Real-time quality control
Empower your teams to leave the field with a clear mind, knowing that the job has been done right.
Today's field tools may help to better plan operations, but they have yet to address a fundamental problem: helping workers to do the job right the first time.
- Avoiding mistakes.
- Avoiding oversights.
These anomalies prevent equipment and infrastructure from functioning properly. Companies have to plan for re-interventions and suffer complaints from their clients whose experience has deteriorated.
With Deepomatic, field workers are no longer left to their own devices. They're equipped with mobile software that helps them do their job correctly.
Computer vision algorithms analyze field photos and systematically check each operation. Workers receive instant feedback, enabling them to correct any anomalies detected before leaving the site.
Accelerated time-to-revenue
- Subcontractors are paid from the very first intervention
- Operators deliver their services faster to end-users
Lower costs
- Fewer re-interventions
- Lower quality control and back office costs
- Reduced costs linked to equipment damage
- Greater customer experience
Customer loyalty
- Better quality of service for companies and end customers
- Compliance with quality and safety standards
- More autonomy for field workers
Mobile app
Deepomatic's mobile app enables field workers to capture before-and-after photos of their work. Our AI tells them in a matter of seconds whether their actions have been carried out correctly.
- Avoid leaving defects uncorrected, leading to rework.
- Put quality control back at the heart of every operation to ensure its success.
The need of Quality Control Management Software
Challenges in Traditional Quality Management
Traditional quality management faces various hurdles impacting the efficiency and efficacy of maintaining quality standards. A common issue is a siloed approach where quality management is mistakenly designated to a particular department instead of being an enterprise-wide endeavor. This fragmented view hinders effective collaboration from field workers to back office teams, leading to disjointed quality control processes. Implementing a comprehensive QMS software can help overcome these cons by fostering a unified approach.
The traditional methods also result in reactive quality control, addressing issues post-occurrence instead of proactively preventing them through real-time feedback on the job done in the field. Additional barriers include a hesitance to embrace new technology and allocate the vital resources and time for quality initiatives. Such resistance often arises from the notion that these systems pose an upfront resource and time investment, obscuring the long-term benefits of superior quality and efficiency.
Moreover, organizations are increasingly looking for sophisticated analytics facilitating informed decision-making by presenting quality data through actionable insights. QMS software provides business intelligence dashboards enabling companies to monitor compliance and health of their infrastructures, assess risks, and optimize processes more effectively.
By implementing a comprehensive QMS software, businesses can overcome these challenges through a structured quality approach, real-time data analysis, and ongoing improvements organization-wide. This system not only supports compliance and documentation but also enhances customer satisfaction and operational efficiency.
Advancement in AI and Its Impact on Quality Control
The advent of Artificial Intelligence (AI) and cutting-edge technology has revolutionized quality control paradigms. AI-enabled quality management software offers predictive capacities, facilitating proactive measures by foreseeing and mitigating quality issues before they arise. This advancement transforms traditional quality management systems into intelligent platforms that enhance process efficiency and compliance.
This shift to AI and automation elevates quality management from a reactive approach to a preventive one, significantly enhancing the productivity of quality control processes. AI systems, equipped to analyze enormous datasets in real-time, offer insights crucial for early problem detection, thereby decreasing the cost of quality failures and elevating quality standards overall.
Deepomatic's approach to Quality Control Management
Intelligent Automation with Deepomatic
Deepomatic seamlessly integrates automation into quality control processes. This strategy leverages advanced computer vision and AI to completely reshape traditional quality control frameworks, offering robust solutions for organizations operating in the field.
Central to Deepomatic's groundbreaking solution is a mobile application crafted for field workers to effortlessly capture photos at critical moments of their interventions. These images undergo immediate analysis through AI-driven computer vision algorithms, keenly evaluating each operation to verify job conformity with fixed quality benchmarks.
The merit of this live analysis is the prompt feedback delivered to field workers, allowing them to rectify any deviations or defects forthwith, thus eliminating the need for revisits. Such a forward-thinking approach drastically curtails the necessity for follow-up interventions, resulting in notable cost savings.
Real-Time Health Scoring and Preventive Maintenance
Deepomatic's platform extends its capabilities to encompass preventive maintenance and asset management. By analyzing pictures taken during site visits, the AI assigns health scores to assets, thereby identifying potential issues before they become major concerns. This proactive maintenance strategy is key to prolonging the service life of physical assets, reducing the risk of unexpected downtimes, and trimming maintenance costs, all of which contribute to a more efficient quality management system.
Getting Started with Deepomatic
Implementing Deepomatic’s quality control management software is a streamlined process designed to minimize disruption to your existing operations. Following our methodology built on 10 years of experience, companies can transition smoothly to a robust quality management system.
Deepomatic recommends a structured integration journey that includes data collection, AI training, solution integration, and change management. By following this approach, companies can have the Deepomatic platform live and operational within three months. This systematic implementation ensures that all quality management requirements are met and that the system supports the organization's business objectives.