We Achieved Low-Cost AI Agent Integration with Postgres and JavaScript (orkhanscience.medium.com)

A team built a cost‑effective retrieval-augmented AI chatbot and agent framework by reusing existing infrastructure: Node.js/React apps, AWS-hosted Postgres with the pgvector extension, a JavaScript AI SDK, and access to Anthropic/OpenAI models (with Ollama/local models as a lower-quality free option). In a 2–4 week proof of concept they stored embeddings in Postgres (pgvector on RDS), used vector search to supply context to LLMs, and delivered coherent, context-aware responses—proving that most enterprise use cases can be met without heavy new investments. Technically, their recommended template is straightforward: AWS + Postgres (CREATE EXTENSION vector), ORM Drizzle, Node.js backend and React frontend, plus the JS AI SDK for integration and model access via an internal AI gateway or external APIs. A key component was a natural-language interpreter that maps informal user queries to firm-specific terminology so the agent can correctly query structured data. The result is a pragmatic, scalable RAG pattern that reduces cost and development time while enabling rapid integration of AI agents into existing products—ideal for small-to-medium enterprise applications where simplicity and reuse matter most.