Industry · Marketplaces, private traffic and instant retail

E-commerce & new retail

Listings, service, ads, sourcing and live content — one person leading an AI team.

Book an industry diagnostic →See the sample case

Pain points

What keeps owners up at night

Manual listings and price changes across platforms — every error costs money

Service volume spikes in campaigns, too fast to hire and train

Too much ad data; budget shifts always lag

Endless demand for livestream and short-video scripts

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

Role	Mode	What AI does / what people keep
Listing specialist	Transform	AI writes titles and pages and syncs prices; people spot-check
Customer service	Transform	24/7 AI answers standard questions; complex ones go to people
Ads optimizer	Augment	Daily AI diagnosis and bid suggestions; the optimizer decides
Merchandiser	Augment	AI compiles competitor and trend data into a draft shortlist
Content producer	Augment	AI drafts live scripts and short-video outlines

Department agents

AI employees we typically deploy

Listing & Pricing AgentCustomer Q&A AgentAds Diagnosis AgentProduct Research AgentContent AgentCompliance Check Agent

Super AI Workbench

E-commerce war room: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · E-commerce war roomDemo UI · placeholder numbers

Cockpit
AI employees
Workflows
Knowledge
Audit log

Revenue¥ ——

Labor cost¥ ——

Output / person——

Open exceptions——

AI task completion by department (illustrative)

Sales

Ops

Finance

Supply

AI assistant · Monday briefing

GMV, margin, refunds: actual / target / gap
Cross-platform price and stock sync alerts
Daily ad ROI and bid suggestions
Service: AI resolution rate and hand-off reasons

Sample case

How we would approach this industry

Sample scenario · not a real client

An e-commerce retailer (sample)

This is a sample scenario that illustrates our approach in this industry — not a real client. All results are modeled per company during the diagnostic.

01 Discover (2–3 weeks): map the organization and break down roles such as “Listing specialist” and “Customer service” into tasks and time share.
02 Build (90-day pilot): launch Listing & Pricing Agent and Customer Q&A Agent in parallel with the existing process.
03 Adopt & improve (months 4–12): extend to Ads Diagnosis Agent and Product Research Agent; adjust headcount only after each pool meets its guardrails.

Change in execution labor cost
Change in core process hours
Modeled payback

These metrics are modeled with your company’s real data during the diagnostic.

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: Mercado Libre — © OpenAI; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientE-commerce & new retail · Argentina / LatAm

Mercado Libre × OpenAI (with Mercado Libre’s platform team)

FDE intensity: MediumCo-build (platform)

Verdi “started to handle 10% of customer service mediation”; fraud-detection accuracy “nearly 99%”

As reported by OpenAI · 2024 · provider-published

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Background

Massive scale: customer-service mediation, product listing and fraud review all relied on large manual teams.

How the FDE work was done

The in-house team and OpenAI built Verdi, a unified developer platform on GPT-4o models, so business teams could quickly ship service, listing and risk apps.

Results (as reported by OpenAI)

Within months Verdi handled 10% of service mediation on a major site, with “potential to support the tasks of 9,000 human operators”; “catalog 100x more products”; fraud accuracy nearly 99%.

Takeaways

Build one platform first, then let business teams ship apps on it — start with service, expand to ops and risk.

Provider: OpenAI (with Mercado Libre’s platform team)
Delivery mode: Co-build (platform)
Region: Argentina / LatAm

Read the original ↗Mercado Libre introduces Verdi, an AI developer platform powered by GPT‑4o

English summary by BOOK.AI; figures as reported by OpenAI. Quotation marks indicate wording from the source.

Image from the source page: Zalando — © OpenAI; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientE-commerce & new retail · Germany / Europe

Zalando × OpenAI

FDE intensity: MediumCo-build (model migration and evals)

“Product clicks increased by 23%”; “wishlist additions rose by 41%”

As reported by OpenAI · 2024-2025 · provider-published

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Background

Huge SKU range and vague shopper intent; classic search struggled with “what to wear” questions.

How the FDE work was done

The team migrated its assistant to GPT-4o mini with OpenAI and rebuilt its evaluation process; 50% of traffic moved in two weeks before full cut-over.

Results (as reported by OpenAI)

Product clicks in recommendation carousels +23%; wishlist additions +41%.

Takeaways

Build the evaluation system before you switch — let data, not gut feel, decide.

Provider: OpenAI
Delivery mode: Co-build (model migration and evals)
Region: Germany / Europe

Read the original ↗Boosting the customer retail experience with GPT-4o mini | OpenAI

English summary by BOOK.AI; figures as reported by OpenAI. Quotation marks indicate wording from the source.

Image from the source page: Douyin E-commerce (ByteDance) — © PingWest; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientE-commerce & new retail · China

Douyin E-commerce (ByteDance) × Volcano Engine

FDE intensity: MediumJoint R&D

AI live-stream practice room has reached 100,000+ (“10万+”) new merchants

As reported by PingWest · 2025-11-06 · media report

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Background

New merchants found live-streaming hard: unsure what to say, nervous on camera, worried about violations.

How the FDE work was done

Douyin E-commerce and Volcano Engine built an AI practice room covering script polishing, product talk, fan interaction, compliance checks and feedback.

Results (as reported by PingWest)

The AI practice room has reached 100,000+ (“10万+”) new Douyin merchants.

Takeaways

AI “sparring partners” turn training into a product — ideal for sales and frontline roles.

Provider: Volcano Engine
Delivery mode: Joint R&D
Region: China

Screenshot of the source page (captured 2026-10-19): Douyin E-commerce (ByteDance) — © PingWest (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): “双 11” 进行时！火山引擎携手抖音电商，以AI 驱动商家新增长

English summary by BOOK.AI; figures as reported by PingWest. Quotation marks indicate wording from the source.

Image from the source page: Balabala (Semir Group kidswear) — © www.digitaling.com; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientE-commerce & new retail · China

Balabala (Semir Group kidswear) × Taobao & Tmall (Alibaba)

FDE intensity: MediumCo-creation (deep beta of AI customer service)

In May alone, one department had 600+ hours of work done by AI; customers served +10.6% YoY, conversations +19.2%

As reported by Digitaling (via Tianxia Wangshang) · 2025-06-13 · media report

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Background

Promotions strained customer service and content production; legacy bots only gave fixed answers to preset questions.

How the FDE work was done

The brand beta-tested AI customer service with Taobao: the platform AI identified buyer intent and suggested replies for human agents to confirm, while AI also generated video and image-text assets.

Results (as reported by Digitaling (via Tianxia Wangshang))

600+ hours of one department’s work done by AI in May; customers served by human agents up 10.6% YoY and conversations up 19.2%.

Takeaways

Co-creating a beta with the platform is a low-cost way to trial AI service; “AI suggests, human confirms” limits risk.

Provider: Taobao & Tmall (Alibaba)
Delivery mode: Co-creation (deep beta of AI customer service)
Region: China

Screenshot of the source page (captured 2026-10-19): Balabala (Semir Group kidswear) — © www.digitaling.com (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): “增收、降本、提效”，这些品牌用AI抢赢天猫618 - 数英

English summary by BOOK.AI; figures as reported by Digitaling (via Tianxia Wangshang). Quotation marks indicate wording from the source.

Public case · not a BOOK.AI clientE-commerce & new retail · China

Demashi (e-commerce brand) × Tanyu Technology (Volcano Engine partner)

FDE intensity: LightProvider product + brand-customised agents

70% of routine inquiries handled; conversion up 7%, cost down 25% (as reported by vendor)

As reported by China.com (provider award release) · 2025-09-23 · media report

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Background

Legacy chatbots misunderstood shoppers and sounded scripted, and peak promotions overloaded customer service.

How the FDE work was done

The agent learned from product pages and top sales reps’ scripts to form a human–AI knowledge base; the brand customised agents to its own SOPs, with multi-agent dispatch handling conversations.

Results (as reported by vendor: China.com (provider award release))

70% of routine inquiries handled, reception capacity up 300%, average response 8 seconds, conversion up 7%, cost down 25% (as reported by vendor).

Takeaways

Turning top sales reps’ scripts into a knowledge base is one of the fastest wins for e-commerce service agents.

Provider: Tanyu Technology (Volcano Engine partner)
Delivery mode: Provider product + brand-customised agents
Region: China

Read the original ↗Original title (Chinese): 探域入选《互联网周刊》2025新一代信息技术应用优秀案例_中华网

English summary by BOOK.AI; figures as reported by China.com (provider award release). Quotation marks indicate wording from the source.

See public FDE practice across all 14 industries →

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