Livestream clip pipeline for a fashion store: one livestream works like ten
Client Profile
Pain Points
Live content wasted
Hours of livestream only feed the host's traffic, leaving highlights unused.
No dedicated editors
Without full-time editors, clips are slow and inconsistent.
Homogeneous ad assets
Recycling the same few assets quickly tires out ad campaigns.
Live Demo
How We Built It
Highlight detection
Audio-video and interaction signals auto-detect high-value moments.
Auto captions and scripts
Auto-generate selling-point captions and voiceover scripts, ready to post.
Multi-platform adaptation
Auto-crop to Douyin, Xiaohongshu and Channels specs.
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
Session review
Analyzed past streams to define highlight criteria.
Clip pipeline
Built auto-clipping, captioning and finishing.
Bulk ad runs
Pushed clips into ads and watched the data.
Optimization
Tuned clipping strategy from feedback.
Routine operation
Every stream auto-produces clip assets.
Before vs After
“Every stream now auto-produces dozens of clips, so we never run out of ad assets.”
Where It Fits
Related Cases
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