Your report does not need a better chart
The problem is almost never the visualisation. It is that the data was born outside the system, exported by hand and arrived stale. Here the operation runs on the platform, so the reading comes from inside, and it comes written.
Recurring reports, delivered by role, with nobody assembling a spreadsheet
Why the dashboard you asked for goes unused
The data arrives stale. Someone exports, cleans and publishes. By the time the number shows up, the decision was already made.
A chart does not decide. Seeing the line drop does not say what to do. The reading is missing, and the reading is senior work.
Every area has its own spreadsheet. Three versions of the same indicator, and the meeting turns into an argument about which number is right.
Nobody opens it. A dashboard that needs someone to log in and interpret ends up unopened. What works is what arrives ready.
Analysis and a recommendation, not just the number
The report is generated on the schedule you set, grouped by your own org structure and sent to the people or roles you choose. It brings the consolidated view, who fell outside the curve, and what the AI recommends looking at first.
Schedule and recipients
Daily or weekly, at the time you set, to people or to a role.
Grouped by your structure
By department, sub-department, or a grouping you define yourself.
Who fell off the curve
Outlier detection with severity levels, instead of a table for you to dig through.
Alerts when it matters
Low output, inactivity and idle time become a notice, not a month-end discovery.
From an event in the operation to a recommendation in your inbox
Nothing here depends on someone exporting. The path is short because it starts inside the system where the work happens.
That closeness is what allows the recommendation to exist. When data is exported, the context (who, at which step, against which deadline) is left behind, and without context there is no recommendation, only a chart.
The path
The work happens
A step is completed, an hour is logged, a document is signed. The event is already data.
The indicator moves
No nightly load and no spreadsheet: the indicator reflects what just happened.
The AI reads the whole
It compares against the previous period, the target and the rest of the structure, and writes the analysis.
It reaches the decision maker
On the agreed schedule, to the right role, with a recommendation of what to look at first.
Not everything needs AI: cascading indicators, targets and periodic check-ins are rules, and rules are auditable.
What makes up the intelligence
Three fronts leaning on the same operational data.
TALI
The AI that reads the modules and answers about the operation in conversation.
Productivity and capacity
Where process capacity is leaking, with reports and alerts.
Performance
Cascading indicators and objectives, with check-ins and trend.
Surveys
Climate, NPS and forms, with results tied to the org structure.
Where this layer changes the conversation
- Board meeting: Instead of arguing whose number is right, discussing what to do about it.
- Efficiency targets without hiring: Finding where capacity leaks before concluding that people are missing.
- Distributed teams: Seeing the operation by area and by process, without turning it into surveillance of individuals.
Questions about the intelligence
Does this replace Power BI?
Can I build my own dashboards?
How do you keep this from becoming surveillance?
See the report with a slice of your own company
In 30 minutes, using your structure, we can show how the reading would arrive.