E-commerce AI support and content: one person runs multiple stores
Client Profile
Pain Points
Support under-staffed
Peak inquiry volume can't be answered fast enough, and slow replies kill conversion.
Assets are slow to make
Every product needs detail pages, hero images and ad assets.
Multi-platform duplication
The same product is maintained separately on Taobao, Douyin and Pinduoduo.
Live Demo
How We Built It
AI customer service
An LLM grounded in store knowledge answers common questions and hands off complex ones.
AIGC content factory
Product info auto-generates copy, selling points, hero text and short-video scripts.
One-click distribution
Content auto-adapts to each platform's specs for one-click publishing.
Not betting on one AI — combining the right few
For a real business scenario, we don't lock in a single technical path up front. We evaluate candidate paths on the same benchmark, then design a hybrid so each path does what it's best at.
General LLM API
Public-cloud models such as GPT-4o / Qwen / DeepSeek
- ✓Out-of-the-box, minimal integration cost, cheap to start
- ✕Limited generalization to vertical domains, unstable field-level accuracy, separate compliance design
Industry fine-tuned model
Domain fine-tuning of open-source models on industry data
- ✓Grasps industry semantics, much higher accuracy, private deployment
- ✕Needs labeled data plus ongoing training and upkeep
RAG + rule engine
Retrieval-augmented knowledge plus business rule checks
- ✓Highly targeted, explainable, best latency and cost
- ✕Requires continuous data-loop investment
Fusion, not selection
What we deliver is not a single model but a fused pipeline of primary path + semantic correction + graceful fallback: routine cases are handled by the industry model in seconds; low-confidence fields are re-understood by an LLM; on service anomalies the system degrades to a general API and flags manual review — achieving accuracy, cost, latency and controllability at once.
An explainable, degradable, evolvable AI pipeline
We treat AI engineering as a pipeline, not a black box: every step has clear responsibilities, inputs/outputs and fallbacks. The five-step pipeline plus four-layer architecture below is the technical skeleton.
Ingestion & access layer
Multi-channel access with unified auth, idempotency and masking
AI inference layer
Multi-path inference plus a confidence router switching by features
Business orchestration layer
Connecting results to real business flows with rule checks and closure
Data loop layer
Every online error flows back; models iterate monthly
Tech Stack
Delivery Process
Script audit
Cataloged frequent questions and conversion scripts.
Support launch
Trained the bot and started with simple questions.
Content pipeline
Built AIGC generation with review workflow.
Pilot
Small-scale conversion testing.
Full operation
Daily use with continuous script and asset optimization.
Before vs After
“We used to hire temp support for sales peaks. Now AI handles most inquiries and the team focuses on selection and ads.”
Where It Fits
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