ChargePoint

ChargePoint, operator of one of the largest EV charging networks in the world, faced a quality control challenge common to complex electronics assembly: maintaining consistent inspection standards across multiple product programs without a scalable, data-backed process. As EV charging infrastructure scales rapidly, hardware reliability is non-negotiable — a defective charger in the field damages both the customer experience and brand trust. Their operations and engineering teams lacked complete visibility into each assembly stage, making it difficult to catch anomalies early and trace defects back to their root cause efficiently. The absence of standardized inspection data left engineers spending excessive time on reactive defect hunting rather than proactive quality assurance.

ChargePoint deployed Instrumental's Manufacturing AI and Data platform, installing 6 Instrumental Stations across 2 product programs to bring computer vision-based inspection to every stage of EV charger assembly. Each station captures image data at assembly checkpoints, creating a complete and traceable data record tied to individual units. The computer vision system analyzes this visual data to flag anomalies — surface defects, misalignments, or assembly deviations — that human inspection might miss or catch too late in the process. The platform provides factory-wide oversight through a centralized dashboard, enabling engineering teams to query historical assembly records when investigating issues. This architecture supports both new product introduction workflows and ongoing mass production quality monitoring.

The deployment delivered measurable improvements across quality and engineering efficiency:

• 50% reduction in root cause analysis time — defect hunts that previously required deep manual review of upstream processes were resolved in half the time using traceable image data
• 17 anomalies identified during development — caught before they reached final design, preventing downstream field failures
• 6 stations across 2 programs provided standardized coverage without requiring a separate inspection line per product

As Technical Program Manager Ali Mansour noted: "Without Instrumental, we would have to start looking deeply at the earlier assembly processes. Using Instrumental reduced our defect hunt [and root cause] time by half." Engineering teams shifted from reactive firefighting to structured, data-driven quality management.

• Deploy inspection at the assembly stage level, not just final test — catching anomalies during development prevents them from being designed into production.
• Traceable data records are the multiplier: the speed gain in root cause analysis comes not just from AI detection but from having a queryable visual history of every unit.
• A small number of strategically placed stations can cover multiple programs — scalability doesn't require proportional hardware investment.
• Computer vision is most valuable when integrated into the development phase, not added as a production afterthought.
• For complex electronics like EV chargers, where field reliability directly affects end-user trust, the ROI case for AI inspection is strongest when framed around defect prevention rather than defect detection.