OMNIERAX
Briefing
CASE STUDIES

Real Deployments. Real Outcomes. Documented With the Specificity That Makes Them Useful.

Omnierax case studies are written to be operationally useful to readers facing similar challenges — not to be marketing collateral that makes our customers look brilliant and our technology appear magical. Every case study includes the operational problem, deployment approach, quantified outcome metrics, and lessons that generalize.

We publish only with customer authorization and only when outcome data meets our specificity standard: quantified, attributable, methodology-described. If a deployment produced mixed outcomes, we document them — including what did not work.

CASE: NORTHWATCH

A National Intelligence Agency Reduced Intelligence Cycle Time by 67% While Doubling Sources Processed.

SENTINELMULTI-SITE NATIONALDEFENSE & INTELLIGENCEDEPLOYED Q3 2024
The Operational Problem

A national intelligence organization was managing collection and analysis across an expanding source base with an analyst workforce that had not grown proportionally. The intelligence cycle — from raw collection to finished product delivery to decision-makers — averaged 18 hours. In a threat environment where adversarial operational tempo was measured in hours, this cycle time created a persistent reactive posture.

The Deployment

Sentinel deployed in a sovereign air-gapped environment. Intelligence fusion layer connected to 12 previously siloed collection systems. Entity resolution engine run against 4 years of accumulated historical data before going live. Full deployment to operational status in 89 days.

Outcomes (12 months)
Metric
Baseline
Post
Δ
Intelligence cycle time
18 hours
6 hours
−67%
Sources monitored simultaneously
12
28
+133%
Analyst time on analysis (vs. processing)
30%
75%
+45 pp
Novel network connections (first 30 days)
—
847
—
Lessons That Generalize
  • Pre-loading 4 years of historical data into the entity resolution engine accelerated novel-connection discovery by an order of magnitude vs. live-only ingest.
  • Air-gap model update delivery was the single most consequential operational design decision; design it before procurement, not after.
  • Analyst-time reallocation only materializes if collection automation is accompanied by retraining the analytic team on the new cycle cadence.
CASE: MERIDIAN INDUSTRIAL

An Automotive Tier-1 Supplier Reduced Unplanned Downtime by 41% Across Seven Facilities in Year One.

MAXIMUSVERTICAL SOLUTIONSMULTI-SITE (7 plants, 3 countries)MANUFACTURINGDEPLOYED Q1 2025
The Operational Problem

A global automotive components supplier was losing ~4.2% of total available production time to unplanned equipment failures. Downtime was distributed across multiple equipment categories, occurring at unpredictable intervals, and causing cascading supply disruption to multiple OEM customers. Maintenance was scheduled by time-based intervals, not equipment condition.

The Deployment

Maximus deployed with integrations to 7 facility MES and SCADA environments, connecting 2,847 monitored equipment assets. Vertical Solutions automotive configuration provided pre-built equipment health models for 23 equipment categories. Deployment phased: 2 pilot facilities in months 1–3, full 7-facility rollout months 4–9.

Outcomes (12 months)
Metric
Baseline
Post
Δ
Unplanned downtime % of available time
4.2%
2.5%
−41%
Planned maintenance share of total maintenance
34%
71%
+37 pp
Average advance warning before failure
—
6.2 days
—
Emergency maintenance cost (YoY)
—
−$4.3M
—
OEM on-time delivery
—
+11 pp
—

"We had tried predictive maintenance twice before. The difference this time was the ontology — once the equipment hierarchy and failure modes were modeled coherently across all seven plants, the models stopped being site-specific science projects and started being a network-wide capability."

— VP of Manufacturing Operations, Multinational Tier-1 Supplier
Lessons That Generalize
  • Cross-site ontology coherence is the prerequisite, not the consequence, of predictive maintenance scale.
  • The economic case is driven by emergency-maintenance avoidance more than spare-parts optimization — finance modeling that ignores this underweights ROI.
  • OEM scorecard improvements lag operational improvements by approximately one quarter; commit to measuring both.
CASE: COVENANT HEALTH

A Regional Health System Reduced ICU Transfers from Non-ICU Units by 23% — Without Increasing Alert Volume.

CORTEX AIVERTICAL SOLUTIONSENTERPRISE (12-hospital network)HEALTHCAREDEPLOYED Q2 2025
The Operational Problem

A 12-hospital system was experiencing ICU transfers from non-ICU units at a rate clinical leadership assessed as preventable. Their existing early warning score system was producing alert volumes clinicians characterized as fatiguing — so many alerts that clinicians prioritized alert review over direct assessment. The challenge: improve detection sensitivity without increasing alert volume.

The Deployment

Cortex AI deployed within the health system's on-premise infrastructure (no PHI transmitted externally). Integrated with Epic EHR, bedside monitoring, lab, and pharmacy systems. Multi-signal deterioration models calibrated to the system's specific patient population before go-live. 90-day validation period before production deployment.

Outcomes (12 months)
Metric
Baseline
Post
Δ
ICU transfers from monitored non-ICU units
12-mo baseline
−23%
−23%
Clinical alert volume
prior system
−31%
−31%
First signal → clinical review
2.1 hours
38 minutes
−70%
False positive rate (no intervention in 4h)
—
−44%
−44%

"The most important number in this evaluation is not the 23% reduction — it is the zero adverse events attributable to the system during measurement. We will not deploy clinical AI we cannot validate that way."

— Chief Medical Information Officer, Regional Health System
Lessons That Generalize
  • Population-specific calibration during a 90-day validation period was the precondition for clinician trust at go-live.
  • Reducing alert volume while improving sensitivity is achievable, but only with multi-signal fusion — single-signal score tuning will not produce both outcomes.
  • Measuring adverse events attributable to the system requires a clinical chart review methodology in place before deployment, not retrospectively.