01
AI customer service
Multilingual first-line support that answers from your own documentation, qualifies the request, and hands the high-value cases to your team.
AI-powered trade services · Ontario, Canada
Trade operations that run on AI, not on overtime.
WORLD WIN TRADE COMPANY is a Richmond Hill, Ontario trade company building an AI-driven service platform. We bring customer service, product discovery, quoting, order operations and analytics into one operating layer — so routine work runs itself and your team handles the decisions that matter.
Scoped before built
Every engagement starts with a written scope, not an open-ended retainer.
Claude on AWS Bedrock
Production-grade model access with region-controlled data handling.
Human approval gates
Quotes, payments and order confirmations can require sign-off.
You keep the assets
Code, prompts and data stay in your accounts and repositories.
What we do
Five services, one operating layer.
Each one can stand alone or combine into a single system. We start from the workflow that costs you the most hours, not from a technology list.
02
Product & service showcase
An online catalogue with AI-assisted search, comparison and personalised recommendations drawn from your approved product data and documents.
03
Quoting & RFQ automation
Inbound enquiries are parsed into structured requirements, matched against authorised pricing, and routed for approval before anything is sent.
04
Order & process automation
Documents extracted, orders structured, confirmations and reminders sent — the repetitive work that quietly consumes a week each month.
05
Analytics & business intelligence
Enquiry, opportunity and order data turned into daily, weekly and monthly reporting — with the signals that actually change a decision.
06
Integration & handover
Connection to your CRM, ERP, email and payment systems through documented APIs — plus the runbooks your team needs to own it afterwards.
Inside the company
Run by operators, not a sales team.
The platform we offer is the one we use. This is where enquiries, quotes and orders actually move — and where every automated workflow is tested before it goes live.
How we work
Accuracy over enthusiasm.
An AI system that is wrong 10% of the time is not a 90% win — it is a new review burden. We build for the accuracy your operation can actually absorb.
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Scope before code We write down what the system does, what it must never do, and how success is measured — before development starts.
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Approval where it matters Quotes, payments and order confirmations can be routed to a named approver instead of being sent automatically.
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Your accounts, your data Models are called from infrastructure you control. Customer data is not used to train third-party models.
Delivery shape
Five stages, each one payable on its own.
You are never asked to commit to a full platform up front. Stage one produces something you can test against real cases.
01
Discovery & prototype
Business model, users, data sources and approval rules — plus a working prototype of the core interaction.
02
MVP
One agent or one workflow, running on your real inputs, with a management view.
03
Integration
CRM, ERP, email, documents and reporting connected through APIs.
04
Test & deploy
Accuracy, security, performance and cost tested before general use.
05
Scale
Additional agents and workflows added as usage and ROI justify them.
From enquiry to running system
What an engagement looks like.
Timelines below are typical for a first agent or workflow. Anything larger is quoted after discovery, not before.
1
Requirements and architecture
We map the current process, the data it depends on, and the decisions that must stay human. Output: solution architecture, interface specification and a fixed scope.
1 – 2 weeks
2
Prototype against real cases
The core interaction is built and tested against cases from your own history. You see the failure modes early, while they are still cheap to fix.
2 – 3 weeks
3
Build and integrate
The agent or application is developed, connected to your systems, and instrumented with logging, permissions and approval routing.
4 – 8 weeks
4
Test and deploy
Security, accuracy and performance are tested before general use. Rollout starts with a supervised pilot, then widens as the numbers hold.
2 – 3 weeks
5
Scale
Additional agents, workflows and integrations are added as usage and return justify them — not all at once on day one.
OngoingStart with one workflow
Bring us the process that costs you the most hours.
Tell us how it runs today — who does it, how often, which systems hold the data, and where it breaks. We will tell you honestly whether AI is the right answer.