A managed vector database and semantic search API
A Pinecone-style platform that lets developers index millions of embeddings and run low-latency semantic search behind a clean API.
The challenge
Teams wanted to add semantic search and retrieval-augmented generation (RAG) to their products without standing up and operating their own vector infrastructure. They needed a simple API, predictable latency at scale, and an SDK their engineers could adopt in an afternoon.
Our approach
We designed the API surface first — index, upsert, query — then built the storage and retrieval layer to meet a strict latency budget. A dashboard and SDK were built alongside the API so the developer experience was tested from day one, not bolted on later.
What we built
- REST API for creating indexes, upserting vectors, and querying
- Low-latency approximate-nearest-neighbour retrieval layer
- Client SDK and quickstart so teams integrate in minutes
- Usage dashboard with API keys, metrics, and billing
- RAG reference implementation wiring search into an LLM
The results
- Consistent sub-120ms queries across millions of vectors
- End-to-end developer onboarding in under 15 minutes
- Reusable RAG pattern adopted across multiple downstream apps
- Clean separation of API, storage, and dashboard for easy scaling
"The API and SDK felt production-ready immediately. We shipped semantic search into our product the same week."