Dhruv AI is the Fast Suite's own AI and BI module, wired into Fast Maintenance Software. It puts a live maintenance role dashboard on your breakdown, downtime and work-order data, writes insight summaries for you, answers plain-English questions straight from maintenance history through a read-only security sandbox — and clusters thousands of free-text breakdown-cause remarks into the handful of failure themes that actually keep recurring, ranked by machine and line.
Dashboards answer the questions you thought of when they were built. Dhruv AI covers both halves: a live maintenance role dashboard for the questions you ask every day, and a plain-English chat for the ones you've never asked before.
Dhruv AI never touches your raw maintenance records. Questions are answered through pre-defined analytical views, and every AI-drafted query is validated before it runs.
Everything runs on the breakdown, downtime and work-order data you already capture in Fast Maintenance Software — no exports, no separate data warehouse project.
The floor already runs on breakdown tickets, PM schedules and work orders. Dhruv AI puts the management layer on top: a maintenance head sees breakdown frequency and downtime; a reliability engineer sees MTTR and MTBF by machine; a plant manager sees PM compliance and spare consumption — all reading the same live data as the MTTR/MTBF dashboards.
Reading a dozen charts before a maintenance meeting is work someone always skips. One click asks Dhruv AI to summarise the dashboard in front of you into short written insights — where downtime is shifting, which machine is driving breakdowns, where MTBF is falling and which PMs are slipping against the maintenance calendar.
Letting an AI near your maintenance database is a fair worry — so Dhruv AI is built to make writes impossible, not just unlikely. Your question becomes a query against pre-defined analytical views. Before it runs, the sandbox checks it: read-only statements only, whitelisted views only, no write or admin keywords, no stacked statements, comment tricks stripped. Fail any check and the query never executes.
Breakdown-cause remarks are free text — every technician words the same failure differently, so spreadsheets never add them up. Dhruv AI clusters similar remarks together and generates a readable label for each group. And because the view carries the machine and line, each theme tells you not just what keeps happening, but where — which machine and which line — so the breakdown review starts from the real Pareto, not a hunch.
Breakdown frequency, downtime by machine, MTTR/MTBF trends, PM compliance and spare consumption — over live breakdown and work-order data.
One-click written analysis of the dashboard you're viewing — what moved, where downtime crept up, which machine is slipping — regenerated whenever the data changes.
Questions about maintenance history answered as data plus a written summary — no report writer, no SQL, no waiting on IT.
Free-text breakdown-cause remarks clustered into recurring themes with AI-generated labels, ranked by machine and line — the Pareto view of what keeps failing.
Failure themes carry the machine, the MTTR/MTBF trend and the downtime hours — so analytics reflect the impact on uptime, not just the symptom.
A security sandbox validates every AI-drafted query — SELECT-only, whitelisted analytical views, blocked write keywords, encrypted connection credentials.
Bring a real question from your last maintenance review. In a 30-minute demo we'll connect Dhruv AI to sample maintenance data and answer it in front of you.
