HeyNeighbor
HeyNeighbor

Risk Intelligence Engine

How HeyNeighbor detects risk before it becomes a crisis.

The intelligence engine captures operational signals, connects them across systems and time, identifies patterns as they form, and escalates exposure to leadership before consequences occur.

The problem

Risk doesn't arrive as a crisis. It forms as a pattern.

A maintenance request becomes a repeat complaint. A repeat complaint becomes an inspection failure. An inspection failure becomes a claim. At every stage, the signals were there, but no system connected them.

Signals are scattered

Risk signals arrive through different systems, including maintenance, complaints, inspections, and public reviews. No single system holds the full picture.

Context is lost over time

When work orders close, the underlying signal disappears. Staff turnover compounds the problem. Institutional memory erodes with every transition.

Leadership sees too late

By the time a pattern reaches leadership, it has already escalated into a claim, a lawsuit, or a safety incident. The window for prevention has closed.

How the engine works

Six layers from signal to leadership visibility.

Each layer builds on the one before it. Signals connect across systems and time, patterns form, and leadership gets early visibility when exposure becomes real.

Signal capture

Operational risk signals are recorded from maintenance requests, resident complaints, inspections, incident reports, and public data. Each signal is timestamped, categorized, and preserved with full context, nothing is discarded when a ticket closes.

Cross-system correlation

The engine connects signals across source, location, category, and time. A maintenance request in February, an inspection finding in March, and a resident complaint in April are no longer three unrelated events, they become a visible thread.

Pattern recognition

When related signals recur within a property or across a portfolio, the engine identifies the forming pattern. This is not simple keyword matching. It is a structured view of how isolated conditions turn into repeated exposure.

Threshold evaluation

Each pattern is measured against severity, recurrence, and cross-system spread. When a pattern crosses threshold, multiple signals, multiple systems, or escalating severity, it is flagged as actionable exposure.

Leadership escalation

When exposure crosses threshold, leadership receives a structured escalation with the full supporting record attached. No manual handoffs. No forwarded emails. The complete signal history arrives with the alert.

Continuous memory

Unlike traditional systems that reset visibility when work orders close, the engine preserves the full history of signals, patterns, and escalations. Institutional memory is maintained regardless of staff turnover or system changes.

Design principles

Built for pattern detection, not event logging.

Patterns over events

Individual signals are data. Patterns are intelligence. The engine is built to surface the latter.

No manual escalation required

Risk visibility should not depend on someone remembering to flag it. Threshold-based escalation removes that dependency.

Historical continuity

Closing a ticket should not erase a signal. The engine preserves context so patterns remain visible over months and years.

Leadership-grade clarity

Escalations are structured for decision-makers, with clear severity, full context, and defensible documentation. Not raw data dumps.

See the risk intelligence engine in action.

We'll walk you through how signals are captured, how patterns form, and what leadership sees when risk becomes real.

By submitting, you agree to be contacted by email about HeyNeighbor. SMS notifications are optional and only sent if you later provide a phone number and explicitly opt in.