AI/ChatGPT Strategy for ERPNext

A Journey Through Failed Experiments (and What Finally Appears to Be Working)

In this blog, Our consultant shares a deeply honest account of what it really takes to build a trustworthy AI agent for ERPNext—beyond demos, hype, and million-view tutorials.

When I see many friends working day and night to build an AI chatbot for ERPNext, I feel a strange mix of admiration and quiet sadness. Admiration — because they are genuinely smart, driven, and technically strong. Sadness — because I recognize the road they are walking on. I’ve walked it already. Slowly. Painfully. Often alone.

This work wasn’t a side project or a weekend experiment. It was done for real clients, with real data, real expectations, and very little tolerance for hallucinations. And after many months of failed experiments, broken assumptions, and uncomfortable truths, I feel an urge to speak — not to discourage anyone, but to save them from losing a year learning the same lessons the hard way.

This is not a success story. It’s a log of broken assumptions, failed architectures, and a faint sense that something — at last — might be working.

Read full article here

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Congrats on hitting the 90% mark… next milestone 99% by Dec 2026

Building an AI Agent for ERPNext

As part of our open-source chatbot initiative for Frappe ERPNext, this video kicks off the “Building an AI Agent for ERPNext” series.
We cover training your own models, running a self-hosted AI server, fine-tuning, building a chatbot UI, managing repositories on Hugging Face/GitHub, and collaborating with the community.

Also, we are doing Ollama and replicate installation, use QWEN model installations on this video

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Episode 2: Contextualizing Data with RAG | Solving AI Hallucinations in ERPNext
Watch it https://youtu.be/jcwVCJ1-ltc

Key Topics Covered:

  • The fundamental logic of Transformers and Attention.
  • Why generic LLMs fail with private ERPNext apps and custom fields.
  • Using RAG to ground AI in master table data and specific schema.
  • The difference between processing transactions and providing context.
  • How embedding models like Nomic-AI manage mathematical vectors.
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