Enterprise globalization
Multilingual websites, search visibility, localized content, and inquiry workflows for global buyers.
Open ↗AI GLOBALIZATION · AGENT OPERATIONS
Put AI to work. Take Chinese companies global.
DEW helps industrial and technology companies organize knowledge, connect data, deploy governed AI agents, and build digital systems for global customers.
Book a 30-minute fit callWHAT YOU GET
Start with a real workflow and a measurable pilot.
WHO THIS FITS
A useful engagement starts with the right problem.
Global buyers cannot understand your capability
Product, engineering, and facility materials exist, but the site does not follow an international buyer’s decision path.
Teams repeat information work every day
Knowledge questions, inquiry email, account research, and cross-system entry consume valuable time.
AI exists but cannot enter operations
There is no defined role, trusted knowledge, permission, approval, logging, or measurable pilot.
THE CURRENT GAP
What has to change before technology creates value.
Core: enterprise globalization
Turn product and engineering strength into multilingual websites, search content, sales materials, and inquiry journeys.
Wing: enterprise AI agents
Deploy knowledge, inquiry, and sales agents into bounded real roles.
Wing: business automation
Connect email, CRM, forms, approvals, and internal systems into trackable workflows.
HOW WE WORK · FDE
Forward Deployed Engineering: from operational reality to production
DEW works directly with customer business and technical teams to define the problem, connect real data and systems, build the smallest useful production loop, and improve it through adoption and operating feedback.
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01 / Discover in context
Enter the real workflow, users, systems, and constraints before proposing a fix.
Workflow · users · constraints -
02 / Scope the deployment
Define data, integrations, permissions, risks, acceptance, and the smallest useful scope.
Scope · risks · acceptance -
03 / Build the smallest useful loop
Build a working loop with real or approved anonymized data and a human fallback.
Working loop · human fallback -
04 / Deploy into production
Integrate, test, release, and document ownership and exception handling.
Release · ownership · exceptions -
05 / Drive adoption and iteration
Watch usage, failures, cost, and outcomes, then improve the operating workflow.
Usage · feedback · improvement
EVIDENCE
Delivered work, governed systems, and explicit acceptance criteria.
Business before model
Define the role, workflow, and outcome before choosing models and tools.
Product and engineering together
Architecture, interaction, development, data, and AI stay in one delivery chain.
Responsibility after launch
Monitoring, exceptions, feedback, content, and search improvement remain part of the product.
BOUNDARIES
What this engagement does and does not promise.
No promise of first-place ranking
We build sound structure, content, and operations without manufacturing ranking guarantees.
No promise of zero AI errors
Sources, refusal, approval, testing, and logs manage risk.
No fabricated cases
Only real delivery, verifiable scope, and approved facts are shown.
FAQ
Questions teams ask before starting.
Which companies are the best fit?
The first focus is industrial, equipment, technology, and globalizing Chinese companies with complex knowledge, clear overseas goals, or repeated workflows.
Should we start with the website, an agent, or automation?
It depends on the current bottleneck. The fit call identifies whether customer acquisition, knowledge, inquiries, sales, or internal operations should go first.
Why start with a two-to-four-week pilot?
A pilot validates quality, efficiency, permission, and team usage on real samples before larger investment.
How long do SEO and GEO take?
Technical foundations can move quickly, but indexing, rankings, and AI citations require ongoing content, external signals, public accessibility, and time.