A research-intensive sourcing desk doesn't need more hires — it needs its visual search automated
The Challenge
Sourcing-intensive businesses that match client requests to available products, such as luxury personal shopping, scale faster than a manual research process can support. Each request requires hours of web searches and image-by-image review to find products matching the specification, and there's no way to raise request volume without proportionally raising the time the founder and team spend on research.
Because the process lives in the head and browser history of a few experts, it can't be delegated without a long apprenticeship, and every new hire starts from zero. Client expectations for speed rise as the business becomes better known, while the research burden per request stays fixed, so growth in demand turns directly into longer turnaround or declined requests. The accumulated history of what was sourced for whom, arguably the business's most valuable asset, sits unused in email threads.
- Expertise bottleneck — the matching process lives in one or two people's heads and browser history, so it can't be delegated without months of apprenticeship.
- Linear scaling — every new client request adds directly to manual search hours; there's no way to grow volume without growing headcount.
- Rising expectations, fixed capacity — as the business gets known, clients expect faster turnaround while per-request research time stays the same, so demand growth becomes declined requests.
- A buried asset — years of sourcing history sit unused in email threads instead of training the business's own matching system.
The Approach
A custom AI vision pipeline replaces manual search. A vision model processes product images to extract style attributes, materials, and category tags; a language model interprets natural-language sourcing queries and applies matching logic against the extracted attribute data, returning structured product recommendations in seconds.
Built as an MVP in five weeks, the pipeline also converts the business's historical sourcing data into a labeled, proprietary training dataset the company now owns for future fine-tuning. Attribute extraction is standardized so results are comparable across sources, and the natural-language interface means team members describe what they need in the same terms a client would use. The scope was deliberately narrow at first (recognition, matching, recommendation output) so value was proven before any broader workflow integration.
Visual attribute extraction · Natural-language query interpretation · Attribute-based matching · Structured recommendation output · Proprietary dataset labeling
AI Role
A vision model extracts structured style, material, and category attributes from product images, while a language model interprets natural-language sourcing requests and matches them against the extracted attribute data. AI Automation Scope: End-to-end product discovery and matching, from image ingestion through natural-language query resolution.
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