Raven.ai

Raven.ai is a manufacturing intelligence and operational analytics software designed to help manufacturers optimize production efficiency through real-time visibility and actionable insights. Positioned within the Manufacturing Execution System (MES) and OEE (Overall Equipment Effectiveness) optimization category, Raven.ai combines automated machine data collection with human input to deliver a complete and contextualized view of shop-floor performance. Founded in 2013 and headquartered in Ottawa, Canada, Raven.ai was built to address a critical gap in manufacturing operations: the lack of contextual data behind machine downtime and performance losses. Traditional MES and OEE tools often rely solely on sensor data, which can leave blind spots when machines fail to report or when human decisions impact production. Raven.ai solves this by integrating smart operator assistants with automated data streams, ensuring 100% accountability for production time and losses. The platform’s core functionality revolves around real-time data capture and analysis. Operators interact with intuitive touch-based HMIs (Human-Machine Interfaces) to tag downtime events only when necessary, reducing manual workload while enriching machine data with human context. This approach transforms fragmented logs into a continuous, accurate timeline of production events, enabling precise root-cause analysis and faster corrective actions. Raven.ai also offers advanced reporting and analytics tools accessible via web and mobile dashboards. These tools provide live OEE tracking, historical performance trends, and Pareto-style loss analysis, helping managers identify bottlenecks and prioritize improvement initiatives. Automated alerts and Andon-style notifications allow teams to respond immediately to downtime or quality issues, while gamification features—such as shift-level performance boards—boost operator engagement and accountability. The software integrates seamlessly with existing infrastructure, including PLCs, ERP systems, and IoT sensors, making it highly adaptable for mid-sized and large manufacturers. Its tiered subscription model ranges from Core (basic operator tagging and reporting) to Premium and Enterprise (advanced automation, integrations, and analytics), ensuring scalability as operational needs grow. Primary Uses of Raven.ai include: OEE Optimization: Tracking and improving equipment effectiveness through accurate downtime categorization. Continuous Improvement: Identifying inefficiencies and driving lean manufacturing initiatives. Real-Time Visibility: Providing managers and operators with actionable insights during production. Data-Driven Decision Making: Leveraging contextualized analytics for strategic planning and operational excellence. Global brands such as Michelin, Coca-Cola, Walmart, and Danaher trust Raven.ai to enhance productivity and reduce waste. With a strong focus on usability, scalability, and ROI, Raven.ai stands out as a modern solution for manufacturers seeking to bridge the gap between automation and human intelligence.

Raven.ai distinguishes itself in the manufacturing intelligence landscape through several strategic advantages that position it ahead of competing OEE and MES solutions. 1. Full-Context Data Integration A cornerstone of Raven.ai’s strength is its dual data collection model: automated machine integration combined with frontline human tagging. While many platforms rely solely on either sensors or manual logs, Raven.ai’s smart assistants prompt operator input only when machine data is insufficient, ensuring a 100 % accounted production timeline. This complete and contextual data foundation allows more accurate root-cause analysis and robust decision-making by eliminating blind spots. 2. User-Centric Smart Assistants Raven.ai’s design prioritizes ease of use on the shop floor. Touch-based HMIs for labeling downtime remove friction in capturing contextual data. Operators aren’t bogged down in forms or dashboards; they interact only when required. This ergonomic approach ensures high data fidelity and minimal disruption, fostering broad field adoption—an edge over competitors with rigid UX or heavy manual data entry requirements.

• Small Businesses (1-99)
• Mid Market (100-500)
• Enterprise (500+)