Embeddings & Semantic Search
Your search box is where employees and customers tell you exactly what they want, in their own words — and where keyword matching tells them 0 results found because they typed "cancel my plan" instead of "subscription termination." Embeddings close that gap: search by meaning, not by string.
But semantic search alone is a half-measure. Pure vector retrieval misses exact identifiers — part numbers, error codes, names — that keyword search nails. Production-grade systems are hybrid: dense embeddings for meaning, BM25 for precision, a reranker to sort the union, and evaluation on your real query logs to prove the ranking improved. That full stack is what we build, and it's the retrieval backbone under every serious RAG and agent system.
Embedding pipeline design
Model selection, chunking, and refresh strategy matched to your content and languages.
Hybrid search
Dense plus keyword retrieval fused with reranking — meaning and precision, not one or the other.
Reranker integration
Cross-encoder reranking that turns decent recall into excellent top-5 results.
Search evaluation
Relevance metrics on your actual query logs, so improvement is measured, not asserted.
Cost and latency engineering
Quantization, caching, and batching that keep quality up and the bill down.
Which embedding model should we use?
The one that wins on your data — which we determine by benchmarking a handful of candidates on your documents and real queries. Domain vocabulary, languages, and cost structure change the answer; leaderboard rank alone doesn't survive contact with your corpus.
Semantic search misses our exact part numbers and codes. Why?
Because embeddings blur exact strings into meaning — that's their job. The fix is hybrid retrieval: keyword search catches identifiers, dense search catches intent, and a reranker merges them. This is the single most common gap we fix in existing systems.
How do we know the new search is actually better?
Offline relevance evals on labeled queries first, then online A/B measurement on click-through and task completion. We set the baseline before touching anything, so "better" is a number with a confidence interval, not a stakeholder's impression.
Put Embeddings & Semantic Search to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →