AI Predictive Maintenance System

What Challenge Did Industrial Manufacturing Corp Face?

The manufacturing company was experiencing costly unplanned downtime across their 12 factories. They needed a system to predict equipment failures before they occurred and optimize maintenance schedules.

How Did Agochar Approach This Project?

We began with a comprehensive discovery phase to understand the client's needs, existing infrastructure, and future goals. Our team developed a detailed technical architecture that would address current challenges while providing flexibility for future growth.

• Conducted stakeholder interviews and requirement gathering
• Analyzed existing systems and identified integration points
• Designed scalable architecture using modern best practices
• Implemented iterative development with continuous feedback

What Solution Did Agochar Build?

We deployed IoT sensors on critical equipment and built a machine learning pipeline to analyze vibration, temperature, and operational data. The system predicts failures with 92% accuracy and automatically schedules preventive maintenance.

How Was the Project Implemented?

Our development process followed agile methodology with two-week sprints and regular client demonstrations. We maintained close communication throughout the project to ensure alignment with business objectives.

1. Phase 1: Foundation and core functionality
2. Phase 2: Feature development and integrations
3. Phase 3: Testing, optimization, and launch preparation
4. Phase 4: Launch and ongoing support

What Was the Business Impact?

The project delivered significant measurable results for our client. The new system has transformed their operations and positioned them for continued growth in their market.