AI and Automation
Ask Your POS Data Questions in Plain English
No SQL, no dashboards. Here is how Neura57 lets Malaysian retailers ask their POS and accounting data questions in plain English and get answers in seconds.
Most retail questions never get answered because building a report takes too long. Neura57 changes that. Its AI assistant, powered by Claude on AWS Bedrock with a Cube Core semantic layer, lets you ask questions about your sales, inventory, and trends in plain English and get a data-backed answer in seconds.
This post explains how it works and why the answers can be trusted.
The questions you actually have
Real questions sound like this:
- Why did sales drop at Subang Jaya last Friday?
- Which SKUs are growing fastest this month?
- Which outlet is overstocked on slow movers?
- How did the weekend promotion affect basket size?
These are simple to ask and hard to answer with traditional tools, because each one needs a fresh query or a new dashboard.
How the AI assistant works
You ask in plain language
You type the question the way you would ask a colleague. There is no query language to learn and no dashboard to configure.
A semantic layer keeps it accurate
This is the important part. Neura57 does not let the AI write raw SQL against your database. Instead, the assistant works through a Cube Core semantic layer that defines your metrics. The AI chooses from trusted, predefined measures, which keeps answers accurate and avoids the risk of a wrong query returning a confident but wrong number.
You get an answer with its source
The assistant returns a clear answer along with the data behind it, so you can see how the number was produced rather than taking it on faith.
Why the accuracy matters
A natural-language tool that occasionally invents numbers is worse than no tool at all, because people act on it. By routing every question through a governed semantic layer rather than free-form SQL, Neura57 trades a little flexibility for a lot of trust. The answer you get is the answer your data supports.
What you can do with it
- Investigate a drop or spike the moment you notice it
- Compare outlets, SKUs, and periods without building anything
- Hand non-technical managers a tool they can actually use
- Follow up with another question, because context carries over
Getting started
Neura57 connects to SQL Account, AutoCount, SAP Business One, and StoreHub, then exposes your data through the assistant. There is no migration, and your data is never used to train AI models.
Request a demo and ask your own data a few questions in the session.
The Neura57 Team
Retail Intelligence at 57 Codebox
The team behind Neura57 at 57 Codebox Sdn Bhd. We have built enterprise software and AI systems for Malaysian businesses since 2017, with hands-on work in AWS Bedrock, Cube Core semantic modelling, and machine learning forecasting for retail and F&B operators.