Add a widget. The agent learns to fill it right.
The hard part of BI was never the chart — it's getting the number right against your own systems. In ABBI, every widget you create comes with an agent that automatically trains itself to fill that widget correctly from your enterprise data. You define what you want to see; it learns how to produce it accurately.
A generic agent guesses. Yours shouldn't.
Drop a general-purpose AI on your data and it will confidently pull from the wrong table, miscount what "active" means, or invent a plausible-looking figure. For a board people make decisions on, plausible isn't good enough.
ABBI closes that gap automatically. Behind each widget, an agent learns your specific task — which source is authoritative, how your business counts, what a correct answer looks like — until it fills that widget right, every time.
Generic agent: counts every signup ever — wrong table, wrong definition.
Trained ABBI agent: 30-day active, from the billing system, excluding trials — your definition, your source.
Define the widget. The rest is automatic.
There's no separate training step you run. Creating the widget is what starts it.
Say what it should show — "Q1 revenue by region", "open high-risk shipments". In plain terms.
It explores your connected systems to find where the real answer lives and how it's structured.
It practices producing the value and checks itself against the evidence until the result holds up.
As your data and definitions shift — or you correct it once — it adjusts and keeps the widget accurate.
Why the numbers hold up.
The agent learns the authoritative source for each widget, not whatever it found first. The figure traces back to a real system you trust.
"Revenue", "active", "region", "at risk" — it learns how your business defines them and counts the way your finance and ops teams do.
Integrity verification is the training signal: a value only counts as right when it's backed by evidence. So training pushes toward truth, not just confidence.
You skip the part that usually takes months.
There's no data-modelling project, no prompt-engineering effort, no hand-mapping every metric to a table. The training that makes the agent accurate on your data happens on its own, per widget, the moment you ask for it.
It trains itself per widget.
You describe the outcome, not the method.
It finds the right source for you.
Correct it once; it remembers.
See a widget train itself on your data.
Define what you want to see — watch the agent learn to fill it correctly.