Home-goods e-commerce service QA and after-sales: kill bad reviews in the ticket queue
Client Profile
Pain Points
Uneven script quality
Many new support staff with inconsistent scripts mean uneven service.
Slow after-sales
Manual ticket judgment and routing are slow; delays invite bad reviews.
Complex returns
Back-and-forth returns raise communication costs and hurt experience.
Live Demo
How We Built It
Full-session QA
Every conversation is auto-scored for emotion and risky phrasing.
Smart ticket routing
Tickets auto-classify, route to the right handler, and suggest replies.
Self-serve returns
Guide customers through returns to reduce manual involvement.
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
Service standards
Compiled service standards and QA points.
QA launch
Deployed conversation QA, then optimized.
Ticket system
Integrated auto classification and routing.
Self-serve flow
Launched self-serve returns.
Closed loop
Continuously improve scripts from bad-review attribution.
Before vs After
“We used to react after problems happened. Now the system flags risky tickets in advance, and bad reviews are way down.”
Where It Fits
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