Jrsinnovation

A cross-utility, segment-level water-main failure corpus assembled as a byproduct of deploying the prediction platform: for every pipe segment, feature rows (material, install vintage, diameter, joint type, manufacturer, soil conditions, pressure behaviour) joined to ground-truth outcome labels (broke / did not break, failure mode, emergency vs planned repair, replace vs repair) drawn from client break history and work orders. The commercially interesting object is the pooled view — one municipal utility's break history is too thin to train on, so only an integrator deployed across many utilities holds enough labelled failures to make a robust model, and that pooled corpus is the scarce asset.

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Coverage

Universe, instruments and categories.

Industry: Water UtilitiesApplication Software
Instruments: equities
Categories: Alternative

Dataset card

Type: not stated
Format: Tabular asset-register and event data — per-pipe-segment feature tables (material, vintage, diameter, joint type, manufacturer, soil class, pressure behaviour) joined to break/work-order event logs; plus time-series SCADA pressure records and free-text maintenance/work-order descriptions.
Volume: ~250K-2.5M labelled pipe-segment records pooled across client deployments (municipal distribution networks typically run 1,000-10,000 km of mains, i.e. roughly 50K-500K modelled segments per utility, each carrying asset attributes and a failure outcome per period), plus ingested SCADA pressure series and work-order text per client.
Users: N/A — B2B municipal-platform vendor with no consumer registration; counterparties are water utilities and the relevant base is client deployments plus modelled pipe segments, not accounts.
History: 30 years
Update frequency: not stated
Growth: Active — the platform page is live with current-generation AI positioning and the firm trades as an active digitalisation integrator (~56 staff, Alpharetta GA). No release-cadence or hiring evidence was retrievable, so this rests on current product marketing rather than observed shipping cadence.
Launched: ~2018-2020 for the parent IT-services firm (company-record signals place the registered entity's emergence around early 2020 at ~56 staff); the iFactory product arm and specifically this water-infrastructure platform are later still, roughly 2022-2024, inferred from current-generation machine-learning positioning and the absence of any earlier reference to it.
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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