Retail & e-commerce
Shoppers describe what they want in their own words; catalogues are organised by SKU.
The challenge
Section titled “The challenge”- Keyword search fails on descriptive queries (“warm waterproof jacket for kids”).
- “More like this” needs similarity, not categories.
- In-store kiosks and apps may run with poor connectivity.
Why SrvDB fits
Section titled “Why SrvDB fits”- Semantic product search over product descriptions and images (CLIP-style embeddings).
- Recommendations with one
searchon an item’s own vector. - Works offline on kiosks and in mobile back-ends.
Five projects, five levels
Section titled “Five projects, five levels”Each project has its own page: the problem, how SrvDB powers it, what changes, architecture, use cases, core code and limits.
| Level | Project | What it does |
|---|---|---|
| 1 · Beginner | Semantic Product Search | Shoppers describe what they want and find it. |
| 2 · Intermediate | Shopping Assistant | Answers product questions from your catalogue and reviews. |
| 3 · Advanced | Listing Guardian | Detects duplicate and counterfeit-looking listings. |
| 4 · Expert | Multimodal Kiosk | In-store kiosk that searches by text or photo. |
| 5 · Frontier | Edge Recommender | “More like this” recommendations running on store or app hardware. |