Scout is a semantic search engine for profile-based products. It understands what a user means in plain language — across talent, creators, properties, listings — and returns who, or what, actually fits.
Users rarely know the keywords. They only know what they mean.
Keyword search works when users know the exact terms to type. In profile-based products, they rarely do. A recruiter looking for “someone who has scaled a data team through hypergrowth” does not know which keywords to use. A brand looking for “a creator with a warm, editorial aesthetic and a quietly loyal audience” cannot express that in a filter panel. A tenant looking for “something that feels like a loft but in a quieter neighborhood” is not going to find it by bedroom count.
The problem is not that users are imprecise. The architecture was designed for a different kind of query. Keyword and filter search treats profiles as flat documents — but real profiles are multidimensional. The person, the creator, the property each carries a topic dimension, a visual dimension, an audience dimension, a behavioral dimension, a style dimension. Keyword search collapses all of it into one text field and hopes.
Scout does not. It indexes every dimension separately, understands what the query is asking across each of them, and retrieves against all of them at once.
Most semantic search is one layer — embed the query, embed the documents, take the nearest neighbors. Scout is three: it understands the query before it retrieves, retrieves across every dimension at once, and gates the results before you ever see them.
Most semantic search is one layer — embed the query, embed the documents, take the nearest neighbors. Scout is three: it understands the query before it retrieves, retrieves across every dimension at once, and gates the results before you ever see them.
Most semantic search implementations are a single layer. Scout is three — indexing, query understanding, and retrieval — and each is doing work the others cannot.
Most semantic search implementations are a single layer. Scout is three — indexing, query understanding, and retrieval — and each is doing work the others cannot.
Every profile, indexed across every dimension.
Profiles are not stored as a single blob. They are decomposed into discrete facets — each capturing a different dimension of what a profile is — and each facet is indexed independently. A query about experience is not diluted by location noise. A query about style is not confused with category. On top of text, CLIP visual embeddings place images and language in the same vector space, so a description of what something should look like lands near profiles whose imagery matches — with no labels on the images at all.
The query arrives in plain language. Scout reads it like an expert would.
Before any retrieval, Scout parses the query across every dimension at once — extracting topics, generating facet-specific keyword and phrase lists, resolving hard filters expressed in natural language, and detecting special intents that shape how retrieval should run. An LLM planner then decides how to structure the page: not a flat ranked list, but a set of distinct thematic sections, each targeting a different angle and running its own retrieval.
Only the right candidates make it through.
Each section retrieves independently — structured filtering, facet-boosted keyword matching, and kNN vector search running simultaneously. A neural reranker re-orders the raw candidates by true semantic relevance, then an LLM relevance gate makes a final pass, giving each candidate a binary verdict and removing anything that technically matched but clearly does not fit. What remains streams to the interface.
Embedding the query and finding nearest neighbors is table stakes. These four are what separate a retrieval engine from a search box.
Embedding the query and finding nearest neighbors is table stakes. These four are what separate a retrieval engine from a search box.
Standard semantic search embeds the whole profile. Scout embeds each dimension on its own. The payoff is searchability without noise — visual queries are not diluted by topic signals, audience queries are not confused with content format.
Text facet embeddings and CLIP visual embeddings run in parallel. Scout retrieves against what a profile says and what it looks like, simultaneously and independently. Most approaches give you one or the other without custom engineering.
Most search returns a ranked list. Scout returns a structured page — multiple thematic sections, each independently targeted. Users see the best results organized by angle. That is the difference between a search engine and a discovery product.
Retrieval and reranking get you close. The LLM relevance gate gets you precise — profiles that matched on keywords but missed on meaning are removed before the user ever sees them. What is left is a set a human expert would be proud to have assembled.
Scout's architecture is built to grow. The dual embedding strategy — text and visual — is one instance of a broader principle: any modality with a contrastive, language-trained model becomes a searchable dimension.
CLAP does for audio what CLIP does for images — embedding actual sound so a query like "warm, intimate acoustic with a melancholic mood" retrieves against it, with no text labels. Video-language models extend the logic to motion and pacing; document embeddings to portfolios, CVs, and case studies; behavioral and temporal embeddings to trajectory — not just who a profile is, but how they have moved over time.
Every domain Scout has shipped in — talent, creators, properties — added new modalities and new facets, and every deployment made the architecture more capable. A new engagement inherits not just the engine, but everything learned from every domain that came before it.
“Keyword search was never going to get you there. The gap between what your users mean and what they type is an architecture problem — not a UX one.”
Every engagement that draws on Scout starts with the three-layer architecture already in place. The facet model is designed. The dual embedding strategy is implemented. The query understanding pipeline, the search planner, the reranking and gating logic — all of it has already run in production. What an engagement adds is your domain, your profile structure, and your users' query patterns. That is the part worth spending time on.
Tell us about your domain, your profile structure, and where your current search breaks down. We'll tell you honestly what Scout can do for it — and what it would take.