AI scores every job, crew, site, and piece of equipment for risk - ranked by consequence, not just probability. Proactive, not reactive.
Risk in most operations is managed by gut feel and past experience. A foreman knows which jobs tend to go sideways. A supervisor knows which crew pushes too hard. A maintenance manager knows which equipment is overdue for attention. But gut feel doesn't scale, doesn't document, and doesn't give you a ranked list of what to address this week.
TMI's Risk Scoring system quantifies what experienced people already sense - and extends it across the entire operation. Every job, crew, site, and asset scored continuously against historical incident data, sensor streams, weather, crew certification records, and cost burn patterns. Not a dashboard full of raw readings. A ranked list of what's at risk, ordered by consequence and probability, with the context needed to act.
When a combination of factors - an overdue inspection, a crew on their third consecutive long shift, and a weather event - converges on a single site, the system surfaces it before anyone's phone rings about a problem. Risk managed before it becomes an incident. Not reconstructed from it.
Production rates, pressure readings, crew certifications and hours, equipment maintenance history, cost burn, weather data, and safety event logs - all ingested and analyzed continuously. Not a single data point in isolation. The system looks for the combinations of signals that precede incidents in your historical data and surfaces them before they compound.
Every identified risk scored on two dimensions: probability of occurrence and consequence if it happens. A high-probability, low-consequence event ranks below a low-probability, catastrophic one. The system surfaces what actually deserves attention today - not what's merely statistically common. The ranked list is actionable, not overwhelming.
When a risk threshold is crossed, the alert goes to the person with authority to act on it - not a generic notification to a general inbox. The alert includes the contributing factors, the recommended intervention, and the consequence window if no action is taken. Decision-ready information, not raw data requiring interpretation.
Real-time operational monitoring paired with pattern-recognition across the full asset fleet - the combination that surfaces risk before it becomes a consequence.
A real-time monitoring system across your full operational data stream. Production rates, pressure readings, crew movement, cost burn, and safety events watched simultaneously. Anomalies ranked by severity and routed to the right person before they compound into incidents.
A continuously running model that watches sensor streams, work order history, and usage patterns to surface equipment failure risk before failure happens. Not threshold alerts. Pattern recognition across the entire asset fleet, ranked by consequence, not just probability.
Risk models built and trained on your specific operational history, incident database, and regulatory environment. Generic risk scores replaced with models that reflect how your operation actually fails.
Integrity failures, pressure events, and equipment anomalies have safety, regulatory, and production consequences that are severe and immediate. Risk scoring across the full asset portfolio - ranked by consequence - gives operations teams the prioritized list they need before anything on it becomes an emergency call.
Every active job or site is a combination of resources, people, conditions, and schedule pressure that produces a unique risk profile. The system scores each one continuously - flagging the ones where the combination of factors looks like the ones that preceded past incidents. Intervention before the event, not investigation after it.
Cash flow gaps, aging receivables, rising dispute rates, and at-risk renewals carry cascading consequences in any business, physical or digital. Pattern recognition across financial, customer, and operational data surfaces the accounts and exposures most likely to turn into a loss - ranked by impact, so attention goes where it matters most.
Risk doesn't announce itself. But it leaves patterns in your data before it turns into an incident. We'll show you what those patterns look like in your operation.
FAQ
The system monitors operational data for patterns that indicate elevated risk - safety incidents trending up, equipment failure rates increasing, compliance gaps accumulating, cash flow deteriorating, customer dispute rates rising. Risk scores are calculated and updated continuously. When a risk threshold is crossed, the alert fires before the risk becomes a loss.
Safety and incident risk (near-miss frequency, OSHA recordable trends, crew certification gaps), equipment and asset risk (failure probability rankings, maintenance backlog accumulation), financial risk (customer payment aging, job cost overruns, cash flow gaps), compliance risk (permit expirations, regulatory filing deadlines, certification lapses), and customer risk (service quality trends, dispute frequency, at-risk renewals).
Each risk category has configured indicators with weighted importance scores. When an indicator worsens - safety incident frequency increasing, equipment alerts accumulating, customer payment aging extending - its contribution to the risk score increases. The risk dashboard shows risk by category, ranked by score and trend direction. Operations managers see where risk is concentrating before incidents occur.
A manual risk register is updated periodically by a person reviewing operational data and making qualitative assessments. AI risk management monitors continuously, updates scores in real time from operational data, and alerts when thresholds are crossed. The difference is whether risk is assessed weekly in a meeting or monitored continuously and surfaced when it moves.
Risk indicators pull from all connected operational data - maintenance records, safety logs, billing data, compliance records, customer data, financial data. A risk alert that fires because a specific piece of equipment is showing elevated failure probability connects directly to the maintenance system to trigger inspection and to the dispatch system to flag the affected asset.
Risk management implementation requires a mature data infrastructure - connected operational systems providing reliable data. It is typically built after core operational systems are deployed and generating data. A focused risk implementation covering the highest-priority risk categories takes 6-10 weeks once the underlying data is available.
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