Choose the store
Managed vs self-hosted, query patterns, and budget drive whether we pick Pinecone, pgvector, or another engine.
What We Deliver
- Vector DB selection and architecture
- Embedding pipelines and re-indexing
- Hybrid search (BM25 + vectors)
- Metadata filtering and multi-tenancy
- Pinecone, pgvector, Weaviate, Qdrant setups
- Latency, recall, and cost tuning
Our Process
Index design
Chunk sizes, embedding models, namespaces, and metadata that match how you query.
Ingest & evaluate
Batch + incremental indexing with recall/latency benchmarks on your corpus.
Operate
Monitoring, capacity planning, and re-embedding when models change.
Technologies
- Pinecone
- pgvector
- Weaviate
- Qdrant
- OpenAI
- LangChain
- PostgreSQL
Outcomes You Can Expect
- Semantic search that finds the right docs
- A vector stack sized for your traffic
- Clear multi-tenant isolation
- Predictable indexing and query costs
Why Choose Intellixy
- Experienced engineering team with startup and enterprise delivery
- Agile sprints with transparent progress and staging access
- Clear scope, milestones, and pricing before development starts
- Post-launch support, monitoring, and feature iterations
Industries We Serve
HealthcareE-commerceEducationLogisticsStartups
Frequently Asked Questions
Do I need a separate vector database?
Not always. pgvector on PostgreSQL is enough for many products. Dedicated stores help at larger scale or when you need specialized features.
Which vector database should we use?
We recommend based on scale, ops maturity, and budget — often pgvector to start, Pinecone or Weaviate when managed scale matters.
How does this relate to RAG?
RAG retrieves relevant chunks from a vector (and often keyword) index before generation. A solid vector database is the backbone of accurate RAG.
Related Services
Ready to start your project?
Tell us about your vector database goals. We respond within 24 hours on business days with a tailored plan.