Nursultan

Start production ready RAG project-template with Bult.ai.

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We just released a production ready RAG project-template for Bult.ai.

If you want to deploy a serious Retrieval Augmented Generation system, not a toy demo, this is for you.

What it includes:

• Hybrid search combining BM25 and vector similarity
• Cross encoder reranking for higher precision
• Optional HyDE and multi query retrieval
• Multi model support: OpenAI, Anthropic, Google, Ollama
• OCR for scanned PDFs
• JWT authentication and optional Google OAuth
• Analytics dashboard with usage and latency tracking
• Conversation export to Markdown, JSON, and PDF
• Async background processing with job tracking

You upload documents.
Ask questions.
Get answers with inline citations and source scoring.

It deploys on Bult.ai using:

• GitHub based app service
• PostgreSQL
• pgvector

No Kubernetes. No infrastructure gymnastics.

This template demonstrates how to run production grade AI workloads on a PaaS with full control over architecture.

Full detailed tutorial and documentation:
https://docs.bult.ai/tutorials/tutorial-rag

If you're building AI products and need a strong RAG foundation, fork it and deploy in minutes.

Would love feedback from builders pushing RAG systems to real world scale.

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Hester Henry

They bundled hybrid search and reranking + multi-model support and auth or analytics, so you can ship a production QA system instead of wiring 20 pieces yourself. Big win is no Kubernetes just deploy, upload docs, and get cited answers with scoring.

Nursultan

@hester__henry Exactly

We wanted teams to skip the AI plumbing and start with a real production baseline.

Deploy, upload docs, get hybrid retrieval + reranking, cited answers, multi-model support, auth and analytics, all without stitching 20 tools or touching k8s.


Focus on the product, not the infrastructure.