AI NATIVE · Cross-border Infrastructure

Let global trade
flow down one chain

The AI-native infrastructure for going global. Sites, inquiries, matchmaking, traffic — one workflow, end to end.

Not four products. One closed loop. traffic → site → inbox → deal
01
Traffic
Reach
Creators / SEO / private domain — three lanes at once
02
Site
Sitebox
LLM runs brand → code in 30 minutes
03
Inbox
Inbox
7×24 understands, quotes, follows up
04
Match
Match
Hard constraints + cascading relax. No vector guessing.
Traffic in → site catches → engine converts → matched into orders.
Why us

Not a ranking war. Not a pile of plugins. One workflow that actually closes deals.

The job
Traditional trade
SaaS patchwork
Link4a
Traffic
Buy ads on marketplaces / hand out cards at fairs
Tools don't bring traffic
Creators / SEO / private domain, built in
Inquiries
Human-handled; lost if not answered in 3 days
Tool islands, data doesn't flow
LLM understands + replies, 7×24, real time
Matchmaking
Down to a salesperson's gut
Vector similarity; constraints fail
Hard constraints + cascading relax, explainable
Four modules, one loop

Four things, done together — not four SKUs sold apart.

Traffic comes in, the site catches it, the engine converts it, matchmaking closes it.

Sitebox · Site generation
Sitebox

Site generation

A branded standalone site in 30 minutes

  • LLM runs brand → copy → code
  • Multi-template · multi-language
  • Deploys to Cloudflare Pages
Watch Sitebox run
Inbox · Inquiry AI
Inbox

Inquiry AI

7×24 understand + quote + follow up

  • WhatsApp / email fully wired
  • Auto-translation across languages
  • Funnel state machine, visualized
Talk through your inbox
Match · Matchmaking engine
Match

Matchmaking engine

Hard constraints + LLM re-rank

  • Category / process / MOQ / export
  • 7-level cascading relaxation
  • Recommendation reasons in plain Chinese
How the engine works
Reach · Content engine, omni-channel traffic
Reach

Content engine, omni-channel traffic

One content engine — get the factory seen everywhere

  • Social matrix: TikTok / Instagram / X / LinkedIn
  • SEO + GEO: ranked on Google and quoted by AI engines
  • Outbound + lead mining: LinkedIn / customs data / email
Talk through your traffic plan
Inside the matchmaking engine

A database, a light model, a heavy model and deterministic tools — orchestrated into one workflow that runs.

We borrow the task-orchestration paradigm from agentic coding: each phase does one job well, and hands off explicitly.

RFQ in
Phase 1
Understand
haiku · ~1s
Parse to JSON
Confidence < 0.4 → escalate to human
Phase 2
Pre-filter
Mongo · ~30ms
7-level cascading relax
Candidates ≤ 100 · debuggable
Phase 3
LLM re-rank
haiku · ~2s
Recommendation reasons
Recall-biased · metadata packing
Phase 4 · reserved
Agent loop
sonnet · multi-turn
4-tool loop
Only on low scores · ≤ 8 turns
Result

We don't treat the model as a search engine, and we don't string tools into a plugin market.

Light models read intent, the database does the hard filtering, the heavy model only earns its keep on the few hard cases — every step is explainable.

De-identified RFQ

"Looking for a supplier of 304 stainless steel tumblers, double-wall vacuum, 500ml, laser logo, MOQ around 2000, ship to Germany before Q4."

MongoDB candidates · 0
YongkangJinhuaYiwu +117
Engine output · top 2
Factory A · Yongkang, Zhejiang 0.00
304 verified · vacuum line in-house · laser marking · exported to DE, has CE
Factory B · Jinhua, Zhejiang 0.00
MOQ 1500, more flexible · slightly longer lead time, relaxed at level 3
Real cases

Real factories. Real authorization. Real orders.

CASE 01 Zhejiang · Hardware · Stainless steel
Zhejiang · Hardware · Stainless steel

"From a fair business card to inquiry automation — first German order in 3 weeks."

Site live 10 days
First order closed 21 days
Monthly inquiries +280%
CASE 02 Guangdong · Small appliances · OEM
Guangdong · Small appliances · OEM

"The AI handled night-time inquiries in 6 timezones — no more missed leads."

Reply latency < 2 min
Quotes / month 320+
Close rate +46%
CASE 03 Fujian · Outdoor gear · DTC
Fujian · Outdoor gear · DTC

"Matched to 4 TikTok creators in a week — the first one drove 1,100 orders."

Creators matched 4 / week
First-batch orders 1,100+
GMV growth +3.2×
0 +
Factories served
0 +
Sites launched
0 h
Avg. first-order response
0 %
Inquiry parsing accuracy
About

We believe the next decade of cross-border trade is AI-native — not a model used as a search box, not tools stitched into a plugin market, but database, light models, heavy models and deterministic tools orchestrated into a workflow that actually runs.

Link4a is making the link between Chinese factories and global buyers shorter, sharper and more explainable.

Team / at-work photo · 4:3

Source from verified Chinese factories.

We make the matchmaking actually work — sample to mass production.

Apply to partner
2026 · 05 Matchmaking engine Phase 3 live — avg match time down to 3s New
2026 · 03 Sitebox cuts brand-to-deploy to under 30 minutes
2026 · 01 Factories served crosses 300
Contact

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