Client cases

We deliver results you can accept, not tools

We map how your business actually runs before landing anything per scenario. Four delivered projects spanning enterprise FDE, supporting system development and AI content production.

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Cases

Four delivered projects

Each case states the real challenge, our approach and a verifiable outcome. Client names and operating figures visible in screenshots have been masked.

01 Restaurant chain

FDE transformation for a regional restaurant chain

Agents deployed across every scenario, 15% lower staffing cost

Challenge: Scheduling, inventory, complaint handling and marketing content all relied on manual work — head office and stores moved slowly, and know-how never accumulated.
Approach: We mapped the core head-office and store processes first, then built purpose-built agents per scenario and connected them to the existing systems.

Agents built per scenario, across head-office operations and store operations
On-site implementation, classroom training and hands-on guidance
A verifiable, iterable transformation package rather than more tools

On-site research: mapping core store and head-office processes — On-site research

On-site training and hands-on guidance — On-site training

02 Restaurant chain

Building a restaurant operating data hub

Multiple platforms consolidated into one visual operating view

Challenge: After the first round of transformation, store data was still scattered across platforms. Without a unified dashboard, management could not judge operating quality in real time.
Approach: We built an operating data hub for restaurant operations, producing visual views around brand benchmarking, daily store reports, conversion rate and average order value.

Multi-platform data consolidation plus front-end and back-end development
From hand-built spreadsheets to reading data visually
A large amount of routine aggregation work eliminated

Brand benchmark and daily store report

Operating data hub: platform filters and core metric cards — Operating data hub

03 Customer acquisition

AI multimedia content enablement

A production line deployed, shipping content at a steady high rate

Challenge: Short video and graphic assets had to ship continuously for acquisition campaigns. Outsourcing was costly and slow, and could not keep pace with high-frequency投放.
Approach: We deployed an AI multimedia production line and delivered the matching SOP so the client team could run it themselves.

Script planning → digital-human narration → final cut → asset distribution
Steady output of digital-human video, short video and graphic assets
Content production freed from outsourcing schedules and cost

AI content production and video synthesis interface — Content production and video synthesis

AI workbench conversation interface — AI workbench

04 Import and export

FDE transformation for a pet supplies exporter

Six-stage chain, 17 scenarios — export documents from 20 minutes down to 2

Challenge: Export documents, multilingual support and multilingual materials all relied on manual work. Handoffs between steps were done by people, so losses could not be quantified.
Approach: We mapped the six-stage chain from sourcing to after-sales, identified 17 scenarios that could actually ship, and sequenced them by whether the gain was visible same-day and convertible into money.

Export documents generated automatically with cross-validation
Multilingual support available 7 × 24
Multilingual materials generated in batches against a glossary
No production line changes, and no English prerequisite for the team

In-depth data

Before and after

The figures below were confirmed at client acceptance and show the difference each step made.

Case 04 · Jincheng Pet Products Co., Ltd.

· Six-stage chain, 17 scenarios — export documents from 20 minutes down to 2

Export document review time

20 → 2 min per set

Inquiry response time

1 hour → 10 min minutes

Multilingual support

5 agents saved support staff

Step	Before	After
Export documents	Manually transcribed, about 20 minutes per set; mismatched fields meant rejection	Generated automatically and cross-validated, about 2 minutes per set
Multilingual support	Night and holiday inquiries answered only the next day; customers moved on while waiting	Multilingual 7 × 24 responses in seconds, qualified before handover to a human
Multilingual materials	Translation software plus manual proofreading; one or two sets a week, with inconsistent terminology	Generated in batches against a glossary, 15 to 20 sets a day

① Sourcing & R&D
② Manufacturing
③ Compliance & market access
④ Order fulfilment
⑤ Sales channels
⑥ After-sales & support

“Before working with you, our workload was repetitive and heavy. After we started, efficiency improved noticeably and staffing cost dropped by 15%. We are still new to this, so we are very satisfied with the result. As more AI scenarios come online, we hope efficiency can reach at least 50%.”
—— Operations lead, a regional restaurant chain in Hebei

How we pick the first scenarios to transform

No dependency on historical data — the effect is visible on day one.
Losses are explicit, and every one of them converts into money.
No production line changes, and no new skills required of the team.

Wondering what your scenario would look like?

Case details are presented by scenario during a demo, matched to your industry and current setup.

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