Does Qualisense support monitoring and retraining?

Yes. Qualisense supports production model monitoring and retraining workflows that help identify performance degradation and trigger retraining when needed, helping models remain accurate, reliable, and up to date over time.

What Machine Learning models does Qualisense support?

Qualisense supports over 40 algorithms across classification, estimation, and multi-classification tasks.

Accelerate machine learning from experimentation to production. Qualisense is QualityAI’s AI-enabled ML workflow accelerator, helping teams streamline data exploration, feature engineering, model building, validation, deployment, monitoring, and retraining with greater visibility and governance.

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Accelerate the Machine Learning Lifecycle with Qualisense

Move from data exploration to model deployment, monitoring, and retraining with greater speed, reliability, and governance.

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What Is Qualisense?

QualityAI's Unified Workflow for Machine Learning Delivery

Qualisense brings key machine learning workflows into a single delivery framework. It enables users to upload and prepare data, engineer features, build and validate models, deploy approved models into production, and monitor performance over time.

Its capabilities help enterprise teams:

Simplify the ML lifecycle through automation, analytics, and AI-enabled workflows
Accelerate data preparation, experimentation, model deployment, monitoring, and retraining
Improve model reliability, governance, and production readiness
Support operational, analytical, customer, and business transformation use cases

Solving the Barriers to Production-Ready ML

Organizations often struggle to operationalize machine learning because tools are fragmented, workflows are inconsistent, and visibility across the lifecycle is limited. Data science teams spend significant time preparing data, engineering features, building models, validating models, and coordinating deployments across multiple platforms and teams.

These challenges increase risk where scalability, governance, compliance, operational efficiency and model performance matter. Without effective monitoring and retraining, models can drift over time, affecting forecasting accuracy, customer experience, operational decisions, and business outcomes.

Qualisense helps address these challenges through:

Scalable AI Adoption and Data-Driven Decision-Making

Continuous Monitoring and Retraining to Support Model Performance

Stronger Production Readiness and Lifecycle Visibility

Faster Development and Improved Collaboration

Integrated, Automation-Led ML Workflow

A Simple Workflow from Data to Deployment

Qualisense guides teams through each stage of the machine learning lifecycle while automating repetitive tasks and improving operational visibility.

Upload and Explore

Create and optimize features using automated transformation workflows.

Build and Validate

Train, test, and validate machine learning models against defined performance metrics.

Deploy Models

Deploy approved models into production using integrated deployment pipelines.

Monitor and Retrain

Track model performance continuously, and trigger retraining when performance declines.

Tools, Integrations, and Technologies Qualisense Supports

Supported Platforms

Docker-based deployment environments, containerized execution, on-premise and cloud-hosted Docker environments

Integrations

MongoDB for credential verification and management

Data Sources

CSV, Excel, enterprise databases, APIs, cloud storage platforms

Reporting and Dashboards

For testing use cases, prediction deployments can be integrated with the Qualiview dashboard to support monitoring and validation activities

Real Impact, Real Benefits. Our Customer Work in Action.

Transforming Network and Digital Experience for a National Telecom Operator

Challenge
A leading telecom provider faced customer churn, revenue leakage and declining trust due to inconsistent digital journeys, fragmented platforms and poor visibility between pre-production and live environments
Solution
Established an on-site mobile test lab to simulate real-world network conditions, executed end-to-end journey validation across prepaid, postpaid, roaming and digital services, and automated configuration comparison between pre-production and production environments
Result
Reduced customer-impacting production defects by 80%, delivered 40%+ cost savings and enabled stable, on-time releases across complex environments.

Hyper-Scaling Test Automation for a Global Multi-Carrier Platform

Challenge
A global telecom platform provider supporting 200+ carriers and 300+ merchants faced cloud migration risk, with manual testing limiting speed, scalability and accuracy.
Solution
Designed an agile automation-first testing framework, built continuous validation cycles, and enabled real-time execution, analysis and reporting across environments.
Result
Reduced testing time from three days to 20 minutes, deployed 2,000+ automated test cases and increased testing capacity by 20x per sprint.

Driving Productivity Gains Through Enterprise QA Transformation

Challenge
A telecom infrastructure provider managing 10,500+ assets across multiple regions struggled with manual processes, fragmented workflows and inconsistent delivery visibility.
Solution
Implemented a structured enterprise QA model across workflows, integrations and systems, enabled automation of critical processes, and delivered role-based training and documentation.
Result
Improved operational productivity by 70%, achieved 100% on-time releases and reduced production defects by 30%.

Medical Device V&V and 510(k) Submission Support

Challenge
Support a leading healthcare company through the largest 510(k) submission in its history.
Solution
Delivered verification, validation, documentation and traceability across complex medical device requirements, supported by automated data acquisition using Python scripts for test stations.
Result
22,000+ pages of FDA and ISO documentation delivered, 1,400+ requirements verified with complete traceability and zero FDA requests for additional information on verification.

SCADA Modernization for a Leading Electricity Company

Challenge
Upgrade an end-of-life SCADA environment while maintaining NERC CIP compliance and critical service continuity for a utility serving almost four million customers.
Solution
Developed a NERC CIP-compliant project plan, supported hardware and software upgrades, coordinated with compliance teams and vendors, established security monitoring and implemented an improved disaster recovery solution.
Result
SCADA systems upgraded with proper change management procedures, disaster recovery modernized and NERC CIP change management requirements supported throughout the transition.

Accelerating ML Deployment in EdTech

A long-term customer needed to build accurate and reliable student recommendation models while addressing slow deployment and inconsistent governance.

Solution
Qualisense automated ML workflows, deployment and monitoring, enabling faster and more reliable model delivery.
Impact
Reduced deployment time by 40%.

Accelerating Speed to Market for a Leading Vehicle Retailer

Challenge
A leading vehicle retailer needed to modernize auto finance testing, reduce manual effort and improve regression coverage.
Solution
Automated high-priority business process tests and implemented a robust regression automation framework across complex financial workflows.
Result
30% reduction in test execution time, six weeks of manual regression saved per release cycle, 97% process regression test coverage and full end-to-end regression reduced from two months to two days.

Improving Recommendation Accuracy for Retail

A retailer needed a consistent way to provide weekly point-of-sale product recommendations based on customer purchase history and related features.

Solution
Qualisense supported an AI model solution that fed sales applications with ongoing product recommendations.
Impact
Improved recommendation accuracy by 25%.

Reducing Manual Effort for Healthcare Analytics

A healthcare analytics provider faced manual ECG model validation and operational inefficiencies across core processes.

Solution
Qualisense streamlined model validation and key automation workflows.
Impact
Reduced manual effort by 50%.

Multimodal AI Assurance for a Voice and Vision Platform

Challenge
A technology organization needed robust validation for an advanced conversational AI platform across voice, camera input, multi-device workflows and complex user journeys. The system needed to maintain contextual reasoning, accurate intent recognition, responsible boundaries and safe behavior across real-world interactions.
Solution
QualityAI executed a structured multimodal assurance program, using a 24-point heuristic framework to evaluate conversational smoothness, listening quality, response latency, language adherence, visual comprehension and contextual accuracy. The team benchmarked performance against leading AI products, simulated real-world journeys and translated findings into actionable engineering requirements.
Result
Three GenAI products were benchmarked, 24 heuristics were mapped across multimodal quality criteria, and 15+ multilingual machine learning analysts supported evaluation across three global delivery locations. The engagement identified intelligence gaps, strengthened responsible AI boundaries and provided engineering-ready recommendations for product improvement.

Enterprise Testing at Scale for a Water Utility

Challenge
Build a scalable, end-to-end quality assurance model across SAP, Microsoft D365, smart metering and enterprise platforms while improving efficiency, reducing cost and increasing delivery value.
Solution
Delivered a holistic quality model across sector, horizontal and CoE layers, using AI adoption through Qualisense and an automation roadmap covering release regression and enterprise test coverage.
Result
50% productivity improvement through AI adoption, 70% higher test coverage through full regression automation and 50% of the team upskilled across SAP and enterprise modules.