Technology
pgvector
pgvector implementation for production software delivery with clean architecture, maintainability, and predictable rollout.
Best For
Ideal use cases
Teams already standardised on PostgreSQL
Products needing a simpler vector search setup initially
Workflows where Postgres operations and backups are already mature
What We Build
Projects we deliver
pgvector setup with schemas and indexes
Embedding storage and update workflows
Query patterns with filters and performance tuning
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
Semantic search in existing apps
RAG assistants with moderate scale needs
Multi-tenant systems with Postgres-based isolation patterns
Delivery
How we deliver
We validate performance and scale limits early to avoid surprises.
Retrieval quality is tuned with chunking and metadata patterns.
Operations follow your existing Postgres practices for reliability.
FAQ
Frequently asked questions
Often yes for moderate scale, especially when Postgres is already core. For very large workloads, a dedicated vector DB may be better.
Yes. We can pair pgvector with keyword search layers and reranking where it improves relevance.
Yes. We implement migrations, indexes, and performance tuning with rollback-safe workflows.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus a fixed-scope gig to start quickly.
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