02 / 04
AI that warns and suggests
For a business that already has data, such as orders, stock, or equipment, and still decides by experience. We pick one decision that keeps coming back, let the AI learn from that history, and put the suggestion on the screen people already use.
- Python
- PyTorch
- PostgreSQL
- FastAPI
How we do it
The first job is not choosing technology. It is writing down what the AI suggests, to whom, when, and what counts as a wrong suggestion.
The AI learns from data the company already has. If there is not enough of it, or it is messy, we say so and narrow the job.
Each suggestion comes with a one-line reason. Staff accept or ignore it in seconds, and that choice helps the AI do better next time.
What changes
- Know before a job is about to run late
- Some of your best people's judgment stays in the system
- A bad suggestion is easy to see
Three steps
- 01
Choose the job
Write down who receives the suggestion, when, whether they may ignore it, and what a wrong one would cost.
- 02
Learn and test
Let the AI learn from real history, then compare it with the decisions people already made.
- 03
Put it to work
Show the suggestion on the screen people already have open. Watch the results, and narrow the scope if you need to.