Business Process Automation /

We've partnered with a growing manufacturing company to transform their maintenance and quality analysis processes. As they expanded their existing facility and planned a second location, their processes became critically strained. Our focus was on developing an intelligent, on‑premise AI solution that automates anomaly detection and delivers timely, actionable insights to management.

Company Size:

[ 100+ Employees ]

Location:

[ Alberta - Canada ]

Business Challenge /

Our manufacturing client relied on manual review of sensor data, maintenance logs, and production metrics across multiple lines. With no integration between data sources, analysis was siloed, error‑prone, and slow—critical equipment anomalies took 5–7 days to identify, and quality reports were compiled monthly. This reactive approach prevented the maintenance team from proactively addressing issues and deprived management of timely insights during a period of significant growth.

Realizing The Need for Change /

Our client recognized the need for a better approach. As they scaled operations and planned their second facility, unplanned downtime and suboptimal resource allocation became unacceptable risks. They sought a solution that would automate anomaly detection, deliver real‑time insights, and support confident capacity planning.

What We Did /

The measures and methods we implemented to address the client's unique needs and bring their vision into reality.

AI‑Powered Anomaly Detection /

We integrated our Dandy AI engine with the client's existing CMMS and ERP systems, establishing secure on‑premise data pipelines. A Qwen 3‑based generative AI model, tuned on historical maintenance logs and sensor data, now generates actionable anomaly and quality reports.

Deterministic Validation Layer /

We applied Dandy's dynamic context engine to embed client‑specific rules—such as pump failure thresholds varying by machine model—validating model outputs and flagging invalid sensor readings before AI processing.

Continuous Feedback Loop /

We implemented a technician‑driven feedback mechanism, where confirmations feed back into the model for continuous improvement, keeping the system accurate and relevant over time.

Requirements /

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

On‑Premise Compute

AI Model Tuning

Data Integration

Data Automation

Predictive Analytics

Our Value /

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

Faster Problem Identification

Critical equipment anomalies are now identified in under 4 hours versus 5–7 days manually—a drastic increase in problem detection validated by CMMS logs.

Streamlined Operations

Technicians spend 30% less time on data review, and quality report generation dropped from 8 hours weekly to near real‑time delivery. Management receives insights 5x faster (daily vs. weekly).

Enhanced Decision‑Making

85% of AI‑generated reports required no modification before planning use, with a true positive rate above 85% on high‑priority anomalies—giving leadership confident, scalable decision support.

The Finished Product/

Our solution provides the client with a fully automated, on‑premise AI system that identifies equipment anomalies and delivers quality insights in near real time. What was once a slow, manual, error‑prone process is now an intelligent, reliable workflow, poised to scale to their second facility with ease.

Technologies Used /

The technologies used in this project

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