Vector Database & Embedding Architecture Partner

We run a 2-week technical spike where we prototype your core use case on 2-3 candidate platforms using your actual data. We measure query latency, indexing throughput, cost per query, and integration complexity, then deliver a recommendation with concrete numbers and a migration plan.

No. We work across the full ecosystem, Pinecone, Weaviate, Qdrant, Milvus, pgvector, ChromaDB for vector databases, and OpenAI, Cohere, Sentence Transformers, Google Embedding 2 for embedding models. We recommend what fits your requirements, not what we prefer.

Most engagements start with a 2-week evaluation phase (spike and recommendation), followed by a 6-10 week implementation phase covering architecture, integration, testing, and production deployment. We work alongside your engineering team, not as a black box.

Yes. We handle migrations between vector databases with zero-downtime cutover strategies. This includes re-indexing, parallel query routing during migration, performance validation, and rollback planning. We have migrated production systems with 50M+ vectors without service interruption.

That works too. We help teams evaluate and integrate new embedding models, including model benchmarking on your domain data, re-indexing strategies, dimension mapping, and quality regression testing. Many clients come to us specifically to upgrade from text-only to multimodal embeddings.