Brand sellers
From zero to traction on a new product, with research, content and ad pacing watched for you.
Vertical models × agents for cross-border commerce
Models that understand cross-border commerce
One model API that understands the business, one agent that runs from product research to cash recovery, and operators who know the trade. Data, judgment and execution on a single line.
$0B+
Real transaction volume
Prior cross-border supply chain business, 2021–2025, GMV basis
0+
Brand partners
0+
Contracted clients
0
North America fulfillment centers
Plus 100+ merchant warehouses
Tens of billions
Tokens per month
Measured monthly usage, 2026
What we deliver
One platform, three ways to deliver
From model to execution to rollout, built on the same industry data and definitions.
Who it is for
However you sell across borders, it plugs in
Wholesale sellers
Thousands of SKUs at once: inspection, replenishment and repricing in bulk.
Service providers
ERP, browser and logistics products add model capability and one more reason to renew.
Large sellers
Profit, inventory and cash recovery at scale, on one definition and an audit trail.
Then and now
From sourcing to cash, every step has a specific trap
These are the problems we saw most often serving sellers over the years. Now an agent takes them on.
The real problem
Pick the wrong product before peak season and the whole season of stock and ad budget drifts.
How it used to work
Operators relied on experience and a few back-end reports. Trends were reviewed after the fact.
How the agent works now
Tracks the past 7 days of rising and newly ranked terms, then returns target audiences, benchmark ASINs and long-tail terms per direction.
15
Rising terms in 7 days, each with a signal
Same question, two answers
A general model gives advice. OneportAI finds the cause.
The same real operations question. A general model gives generic advice; OneportAI works like a veteran: pull the data, make a judgement, deliver something actionable. Each step on the right maps to one scoring point in the rubric.
QThis ASIN's ad spend is up 40% over the last two weeks and orders have barely moved. How should we adjust the ads?
General modelScoring points 1 / 5
Optimize keywords and bids, lower ACOS, watch conversion rate and listing quality, and keep monitoring ad performance.
- ✕Calls the ad query for the last four weeks of this ASIN
- ✕Checks competitor traffic on core terms in external data
- ✕Judges it a spend-structure problem, not a traffic problem
- ✓Does not blame competitor moves as the main cause
- ✕Names the terms and campaigns behind the excess and the action to take
Task failed
OneportAIScoring points 5 / 5
- ✓Tool useCalls the OneBase ad query for the last four weeks of campaigns and search-term detail1
- ✓Tool useCalls an external tool for competitor traffic and bids on core terms and confirms competitors did not raise prices2
- ✓JudgementJudges it a spend-structure problem: impressions are steady and the excess sits on a few broad terms3
- ✓JudgementRules out competitors and separates working terms from money-burning terms by conversion, not clicks4
- ✓ResultNegates 3 money-burning terms and lowers bids on 2 campaigns, bringing ACOS back inside the target; bid changes need human approval5
Task passed
Model service
One entry point to a model that knows cross-border
Quick to connect, with experts helping you go live. Your systems, coding assistants and AI assistants all use the same entry point.
Your business systems
- Stores
- Ads
- ERP
- Logistics
- Browser
One entry point
Picks the right model automatically
- Deep reasoning
- Specialist models
- Industry knowledge
Where you use it
- Your app
- Coding assistant
- AI assistant
Surrounding services already wired in
Sales channels, ad platforms, ERPs and service providers sit behind the same entry point.
Marketplaces
Amazon
Shopify
TikTok Shop
TemuAd platforms
Business systems
Why it is stronger
Where we are stronger
+13%
Task all-pass rate over the strongest competitor
Tasks come from real business work, rubrics are written by front-line cross-border experts, and a task passes only if every criterion passes.
Performance by business area
OneportAIGPTGLMQwenDeepSeek
How the model gets sharper
Real business comes with its own questions and answers
We turn them into training data. What the agent does in real work flows back into the model, and each makes the other stronger.
Real business→Questions→Practice→Scoring→Update↺
- 01Every task comes from private business trajectories that cannot be scraped or bought.
- 02Experts grade, results flow back the same day, and mistakes become new tasks and signals.
- 03The agent creates new data in real work, so every model version is sharper than the last.
Agent → ModelThe agent works in real business and creates new trajectories
Model → AgentA stronger model makes the agent more accurate
Rollout and expert services
People who know the business, onboarding with you
Going live with the tool is only the start. Our operators help you get the first workflow live, then expand the whole chain.
- 01
Connect
Link stores, ads and ERP on one set of definitions.
- 02
Pilot
Pick the most painful step and get it running.
- 03
Launch
Permissions, quotas and approvals set, and the team starts using it.
- 04
Improve
Experts review results and feed experience back into the product.
Our operators work with you until the first workflow runs.
Security and control
Easy to release, easier to rein in
One wrong step is rarely small, it compounds. So every action has limits.
01
Data isolation
Isolated environments per store, no shared state across accounts, sensitive data stays with you.
02
Every action on record
Who did what and when, fully replayable, with cost split by person and agent.
03
High-risk actions need a human
Low-risk actions run first, medium-risk ones become drafts, high-risk ones go to a person.
About us
18 years in cross-border, two listed companies
We started as sellers. Our two founding teams built two listed cross-border companies, with 18 years of business experience.
We built from zero an Amazon distribution platform handling $1B+ a year, with a large enterprise user base, rich product data and real operating data.
Now we are turning that experience into a vertical model that truly understands cross-border.
- 2016Entered cross-border e-commerce
- 2019–2020Two cross-border companies went public
- 2021–2025Built the distribution platform: $2B+ cumulative
- 2026Focus on the vertical model; agents go to work
What we can do
13 + 100+
North America fulfillment centers + merchant warehouses
10M+
Units shipped per year
18+ years
Of business experience, 10+ years of AI
Tens of billions
Tokens per month
Amazon VCAmazon SCAmazon AdsTikTok ShopLingxing ERPJijia ERPSaihu ERPEccang ERPShopify
Start by getting your most painful step running
See how the model service plugs into your systems, then how the benchmark measures a model that knows cross-border.