Multilingual Amazon listings: new-product launches from 3 days to half a day
Client Profile
Pain Points
Expensive translations
Translating one listing into 12 languages is costly and slow via outsourcing.
Slow launches miss windows
Three days from product to listing misses the best traffic window.
Weak keyword coverage
Hand-written titles bury few keywords, so ranking stalls.
Live Demo
How We Built It
Multilingual generation
Auto-generate titles, bullet points and descriptions, then localize.
Competitor keyword analysis
Mine competitor and search-term data for keyword placement tips.
Bulk A/B testing
Generate multiple versions and A/B test to find the best-converting one.
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
Product templates
Standardized input fields for product info.
Generation pipeline
Built listing generation with review.
Keyword engine
Integrated competitor and search-term analysis.
Bulk launch
Rolled out after testing.
Iteration
Continuous copy iteration from ad and conversion data.
Before vs After
“A new product used to wait three days just for its listing. Now it's up in half a day — much faster during peak season.”
Where It Fits
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