Acumatica Hackathon 2025: PRESENT & WIN!
Sunday morning. 4 hours left for the presentation!
It was all hands on deck to finalize the presentation. I made some tweaks to the LLM, just in case it forgot everything overnight, and tried to polish off a few rough edges (mostly trying not to sweat). When it came time to present, Brian masterfully set the stage with humor and a skit. I dove in for the demo of our Narrow AI Agent.
One judge challenged us. “Can your AI do the same with Shipment emails?” “No. But, want to know our secret? It only took 20 emails! If I got my hands on 20 emails of shipment requests it would do the same”.
All of the other projects were so incredible. And our model could simply not do as much as we wanted it to do. It truly shows some of the brilliant innovation out there. Our team joked that we should have just called ourselves, “Narrow AI”.
And it should be no surprise we were honored, shocked and thankful that our Narrow AI solution took 1st place!
If I were to build the solution again: Future Improvements
We accomplished a lot in very little time, but there is always room to grow. Here are the ideas swirling around in my head:
- More Data: Always!
- At the first step of the workflow, AI agent should classify an email as “Sales Order”, “Quote”, or “Shipment”. It would make space for us to process more specialized actions
- JSON was a mistake. If we are to build an army of narrow AI agents we need to pass simple business information between them.
- RAG (retrieval augmented generation). Our solution could leverage an additional AI model to look through our database. And instead of asking the model in our prompt… “If unsure of this information return GUEST or NOTFOUND”… we would ask the database model, “Find a valid customer number in the database with an email similar to X”.
Deeper Knowledge: What Is Rag?
Retrieval-augmented generation (RAG) is a technique that brings external knowledge sources into the AI model. In short a model needs a source of data to reason upon. Using a technique of retrieval and having your agent query the database you are giving your narrow AI agent real time info on your product offering, services, business customers or anything else that can be stored in your company database.
Acumatica Hackathon 2025 Final Thoughts
This hackathon was an absolute rollercoaster. I walked away feeling grateful that the team and I solved all the challenges that were put before us. We’ve proven a concept and shown that implementing an AI agent directly in the business logic layer of Acumatica is both possible and a fantastic avenue of optimization for all organizations.
We took what seemed like a “black box” and have given it a set of predictable and replicable tools that make narrow ai models impactful to any business. And in the end we have a tool to solve real problems.
So here’s the question… why wouldn’t you implement a narrow AI agent to start to handle those annoying time-consuming data tasks in your office? I will leave that for you to ponder over. And, of course, don’t hesitate to CONTACT ME if you have your own Narrow AI dreams you want to chat about. 😉