Use Case Solution
Search that understands what people are actually looking for.
Semantic search using vector embeddings and LLMs. Find the right result even when the query does not match the keywords. The search experience your users expect from 2025.
The Problem
Sound familiar?
Your search returns exact keyword matches and nothing else. Users search for contract and get nothing because your documents say agreement. They search for how do I cancel and get nothing because your docs say cancellation policy.
Our Solution
Here is how we fix it.
Semantic search using vector embeddings. Your content is indexed as embeddings that capture meaning, not just keywords. A query for cancel my account matches cancellation policy even though those words are not identical. Powered by pgvector for simple deployments or Pinecone for large-scale applications.
What is Included
What we build
Semantic Vector Search
Content indexed as embeddings using OpenAI or open-source models. Queries match by meaning, not just keyword presence.
Hybrid Search
Combine vector similarity with traditional keyword search and apply re-ranking. Best of both worlds for most applications.
RAG for Q&A
Retrieval-augmented generation that grounds LLM answers in your actual content. Accurate answers, not hallucinations.
Tech Stack
Timeline
From kickoff to production deployment
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