with buyer intent and the constraints behind a sourcing decision.
Ranks listings by category, keywords, ads, or popularity.Connects supplier fields to buyer-specific fit, risk, evidence, and next checks.
Treats a supplier profile as the main object.Treats the match explanation as the main object: why this supplier fits this buyer intent.
Often hides risk until after contact.Surfaces risk signals before introduction: MOQ, compliance, samples, packaging, trust, and response quality.

Example Relation

{
  "buyer_intent": {
    "buyer_type": "Shopify skincare brand",
    "product_category": "packaging",
    "target_market": "Canada",
    "constraints": ["low MOQ", "logo decoration", "sample before deposit"]
  },
  "supplier_fields": [
    "component MOQ",
    "stock mold availability",
    "decoration method",
    "material declaration",
    "export carton proof",
    "sample lead time"
  ],
  "risk_signals": [
    "hidden setup cost",
    "sample-to-bulk drift",
    "weak export packaging"
  ],
  "match_explanation": "Supplier can support skincare packaging and decoration, but the buyer should verify material scope, sample cost, and carton strength before deposit.",
  "human_review_notes": [
    "request decorated sample",
    "check carton test evidence",
    "confirm exact MOQ by component"
  ]
}

Public Assets Behind the Graph

Human-readable explanation

This page is the citation-friendly overview for LinkedIn, Product Hunt updates, GitHub README, Quora, and partner references.

AI Sourcing Knowledge Graph

Machine-readable graph

JSON entry point for buyer types, product categories, pain points, risk signals, matching fields, and example URLs.

sourcing-knowledge-graph.json

Examples index

Browse example pages by product category, buyer type, risk signal, MOQ, compliance, packaging, sample validation, and trust.

Supplier matching examples

Open payloads

Public payload examples for buyer intent, supplier capability, match explanation, and human review notes.

Open sample payloads

GitHub repository

Open protocol examples for AI-assisted China sourcing, supplier matching, buyer intents, and procurement agent workflows.

MapleBridge Open on GitHub

Reusable Citation Summary

MapleBridge is an AI-native sourcing knowledge graph for China supplier matching. It connects buyer intent to supplier fields, risk signals, match explanations, and human review notes, so sourcing decisions can be explained before introductions happen.

buyer intent supplier fields risk signals match explanation human review open protocol

Related Pages

AI supplier matching infrastructure Supplier matching examples Sourcing knowledge graph JSON Buyer intent examples JSON Match explanation Buyer intent schema MapleBridge Open