Data silos
Enterprise data is scattered across dozens of systems like CRM, ERP, and OA. With inconsistent formats and fragmented access controls, large models cannot securely read or understand business data.
FDE · Forward Deployed Engineer
FDE Frontier Deployment Engineer, empowering large language modelsTruly implemented in business
From needs assessment to on-site delivery, we help enterprises take AI from demo to production.
About 2 weeks
Deliver a PoC prototype first to validate before full-scale investment.
6 Large
Core capabilities powered by large language models
3 Gear
Collaboration models across different stages
Nationwide
Local city services with nearby response
The last mile of AI deployment
Large models are powerful, but most enterprises hit a wall on these four issues.
Data silos, complex integration, unclear scenarios, and unstable results are the four most common challenges we face on project sites.
Complex Integration
Large model APIs are just the starting point. Integrating with existing IT systems, unifying permissions, and embedding workflows require extensive custom development that traditional outsourcing models cannot handle.
Blurry scene
Business teams recognize AI's potential but struggle to define specific use cases, ROI, or translate business needs into technical solutions.
Unstable performance
Demo looks impressive, but once deployed to production, the answers are inaccurate and untraceable. Business teams hesitate to use it due to lack of trust, causing projects to stall.
Six Core Capabilities Powered by Large Models
From knowledge management to business automation, combine capabilities by scenario instead of relying on a single model for every problem.
Why FDE Pre-deployment is Better Suited for Production Environments
The same model capabilities can yield vastly different results depending on the delivery mode.
| Comparison Criteria | Traditional outsourcing | Standardized SaaS | FDE Pre-deployment |
|---|---|---|---|
| Requirement Understanding | Written requirement documents incur high communication costs. | Fixed functionality; business details not understood. | Work closely with the business team on-site to co-create requirements. |
| Delivery Speed | Long duration, typically measured in months. | Ready to use out of the box, but hard to customize | PoC in approximately 2 weeks to validate value first |
| System Integration | One-time delivery, difficult to maintain later | Siloed interfaces and disconnected data | Deeply integrated into business systems and workflows |
| Performance Guarantee | Pay by milestone; no performance guarantee. | Standardized results are difficult to fine-tune. | Own business outcomes and iterate continuously |
Deployed Industry Scenarios
Here are some of the project categories we've delivered. Actual results may vary based on specific measurements.
Three collaboration models selected by phase
Start with small-scale tests before committing long-term to avoid launching large projects immediately.
PoC Quick Validation
- Cycle
- Approximately 2 to 4 weeks
- Cost
- Fixed Project-Based
- Suitable
- First-time AI users, verify 1 to 2 specific enterprise scenarios.
Delivery:Working prototype + Results report + Implementation roadmap.
On-site FDE Services
- Cycle
- From 3 to 6 months
- Cost
- Monthly service fee
- Suitable
- Enterprises with clear business goals that require deep integration with existing systems.
Delivery:Dedicated FDE engineer onsite, accountable for end-to-end business outcomes.
Subscription-based Operations
- Cycle
- Annual Contract
- Cost
- Annual subscription fee
- Suitable
- A company with validated value that requires continuous iteration and optimization.
Delivery:Remote team + regular on-site visits to continuously optimize results and build capabilities.
AI-powered, built for your business
Beijing Wanwei Suda Technology Co., Ltd.Our team combines large-scale model engineering expertise with deep industry experience. We first understand your business challenges on-site, then determine the best technical solutions.
- AI-powered, built for your businessOur team combines large model engineering expertise with deep industry experience. We first understand your business challenges, then select the right technology to solve them.
- Validate first, then invest.Deliver a working PoC in about 2 weeks using real business data to validate value before deciding to proceed.
- Accountable for business outcomesWe don't sell software or headcount. We continuously iterate based on business metrics like efficiency, cost, and conversion.
Contact
Let's discuss the process you'd like to optimize.
We'll start with a free scenario assessment to determine if AI is the right fit and where to begin for the most stable results.
WeChat Work
Scan the QR code to add our enterprise WeChat and connect directly with an FDE engineer.
Official Taobao Store
Standardized services can be viewed in-store, or you can discuss your needs first to decide which option works best.
Official Taobao StoreCity Coverage
Local Services Across Chinese Cities
Enterprise AI deployment relies on on-site collaboration. We cover 298 prefecture-level cities nationwide, with local support teams in key locations to guide you from scenario diagnosis through on-site delivery.
FAQ
Top Questions Customers Ask About AI Implementation
These questions come from the most common topics in project communications and are key to determining whether an AI project can be successfully deployed.
What sets us apart from other AI service providers?
We deploy FDE engineers directly to the business site: they first understand the business challenges and data conditions, then design the technical solution. Our delivery includes scenario diagnosis, system integration, and ongoing operations post-launch—not just a demo.
How long does it take to see results after communication?
Most projects begin with a 2-week PoC to validate effectiveness and ROI using your own real business data. Once validated, they proceed to an on-site FDE deep integration phase lasting 3 to 6 months or more. The exact timeline depends on the scope and integration complexity.
When is local deployment recommended?
For high-sensitivity data, strict on-premise-only policies, or deep integration with internal systems, we recommend local or hybrid deployment. The solution begins by assessing data sensitivity, existing compute capacity, and post-launch operational capabilities.
Which regions do you serve?
We serve customers nationwide through a hybrid model of remote collaboration and scheduled on-site visits, currently covering 298 prefecture-level cities. For key cities, we can deploy FDE engineers for on-site residency, providing end-to-end support from scenario diagnosis to delivery.