Data and AI Testing

Ensure quality and eliminate bias and inaccuracies
in AI applications to deliver flawless experiences.

Application Modernization

Leverage the best of innovative technology, people and
delivery approaches to help you reimagine, build
and optimize your future.

As we continue to witness the rapid advancement of technology, the integration of AI into applications has become a game-changer for businesses looking to enhance user experiences, streamline processes, and drive innovation. However, it is crucial to ensure the reliability, accuracy, and performance of these applications, as their output can have a significant impact on businesses and users alike.

AI Introduces New Risks and Amplifies Existing Ones

TVS Next offers data and AI testing, helping organizations to identify and fix any issues in their systems, ensuring a seamless and error-free experience for their customers.

Enhanced Threats

Lack of Transparency

Malicious Use

Organizational Risks

Heightened Dependency

Traditional Testing Methods Won’t Work

Non - Deterministic

Inaccurate Training Data



Sustained Testing

Legal and Security Risks

Regulatory Compliance


How TVS Next Tests To Ensure Safe and Scalable AI

AI/ML Support at Every Stage

End-to-end support for AI/ML projects ranging from the initial idea to the final implementation stage. Our team of experts ensures that all AI/ML models are developed in compliance with industry standards and best practices.

Bias & Error Detection

Identify and eliminate any potential bias in both data and models. With this approach, they not only ensure the accuracy of their AI systems but also guarantee that they are completely impartial and fair.

Safe Integrations

Ensure safe integrations by conducting thorough assessments and tests that does not compromise the stability or security of existing systems.

Data Assessment & Interrogation

Conduct rigorous data assessments and interrogations to verify the quality, relevance, and integrity of the data. This step helps in creating robust and reliable AI models.

Stability Assurance

Provide stability assurance by continuously monitoring the AI system to detect and resolve any issues promptly. This proactive approach helps in maintaining the optimal performance of the AI models.

Model Optimization

Fine-tune AI models to enhance their accuracy and efficiency. By adjusting the parameters and using advanced optimization techniques, we ensure that the AI models deliver the best possible results.

Setting Goals for Testing AI Applications

Having clear objectives is a crucial factor in testing AI applications. Without well-defined goals, the testing process can become disorganized and ineffective. Therefore, setting clear objectives before conducting any tests on AI applications is essential. This will ensure that the testing process is efficient and that the results are accurate and reliable.

Detect and alleviate fairness-related harms

Detect and alleviate incorrect responses

Minimize security and privacy-related risks

Fine tune your model through understanding “real world” prompts ​

Find and fix critical bugs

Improve the user experience via feedback

Ensure that individual with disabilities can use the applications

Testing Across AI Lifecycle

Creating safe and effective AI systems involves meticulous steps, from selecting and cleaning data to deploying models into production. Rigorous testing ensures accurate and valuable insights and maximizes their potential and effectiveness.

Data Preparation

This involves a meticulous process of selecting, ingesting, cleaning, fusing, and engineering data. By investing time and effort into this process, we can ensure that the models are well-trained and capable of delivering powerful insights.

Data Selection

Data Ingestion

Data Fusion

Data Quality

Data Cleansing

Model Building

By constructing, training, and validating predictive algorithms with diverse datasets, we can develop robust and accurate models that effectively address complexities within AI systems.

Feature Engineering

Model Building

Model Testing & Selection

Risk Management

Model Deployment

Deploying a trained machine learning model in a production environment is a crucial step towards achieving practical applications and use.

Risk Management



Analyze & Recommend


Driving Successful AI Testing Outcomes
This requires a well-thought-out strategy, precise execution, and continuous improvement. As the adoption of AI for building systems and applications is on the rise, it is imperative to keep up with the evolving landscape. Ensure that your business takes effective measures to outpace competitors.

Align Your Testing Approach To Match Ever-Evolving AI


92% of customers expect companies to have chatbots on their apps or websites


86% of customers indicated concern about bias in AI


Private information protection by AI is doubted by 52% of consumers

Internal Use

65% of companies are using AI internally, while 74% are testing it

Transformation Stories
How a Food Delivery Giant Skyrocketed Their App Rating
Boosting Application Turnaround for a Tech Product Company

Latest Insights

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