Search that matches on meaning, not just keywords — fast and accurate at scale, and almost always hybrid so you get precision too.
Keyword search only finds the words you typed. Ask for "ways to cut cloud spend" and it will miss a document titled "reducing infrastructure costs" because the words do not match — even though it is exactly what you wanted. For anyone searching real content, that gap between what people type and how documents are written is a constant source of frustration.
Vector search closes it by matching on meaning. Text, images and other content are turned into numerical embeddings, and search finds the nearest ones in that space — so a query and a document that mean the same thing land near each other even with no shared words. It is the retrieval engine behind semantic search, recommendations and most RAG systems.
Making it fast and accurate at scale is its own discipline: choosing the right embedding model, building an index that returns results in milliseconds over millions of vectors, and combining semantic with keyword search so you get both meaning and precision. Done poorly, it is slow and vague; done well, it feels like the system finally understands the question.
We start by choosing the embedding model that fits your content and languages — a decision that quietly determines search quality more than almost anything else, and one where the default is rarely the best. Then we design the index and the database for your scale and latency target, so results come back fast even over very large collections.
We almost always build hybrid search, combining semantic matching with keyword and metadata filtering, because pure vector search alone can miss exact terms that matter — a product code, a name, a precise phrase. We tune the whole thing against real queries from your users, so relevance is measured, not assumed.
Search that understands intent, not just matching words.
The retrieval layer behind a grounded AI assistant.
Find similar products, articles or media by meaning.
Detect near-duplicate records and content.
Search across images and text in one space.
Surface the right help article before a ticket is filed.
We define what good search means for your users and content.
We prepare and understand the content to be indexed.
We select the embedding model and design the index.
We build the hybrid search and ingestion pipeline.
We measure relevance against real queries and tune.
We deliver the search service with a tuning guide.
We choose the embedding model and vector database for your content, scale and latency, and almost always build hybrid search rather than vectors alone.
Not always. For many workloads pgvector on your existing Postgres is enough; at larger scale a dedicated store earns its keep. We choose based on your numbers.
It depends on your content and languages. We benchmark a few candidates on your data rather than defaulting to a popular name.
Yes. We select multilingual embeddings where your content or users need them.
Pure semantic search can miss exact terms like codes and names. Combining it with keyword search gives both meaning and precision.
Compact, efficient models that run at the edge — low latency, full privacy.
Privacy-safe training data at scale — augment, balance, and unblock ML.
Benchmark, stress-test and adversarially probe models before they ship.
Book a 30-minute call. We will tell you honestly whether we can help.
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