AI Glossary
Embeddings
Numerical representations of text that capture semantic meaning. Embeddings let AI systems understand that "reduce headcount" and "cut staff" mean similar things, enabling smarter search and classification.
Understanding Embeddings
Embeddings convert text (or images, audio) into dense vectors of numbers that capture meaning. Similar concepts end up close together in this vector space, enabling AI to understand relationships that keyword matching misses.
For businesses, embeddings power semantic search (finding documents by meaning, not just keywords), recommendation systems, duplicate detection, and the retrieval step in RAG pipelines.
Embedding models are relatively inexpensive to run compared to full language models, making them a cost-effective way to add intelligence to search, classification, and matching workflows.
Embeddings in Canada
Canadian companies building embedding-based search systems should consider bilingual embeddings that work across English and French content simultaneously.
Related Services
Frequently Asked Questions
Semantic search across documents, finding similar customer inquiries, product recommendations, deduplicating records, and powering RAG systems that ground AI responses in your company's data.
Embedding models are very affordable — typically $0.01-0.10 per million tokens. This makes them practical for indexing large document collections and running high-volume search queries.
See Embeddings in Action
Book a free 30-minute strategy call. We'll show you how embeddings can drive real results for your business.