Quality AInalyst

Product failures cost billions of dollars annually for manufacturers in terms of warranty costs, expensive product recalls, and damage to brand reputation. Manufacturers face many challenges in analyzing and resolving quality issues in a timely fashion. Quality analysis powered by AI (Quality AInalyst) enables manufacturers to analyze the failure data using the latest AI techniques to find patterns, trends, and anomalies to detect emerging quality issues early and take corrective actions to address the root causes.

Quality AInalyst
Why Choose Quality AInalyst?

Advanced Quality Analytics Powered by AI 

Traditional Business Intelligence (BI) tools, with their reliance on descriptive analytics, reports, and dashboards, fall short of tackling the complex challenges manufacturers encounter. Quality AInalyst, powered by AI, offers all stakeholders across departments, regions, product lines, and suppliers diagnostic, predictive, and prescriptive analytics to discover emerging issues, root causes, and resolution options.

Quality Data Management

Automate the data ingestion process using 400+ pre-built, no-code source connectors to extract data from multiple systems, transform it into a consistent format, and load it in a Quality Data Lakehouse. The product field failure data captured in disparate systems and various formats can now be analyzed to drive quality improvements.

Text Analysis

Text analysis using LLMs (Large Language Models) to extract failure coding from unstructured failure descriptions such as Complaint, Cause, and Corrective Action helps classify and cluster the failures more accurately. uses industry-specific Product ontology and failure taxonomy to identify and extract entities from the textual data.

Data Visualization

Quality Analysts can use natural language queries to get answers without the technical know-how of complicated data structures and SQL. A flexible, easy-to-use interface, using an industry-leading analytics platform and charting tools, enables quality analysts to visualize the quality data and identify the correlations, trends, and patterns.

Predictive Analytics

Predictive models using AI/ML algorithms such as Decision trees, Neural networks, Regression analysis, Time series analysis, and Clustering automatically detect emerging quality issues. Predictive analytics minimize errors that can occur in manual data analysis, handle large volumes of data efficiently, and save time and resources.

Prescriptive Analytics

Prescriptive analytics augment quality and engineering teams on decisions about determining root causes, identifying corrective actions, scoping product recalls, and attributing defects to suppliers. Prescriptive analytics help assess risks by simulating various scenarios and providing proactive insights that are crucial for time-sensitive decisions.

Anomaly Detection

Anomaly detection algorithms identify unusual patterns or deviations in failure data to identify defects early, allowing for timely corrective actions and reducing the number of defective products reaching customers. Anomaly detection provides valuable insights that can guide process improvements and prevent future defects.

Early Warning

Quality AInalyst monitors the data using the latest AI technologies to find patterns, trends, and anomalies that indicate emerging quality issues. Early warning alerts and notifications are generated when specific patterns or anomalies are detected. In addition to automated notifications, quality teams can set up thresholds for safety and regulatory alerts.

Orchestration Center

Quality Orchestration Center enables configuration of data mappings, decision modeling, and tuning AI/ML model performance. Authorized teams can set up analysis projects with a defined scope based on each stakeholder's responsibility. Intelligent Automation enables integrations with CAPA, RMA, and QMS systems to streamline enterprise quality processes.


Improve Product Quality, Reduce Costs, and Enhance Customer Satisfaction 

  • Improve Product Quality

  • Lower Repair Costs

  • Enhance Customer Satisfaction

  • Improve Productivity and Efficiency

Reduce detection to correction time by 25%

Quality AInalyst reduces detection to correction cycle time by enabling manufacturers to detect emerging issues, find root causes, and identify corrective actions faster. Quality issues can now be corrected months earlier resulting in fewer defective products, lower warranty costs, and reduced cost of recalls.



Lower cost of Poor Quality by 15%

Quality issues cost manufacturers over $50 billion dollars in payments for warranty claims, product recalls, and returns. Earlier issue detection and shorter correction cycle times lower costs by shipping fewer defective products and reducing the size of recalls. Parts returns for inspection and supplier recovery cost millions of dollars. Better quality analytics reduce the need for returns, lowering the shipping and handling costs.

Enhance Customer Satisfaction and Retention

Quality issues and product recalls damage brand reputation and result in customer churn. Addressing the quality issues expeditiously will restore customer trust and improve customer retention. Engineering and Manufacturing departments can use the data captured by Quality AInalyst to improve the design of the products and build products with higher reliability, safety, and quality.

Improve Productivity and efficiency by 30%

By automating the detection of defects and quality issues, predictive analytics handle large volumes of data efficiently and improve the productivity of the teams involved in running reports and dashboards manually. Manufacturers and component suppliers can focus more on resolving quality issues instead of wasting time on data issues, manual inspections, and report reviews. Intelligent Automation saves time by integrating with CAPA systems to initiate corrective actions, RMA systems to authorize parts returns, and QMS systems to track quality issues.


How can Quality AInalyst help you?

What sources of data does Quality AInalyst use?

Quality AInalyst uses failure data captured in disparate systems depending on when and where the product repair service is performed. The data sources can be ERP or MRP systems, Call center CRMs, Warranty applications, Repair Order systems, Asset Management systems, returns management systems, and more.

The data inputs for quality analysis can Data inputs for Quality analysis can be warranty claims, support cases, repair orders, field service reports, inspections, IoT sensor data, or diagnostic tool data. The data is also captured in multiple formats, including structured unstructured text, images, part inspections, and diagnostic data.

Quality AInalyst solution includes ETL (Extract, Load, Transform) capability to ingest data from all these diverse sources using 400+ connectors into a Quality Data Lakehouse and perform advanced analytics.

What differentiates Quality AInalyst solution? focuses and specializes in the development of AI-powered software solutions to analyze, augment, and automate impactful decisions.'s advanced analytics powered by cutting-edge AI technologies, including GenAI, predictive and prescriptive analytics, and Vector & Graph Databases, address your business needs now and in the future. solutions are more cost-effective for subscription, implementation, and support fees, lowering your total cost of ownership.'s flexible model to partner with customers on product roadmap will prioritize your needs and requirements. team brings decades of industry and domain knowledge from developing and implementing enterprise software solutions to hundreds of global manufacturers. is leveraging the leading solutions to deliver the most flexible, reliable, and scalable solutions to our customers. software is built on cloud platforms powered by Amazon AWS and Microsoft Azure, industry-leading Microsoft Power BI for the Business Intelligence solution, and a Data Intelligence platform from Data Bricks.'s specialization, deep industry and domain knowledge, advanced AI capabilities, and scalable platform provide you with the best solution for your advanced quality analytics needs.

Does Quality AInalyst integrate with other enterprise systems?

Yes. Quality AInalyst seamlessly integrates with enterprise software applications, legacy data, and other systems of record for various transactions. Quality AInalyst is focused on providing advanced analytics powered by AI while integrating with other business processes, data sources, and workflows. Intelligent Automation integrates with CAPA systems to initiate corrective actions, RMA systems to authorize parts returns, and QMS systems to track quality issues. has also partnered with Systems Integrators (Sis) and service providers to ensure the Quality AInalyst is implemented successfully and integrated with your enterprise systems.

Improve Quality of Products with Analytics Powered by AI

Leverage predictive analytics, prescriptive analytics, and anomaly detection powered by AI/ML algorithms to drive product quality, improve efficiency, and enhance customer satisfaction.