Embeddings and indexing
Choose an embedding model, split documents into useful passages, and store them with metadata in a vector database.
02 / Vector search & databases
Find what matters in your data.
We build search and retrieval systems that match by meaning and bring relevant source material into AI applications.
Discuss your projectSee how retrieval works
One search system. Different uses.
Explore how semantic search, retrieval-augmented generation and matching use related information in different ways.
Retrieval workbenchIllustrative example
QUERYCan I return an opened item?
- 01Returns policy · Opened items
- 02Returns eligibility · Exceptions
- 03Support guide · Return process
Retrieve by meaning across product policies, even when the query uses different words.
What we build
From documents to useful results.
Search and ranking
Combine similarity search, keyword matching, filters and reranking based on how people search your content.
Measure retrieval quality
Test representative queries and measure relevance and latency. Use the results to decide what to tune.
Start a conversation
What do people need to find?
Tell us where your model or search system falls short. We’ll help you define a focused first project.