Semantic Search Engines
Traditional keyword search is frustrating—if a user searches 'sneakers' but your database says 'running shoes', they get zero results. We build Semantic Search engines using Vector Embeddings that understand the *meaning* of a query, delivering highly relevant results even if the exact words don't match.
Core Features
Concept Matching
Users can search 'something warm for a winter trip' and the engine will return heavy coats, thermals, and beanies, without matching any keywords.
Multi-Lingual Search
A user can search in Spanish ('zapatos de correr') and retrieve the exact same English product listings as 'running shoes', automatically.
Hybrid Retrieval
Combining the magic of vector search (for meaning) with the precision of BM25 (for exact SKUs and part numbers) to get the best of both worlds.
Image-to-Text Search
Allowing users to upload a photo to search your database for visually similar or conceptually related products.
Our Process
Catalog Ingestion & Cleaning
Week 1Extracting your current product catalog or knowledge base and cleaning the metadata to prepare it for vectorization.
Embedding Model Selection
Week 2Testing various embedding models (OpenAI, Cohere, open-source sentence-transformers) to find the best fit for your industry's vocabulary.
Vector Database Architecture
Week 3Setting up a high-performance vector database (Pinecone, Qdrant, or Elasticsearch dense vectors) and indexing the embedded catalog.
Hybrid Search Logic Development
Week 4-5Writing the query logic that balances the semantic vector score with traditional keyword scores, and applying business logic (e.g., boosting in-stock items).
A/B Testing & UI Integration
Week 6Deploying the new search API to your frontend (Shopify/React) and running A/B tests against your legacy search to measure conversion lift.
Technologies We Use
FAQ
Will semantic search break when someone types an exact SKU number?
Is it expensive to run vector search?
Do we have to re-index our entire database?
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