TotalApp Docs

Dashboard AI Assistants — Insight Generator

Natural-language executive summaries, root-cause and anomaly detection, and actionable recommendations for chart-heavy dashboard screens.

The Insight Generator

Dashboard screens don't have rows to select — they have charts and metrics to interpret. The Insight Generator is the AI Assistant built for exactly that: instead of acting on data, it reads the dashboard's charts for you and turns them into plain-English findings. It runs on three pillars — a natural-language executive summary, root-cause and anomaly detection, and actionable recommendations — and it is arriving across TotalApp's chart-heavy screens now, starting with the MTBF/MTTR Reliability Dashboard.

Screen preview

A quick look at the screen. Use the toggle in the corner to switch between the dark and light themes.

app.totalapp.app/maint-reliability
Maintenance
MTBF / MTTR Reliability Dashboard
575h
Avg MTBF
397h
Lowest (A1)
22
Interventions
MTTR by Quarter
Q1Q2Q3Q4
AssetMTBF
CNC Machine A1
397h
Boiler System H8
462h
Conveyor C3
588h
Hydraulic Press P2
640h
Compressor K7
701h
Insight Generator
Reliability Dashboard
✕
Executive SummaryAvg. MTBF 575h (above target) — CNC Machine A1 the weak point at 397h
Root CauseMTTR spiked in Q3, driven mostly by Boiler System H8
RecommendationShorten Boiler H8's PM interval to 2 weeks — est. 15% lower MTTR
Ask about MTBF, MTTR, assets...

Insight Generator on a reliability dashboard

1. Natural-Language Executive Summary

Instead of manually reading every chart on the dashboard, the Insight Generator produces a short, plain-English summary of what the numbers say right now.

Example — MTBF/MTTR Reliability Dashboard

"Over the last 12 months your average MTBF is 575h, above target — but CNC Machine A1 is the weak point at only 397h." One sentence replaces a full read-through of the reliability charts.

2. Root-Cause & Anomaly Detection

When a metric spikes or drops, the Insight Generator doesn't just flag the change — it explains which equipment, asset group, or segment is actually driving it, in context.

Example

"MTTR increased sharply in Q3 — driven mostly by the Boiler System H8 asset group." Instead of a lone chart showing a spike, you get the asset group responsible for it.

3. Actionable Recommendations

The Insight Generator closes the loop with a concrete next step tied directly to the data — not a generic tip, but a specific change with an estimated impact.

Example

"Boiler System H8 had 22 interventions in the last 12 months. Shortening the preventive maintenance interval to 2 weeks could reduce average MTTR by roughly 15%." A quantified recommendation, not just an observation.

Rolling Out Across Dashboards

The Insight Generator is arriving across TotalApp's dashboard screens, starting with:

Maintenance — Reliability Dashboard

MTBF/MTTR trend interpretation, asset-level root cause, and preventive-maintenance recommendations.

Manufacturing — OEE Dashboard

Availability, performance, and quality trend summaries with line- and shift-level root cause.

Other chart-heavy screens

Additional analytics dashboards across TotalApp are being wired up to the same Insight Generator pattern.

A note on paths and naming

Each dashboard's Insight Generator is scoped to that dashboard's own screen and its own metrics. Internally, a dashboard's path stays short and domain-meaningful — like the Reliability Dashboard's maint-reliability — rather than a generic /dash-style shorthand, so the assistant's scope is always obvious from the URL itself.

Frequently Asked Questions

Which dashboards have the Insight Generator today?
The MTBF/MTTR Reliability Dashboard in Maintenance Operations is the first live screen. The Manufacturing OEE Dashboard and other chart-heavy analytics screens are being wired up next as part of the ongoing rollout.
Does the Insight Generator change my underlying data?
No. Unlike the Table & List Assistant, the Insight Generator is read-only — it interprets charts and metrics and produces summaries, root-cause explanations, and recommendations, but never edits records itself.
How does root-cause detection know which asset or segment is responsible?
It correlates the metric that changed with the underlying breakdown data behind that dashboard's charts (e.g. per-asset MTTR figures) to identify which specific group is driving the overall trend, rather than just flagging that a change occurred.
Are the recommendations generic tips or specific to my data?
They're tied directly to the dashboard's own numbers — for example, a specific asset's intervention count and an estimated MTTR reduction if a maintenance interval is shortened, not a generic best-practice tip.
Which Writer Engine powers the Insight Generator?
The same Agentic configuration used across TotalApp — Ollama, In-Browser (ONNX/WebGPU), or Cloud LLM, set in Settings → Agentic. See AI Assistants → Overview for how this is shared with Table & List Assistants.