Embedding pipeline
We chunk your content on structure, choose an embedding model matched to your domain, and build the ingestion that keeps it fresh.
AI · Search
Vector search & embeddings — find by meaning, not keywords
We build semantic search, recommendations and the retrieval layer under your AI features — with the right vector database, embedding model and reranking for your data. Measured, not assumed.
By meaning
semantic, not literal
Right store
pgvector to Qdrant
Reranked
the top result is right
Keyword search misses "cancel my plan" → "ending your subscription."
Vector search finds results by meaning, so obvious matches stop slipping through. Done well it powers semantic search, recommendations and the retrieval behind every RAG assistant. Done badly it returns confident nonsense — the difference is chunking, the embedding model, and reranking.
What we build
The parts that decide whether it works.
Vector database
pgvector when you already run Postgres, or Qdrant/Weaviate/Pinecone at scale — chosen for your constraints, with the trade-offs explained.
Reranking & relevance
A reranking pass so the top results are actually the right ones — the single biggest lever on search quality, measured before and after.
Clean search API
A drop-in search endpoint you wire into your existing product — no rip-and-replace.
How we work
Scoped, shipped weekly, handed over clean.
01
Assess your data
We look at your content, volume and the queries that matter, and pick the store and model to fit — fixed estimate up front.
02
Build & measure
Pipeline, store and reranking shipped weekly, with relevance measured on real queries so quality is a number.
03
Integrate & hand over
A clean API into your app, monitored and documented, with readable code your team can own.
Where it fits
What teams hire us to build.
- Semantic search across your product or docs
- Retrieval layer under a RAG assistant
- Recommendations and “more like this”
- Deduplication and record matching
- Support-ticket and FAQ routing by meaning
- Image or product search by similarity
Questions
What clients ask before we start.
What is vector search and when do I need it?
Vector search finds results by meaning, not keywords — so "cancel my plan" matches a doc titled "ending your subscription." You need it whenever keyword search misses obvious matches: semantic search, recommendations, deduplication, or as the retrieval layer under a RAG assistant.
Which vector database should we use?
Depends on scale and stack — pgvector when you already run Postgres and want one less service, or a dedicated store like Qdrant, Weaviate or Pinecone at larger scale. We pick for your constraints, not the trendiest option, and tell you the trade-offs.
How do you make embeddings accurate?
The embedding model and the chunking strategy decide quality. We chunk on structure, choose an embedding model matched to your domain, and add a reranking pass so the top results are actually the right ones — measured, not assumed.
Can you add semantic search to our existing product?
Yes. We build the ingestion and embedding pipeline, stand up the vector store, and expose a clean search API you drop into your app — without ripping out what you already have.
Related services