Fixed reports answer the questions somebody thought of when the report was designed. Dhruv AI reads your own device and audit history and answers the rest — and clusters the free-text remarks auditors write into recurring themes that no report column was ever going to reveal.
Dhruv AI is Improsys's own analytics layer. It runs against your database, it is read-only, and every answer points back at the records it came from.
Ask which area is behind, what keeps recurring, or whether you are ready for the next certification, without building a report.
Auditor remarks grouped into themes — obstructed routes, low pressure, missing signage — which no fixed column captures.
The same finding on the same device or in the same area, surfaced as a pattern rather than as unrelated notes.
How the current position looks against an upcoming certification date, and what queue needs clearing first.
Where the tail actually sits, and whether slow closures are a discipline problem or a vendor problem.
Whether missed rounds cluster around a person, an area or a time of month.
Dhruv AI cannot alter a record it reads, which keeps the compliance trail intact.
It runs against your own data, in your own deployment, rather than sending records to an external service.
Every response can be traced back to the records that produced it, so the answer is checkable.
A report answers the question its designer anticipated. Most useful compliance questions are not on that list.
Dhruv AI is Improsys's own AI and analytics layer, used across the Fast suite. In Fast Fire Audit it reads your device register, inspection rounds, findings and closures and answers questions about them in plain English. It is read-only: it can analyse a compliance record but never alter one.
It runs against your own database in your own deployment. That is one of the reasons the on-premise option matters to hospitals, pharma sites and defence-adjacent plants. Confirm the exact configuration for your deployment during the demo, since it differs between cloud and on-premise installations.
In practice, the free-text clustering. Auditors write remarks in their own words, and those remarks are where the real pattern lives — that obstruction accounts for a third of your findings, or that one shift produces most of the low-pressure reports. No fixed report column was ever going to surface that, because nobody designed a column for it.
It can be incomplete, in the same way any analysis of imperfect data can be. That is why every answer is traceable to the records that produced it, so you can check rather than trust. Treat it as a way of finding questions worth investigating, not as a substitute for the audit record itself.
No. The device register, planning, mobile execution, findings and dashboards all work without it. Dhruv AI is an analytics layer on top, useful once you have enough history for patterns to exist — typically after a couple of full cycles.
Yes. Dhruv AI is the shared analytics layer across the Fast suite, so the same capability appears in Fast Maintenance, Fast Quality and elsewhere, reading each product's own data.
Bring a question your current reports cannot answer. That is the fastest way to see whether this is useful to you.
