Most leak systems use one rule for all the buildings they're installed in. The result tends to be either too many false alarms during normal use, or missed events when the rule is loosened to keep the system quiet. Silviri AI learns each building's normal water-use pattern, then alerts you when something doesn't match it — with fewer false alarms during ordinary use and better detection on the events that matter.
Most leak detection systems work like this: a fixed threshold, the same threshold for every building all the time. "If water flow goes above X gallons per minute, send an alert." It's simple to ship and easy to demonstrate. It also tends to underperform once it's in a real building, because every apartment building is different.
A 200-unit luxury high-rise uses water nothing like a 60-unit garden-style. A 1970s building with old galvanized pipes flows differently than a 2015 building with PEX. A property with morning irrigation looks nothing like one without. A single rule can't cover that range — so the system either over-alerts during normal use, or sits quiet through leaks it should have caught.
Set the rule tight enough to catch the maximum number of real leak events, and the system fires routinely during normal use — morning showers, sprinklers, pool fills, bathtubs filling. Maintenance teams gradually stop responding. By the time a real leak comes in, the alert may not be acted on quickly enough.
Loosen the rule to suppress false alarms, and slow leaks slip through. A toilet running all night at 0.3 gallons per minute can cost thousands of dollars per year — and is invisible to a system tuned only for catastrophic flow. Such a system also has trouble distinguishing a real pipe burst from a bathtub filling.
A rule tuned for a 200-unit high-rise is wrong for a 60-unit walkup. A rule for 7:30am is wrong for 3:00am. A rule that works in summer fails in winter. Vendors can't ship one rule book that works for every building, so they ship a compromise that works imperfectly across all of them.
With a fixed-rule system, the vendor has to choose where to set the threshold. Set it tight enough to catch slow leaks, and the system fires routinely during normal use. Set it loose enough to stay quiet, and slow leaks slip through unnoticed. Both ends of the choice cost the building owner something — just in different ways.
Silviri AI starts by quietly observing your building. What's normal here? When do residents shower? When does irrigation run? What does the building's water signature look like across a month, a season? Once the model has a stable baseline, it can flag deviations — without firing on the routine patterns of normal use.
It's the difference between a fixed threshold and a learned one. And it allows results that fixed-threshold systems struggle to deliver simultaneously.
What looks like an "anomaly" in a one-size-fits-all rule book may be perfectly normal in your building. What looks normal at 7:30am can be an anomaly at 3:00am. A learned baseline reflects your specific patterns. Your team gets fewer alerts overall — and the ones they do get are more likely to be real.
A small continuous trickle that hides in normal usage at a high-volume building stands out in a small one. Silviri scores it as a deviation from your baseline, not as noise lost in an industry average. So slow drips, running fixtures, and dying water heaters can be flagged earlier than fixed-rule systems typically allow.
Every Silviri install starts with conservative basic protection running on day one — your building is covered the moment the sensors are up. As the system learns your specific patterns, the AI takes over more of the work.
Every building has its own water personality — its own mix of resident habits, occupancy rhythm, daily and seasonal patterns. That mix is unique to your property, and it's exactly what off-the-shelf systems can't see.
Silviri keeps adjusting to how your building actually operates. Things change — a new tenant moves in with two kids, the irrigation schedule changes, a unit gets a new dishwasher. The system updates with those changes instead of fighting them. Result: fewer false alarms during normal life, faster catches when something's actually wrong.
How sharp the system gets depends on how much it can see. The Detect tier gives us water/no-water signals. The Optimize tier gives us continuous flow data — that's where the system really starts learning.
We don't slap "AI" on the brochure as a marketing word. The AI does specific work, on specific problems, where it produces a measurable improvement. Here's where.
Half-hour call. We'll walk through how Silviri would actually run on your specific property — including what the AI does (and doesn't do) at each tier. No sales pitch.