The two-minute demonstration. Captions only, no sound.
How it works
Observe
An AI observer reads the configuration and every record people saved, including what they typed in free-text notes.
Propose
It proposes changes where the configuration does not fit the work: a field nobody fills in, information typed again and again in notes, a list value replaced by "Other". The evidence is counted from the records, not estimated by the AI.
Decide
A human accepts or rejects each proposal. When a new field is accepted, the AI fills it in from the existing notes. A rejected change is not proposed again.
Trace and undo
Every decision is recorded: what, why, who, when. Any accepted change can be undone, configuration and data included.
Why it matters
Configuring enterprise software has long meant projects, consultants and change requests, while the way people work moves on every day. When AI can see that gap and propose how to close it, configuration becomes a continuous, evidence-based process, and the decisions stay with the people accountable for the system.
The CRM is only the example. The same approach applies to any configurable enterprise application: its objects, fields, forms and rules.
About this demonstrator
The demonstrator runs on simulated data: the CRM of a sales team, with 510 records from fictional companies. The proposals, their reasons and the values filled in are real outputs of the AI.
It is built on Aigiban's approach to agentic AI: specialized agents, structured models, connectors and explicit workflows, with human judgment at every decision.
Let's talk.
Interested in what this could mean for your product or your organization?