Real workflows
Start with decisions and collaboration already taking place, not a technology checklist.
About YaoVector
Build growth into the operating system
YaoVector works alongside enterprise teams to identify critical constraints and connect commercial intelligence, AI capability and real workflows—turning growth from a one-off initiative into an operating capability.
Yaowu Technology | Company entity and delivery assurance×YaoVector | Operating growth and AI transformation brand
01 / Identity
Company and brand
Yaowu Technology is the company entity responsible for contracting, delivery and long-term service. YaoVector is its business brand for operating growth and AI transformation. Together, they stand behind professional judgement, delivery quality and responsible data practice.
02 / Operating loop
One closed loop, not a collection of tools
We begin with an operating problem and use six connected actions to turn insight into a measurable, iterative and transferable working system.
- 01
Discover
Understand the real constraints across customers, markets, workflows and data.
- 02
Design
Define outcomes, baselines, owners and the smallest testable path.
- 03
Build
Combine knowledge, data, agents and existing systems inside the workflow.
- 04
Operate
Use, observe and correct the system with frontline teams in real conditions.
- 05
Measure
Build feedback around business outcomes, adoption quality and risk.
- 06
Iterate
Codify what works and expand it across roles and operating moments.
03 / Forward Deployed Engineering
FDE: transformation built in the field
Forward Deployed Engineering does not leave a generic solution for the client to interpret. We enter the operating context, define, build and validate with business owners and frontline users, then transfer sustainable operating capability into the organisation.
Named owners
Business, data, systems and adoption outcomes have identifiable joint owners.
Measurable baseline
Define current state, target and evidence before implementation—not just feature demos.
Controlled data
Permissions, provenance, use boundaries and human review enter the design from day one.
04 / Team
A delivery team spanning operations and technology
Public profiles appear only after review by the individual and the company. Each engagement brings together operating, industry, data, product and engineering roles appropriate to the problem.
EDITORIAL REVIEW
Team profiles will appear after authorised review
Until then, we describe the roles required for delivery without placeholder people or unverified biographies.
05 / Engagement
From fit assessment to scaled operation
The engagement advances through explicit decision gates. Every stage has a clear output, and can stop when the evidence is insufficient.
- 01
Fit conversation
Confirm the importance of the problem, accountable owner and engagement boundary.
- 02
Diagnostic and baseline
Map workflows, data, constraints and a testable outcome.
- 03
Focused validation
Co-build and validate results in one high-value workflow.
- 04
Operate and expand
Establish governance, adoption and iteration before extending to more use cases.
06 / Contact
Start with one real operating problem
Tell us the growth or AI transformation constraint that matters most. We will first assess fit and make the next step explicit.