Real screens from the Tenuris app running on a synthetic water treatment plant dataset: 4,218 work orders, 34 assets, 18 technicians, 4 years of history.
Tenuris ingests a CMMS export and surfaces knowledge risks ranked by severity. Each card shows the failure mode, asset, risk type, and the recommended next step.
Click into any issue to see why Tenuris flagged it: which expert, how much faster they resolve, confidence level, linked work orders. The action panel suggests what to do next.
Nothing becomes operational guidance without human sign-off. Propagation alerts flag contradictions. Pending entries show AI-structured drafts ready for approval, edit, or rejection.
Is the organization getting smarter or leaking knowledge? Net knowledge position, capture rate, governance throughput, placement rate, staleness, and expert concentration — one view.
Assets, experts, failure modes, issues, and entries connected in a relationship structure. The headline: who holds concentrated knowledge, and what breaks if they leave?
When technicians describe what they actually did in work orders, Tenuris compares it to the documented procedure. Drift percentages flag where the field story and the book diverge.
This demo uses a synthetic dataset modeled on a mid-size water treatment plant. 4,218 work orders across 34 assets and 18 technicians, spanning Jan 2022 – Dec 2025. No real client data is shown. Your 48-hour diagnostic uses your data and returns findings specific to your plant.