AI stacks with too many moving parts
Separate vector DBs, sync pipelines, and external inference APIs add latency, cost, consistency bugs, and a second security model to maintain — and they send your data off-host.
AI & Data Engineering · Open Source
AI inside PostgreSQL — embeddings, RAG, and agents callable from SQL.
PostgreSQL AI Edition (pg_ai) puts embeddings, semantic search, RAG, and agents directly inside the database — with local inference. No API key. No second vector database. One docker compose up.
Delivery Logic
How Develone frames the business outcome.
A native engine that makes AI a first-class SQL citizen
A C engine (pg_ai_core) with planner hooks, plus PL/pgSQL functions for embeddings, RAG, filtered search, and ReAct-style agents — all driven by local models through Ollama and an async background worker.
Fewer systems, stronger guarantees, data on-host
Embeddings stay transactional with their data, security is inherited from Postgres roles and RLS, and inference runs locally — so multi-tenant AI features ship without a second database or an API key.
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