FirstBatch

FirstBatch builds real-time personalisation infrastructure for AI applications around user embeddings: clicks, likes and purchases are encoded as high-dimensional vectors in a vector database, and an algorithm trades off exploration against exploitation to return a batch of candidates per session. An SDK and dashboard let developers send signals and request batches without building feature engineering, personalisation works through cold start without login or history, and the same user vector guides AI agents and retrieval for RAG.

It sits between models and business data, lighter than a recommendation vendor: it does not take over the content library, only the user vectors and retrieval logic, so it drops into existing apps and LLM workflows. The capability runs on whichever vector store the customer chooses, so pricing and latency at upstream components such as Pinecone and Weaviate shape deployment cost, and the edge comes more from tuning and integration than from owned infrastructure.