Agent Based AI /

Stratus Advanced Technologies partnered with an Alberta‑based SaaS platform serving manufacturing clients to transform overwhelming sensor data into actionable intelligence. By implementing an agent‑based AI system, we enabled manufacturers to reduce alert fatigue while maintaining full visibility into critical safety events.

Company Size:

[ Platform Serving Multiple Manufacturing Facilities ]

Location:

[ Alberta - Canada ]

Business Challenge /

Manufacturing facilities equipped with extensive sensor networks generate overwhelming volumes of data. Operators faced severe alert fatigue—receiving hundreds of notifications daily with many false positives—making it nearly impossible to identify genuine critical events. This directly threatened equipment uptime and worker safety.

Realizing The Need for Change /

The client's SaaS platform needed to transform raw data into actionable intelligence without adding cognitive burden to plant operators. Alberta's data sovereignty requirements demanded private, on‑premise infrastructure. The goal was building an AI system that could intelligently filter, prioritize, and contextualize sensor data while remaining fully auditable.

What We Did /

The measures and methods we implemented to address the client's unique needs and bring their vision into reality. We implemented a dual-layer AI architecture combining our Dandy™ deterministic inference library with fine-tuned generative AI capabilities. Real-time sensor feeds were integrated with existing CMMS and ERP systems, creating a scalable, multi-tenant framework running entirely on private data centers.

Dandy™ Deterministic Inference Library /

The Dandy™ library applies context‑aware rules to suppress false alerts—like vibration spikes during scheduled calibration—by analyzing asset type, shift patterns, and historical operator responses. Every AI decision remains explainable through comprehensive rule logging.

Generative AI Alert Synthesis /

Generative AI converts complex sensor patterns into concise, understandable operator alerts while maintaining full traceability to source data. This layer transforms raw signal data into human‑readable insights within the constraints established by deterministic rules.

Private On‑Premise Infrastructure /

All processing occurs on private data centers, ensuring complete data sovereignty for Alberta‑based manufacturers while delivering advanced AI capabilities at scale.

Requirements /

The specific technology solutions that were selected and implemented to address the challenges they faced

Reduce operator alert volume while preserving critical safety event visibility

Implement explainable AI with full audit trails for regulatory compliance

Maintain data sovereignty through private, on‑premise processing

Integrate real‑time sensor feeds with existing CMMS and ERP systems

Achieve measurable reductions in operator cognitive burden

Enable scalability across multiple manufacturing clients and verticals

Our Value /

The technological advantages our client gained from our assistive and scalable technological interventions.

Alert Reduction

A 68% reduction in total daily alerts per operator (from 120 to 38) allows teams to focus on genuine operational priorities rather than sorting through false positives.

Operational Efficiency

The solution achieved a 45% reduction in operator alert review time, with 50% of false alerts suppressed through context‑aware deterministic rules. Operational teams can now allocate expertise to higher‑value activities.

Safety Precision

Critical safety alerts maintained 98% precision across 100+ test cases, with 100% retention of high‑priority maintenance events. Efficiency gains do not compromise worker safety or operational oversight.

The Finished Product/

Stratus Advanced Technologies delivered an AI‑powered operational intelligence system that transforms sensor data into actionable insights while maintaining full data sovereignty and human oversight. The dual‑layer architecture achieved a 68% reduction in operator alerts and 98% precision on safety‑critical events. Every AI decision remains explainable and auditable, building operator trust and enabling regulatory compliance. This scalable, multi‑tenant framework positions manufacturing clients to lead in responsible AI adoption.

Technologies Used /

The technologies used in this project

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