AI Testing Services
AI Product QA
With specialized AI model testing, data validation, and bias detection, we help you ship reliable AI applications that perform consistently in real-world scenarios.
Why Your AI Product Needs Specialized QA
Up to 40% of dev time gets wasted debugging unpredictable AI behavior.
Just 1 wrong output can damage user trust or trigger compliance risks.
80% of AI project failures happen due to poor data quality and lack of testing oversight.
Thousands of inputs, millions of paths — you need AI testing automation for that.
AI Product Testing ≠ Software Testing
AI models behave unpredictably, learn from user data, and fail in ways traditional software never does. You need testing designed for these unique challenges.
Traditional Software
- Predictable outputs
- Fixed logic
- Clear pass/fail criteria
AI Products
- Variable outputs
- Learning logic
- Context-dependent results
Our AI Testing Services
Data testing
Validate input quality, preprocessing, and how model outputs change based on different data types and volumes.
Model validation
Test ML and GenAI models for accuracy, consistency, and robustness — including edge cases and retraining behavior.
Functional testing
Check that AI-powered features work reliably across platforms, ensuring stable logic, APIs, and user flows.
Output review
Assess generated results for safety, bias, and usability — flagging hallucinations or unacceptable outcomes.
Security checks
Test for adversarial attacks, data leakage risks, and model misuse — protecting your AI from intentional harm.
Explainability testing
Verify model transparency, traceability, and behavior alignment — helping teams meet compliance and ethical standards.
Expert AI Testing: Your Competitive Advantage
AI-Specific QA Frameworks
Traditional testing misses AI-specific risks like model drift and data bias.
Our specialized frameworks validate data quality, model behavior, and system stability, ensuring your product is reliable and ready for the real world.
Predictable Performance
Unpredictable AI behavior wastes valuable dev time.
We help you take control by ensuring your model performs consistently and predictably under any condition, so you can build with confidence and ship faster.
Unbiased Outcomes
Your product must work fairly for all users.
We perform rigorous bias analysis to guarantee your models deliver ethical and equitable results, protecting your brand’s reputation and building user trust.
Minimized Hallucinations
False or fabricated outputs can quickly erode user confidence.
We use targeted methods to identify and minimize “hallucinations,” ensuring the accuracy and reliability of your AI’s generated content.
Superior User Experience
Users expect a flawless product with predictable UX flows.
We test how your AI features integrate with the user interface and data, guaranteeing a smooth and intuitive experience that keeps users engaged and satisfied.
Cost-Efficient QA at Scale
Scaling an AI product without proper testing can be expensive.
Our services help you manage resources and costs efficiently, ensuring high quality that grows with your business, not against it.
“When building an AI product, you’re constantly worried about it saying something wrong or offensive. We knew we couldn’t just hope it worked. TestFort’s team was great at pointing out things we missed, like those subtle biases that could have become a PR issue. They’ve built a testing pipeline based on modern frameworks specifically for our AI app and gave us the confidence to launch.”
Chief Product Officer, Project Planning Tool
Our Latest AI Product Testing Cases
AI Testing for a B2B Sales Copilot
Problem. A sales copilot designed to automate email drafts was generating irrelevant, unprofessional, and sometimes biased content, leading to low user adoption.
Solution. We implemented a custom testing framework focused on prompt engineering, output validation, and bias detection.
Our team created a comprehensive library of edge-case prompts and a scoring system to evaluate the quality, safety, and tone of the generated content.
- Over 60% reduction in hallucination rate
- Over 35% improvement in response accuracy
- Eliminated key instances of gender and racial bias
- From 6.5 to 8.7 average user satisfaction rating growth.
QA for an E-commerce Recommendation Engine
Problem. An e-commerce platform’s recommendation engine was providing irrelevant product suggestions and its performance was degrading over time due to model drift and poor data quality.
This resulted in a over 20% drop in click-through rates.
Solution. We developed an automated regression testing pipeline that monitored the model’s performance on live production data. Our data validation strategy identified and rectified data quality issues, and we set up continuous monitoring to prevent future drift.
- From 6.5 to 8.7 average user satisfaction rating growth
- 95% model accuracy validation
- Restored click-through rates to pre-drift levels
- Identified 2 critical data leakage risks.
AI Quality Audit for a CI/CD Assistant
Problem. The client’s AI assistant for CI/CD workflows struggled with accuracy, adaptability, and security, often producing hallucinated or inconsistent responses and failing to deliver proactive, context-aware support.
Solution. TestFort conducted a full AI quality audit using manual, automated, and usability testing to identify weaknesses and implement a continuous monitoring and regression testing framework.
- 20% increase in user satisfaction
- 25 critical AI defects identified
- 60% reduction in hallucinations
- 17% higher reply accuracy
AI-Powered Testing Boost for Creative Console Builder
Problem. The client’s QA process struggled with duplicated bugs, limited test coverage, and inefficient reporting, slowing down releases and damaging overall QA efficiency.
Solution. TestFort applied AI tools like DeepTriage, ChatGPT, and Power BI to automate bug triage, enhance test coverage, and accelerate reporting while maintaining data security and smooth integration.
- 80% faster bug report creation
- 80% faster test case generation
- 40% broader edge-case coverage
- 50% more accurate documentation
Testing AI Models and AI Applications by Type
GenAI products
Chatbots, copilots, content generation tools, and LLM-based assistants — tested for prompt reliability, hallucinations, and safe user interaction.
ML-powered systems
Recommendation engines, fraud detection, demand forecasting, risk scoring tools, and other real-time machine learning systems.
Agentic AI
Autonomous agents that plan, act, and learn with limited human input — validated for outcome reliability, safety boundaries, and control flow.
NLP & voice AI
Language models, sentiment analysis tools, voice recognition systems, and multilingual AI — tested across intent accuracy, tone, and edge-case inputs.
CV & vision models
Image recognition, object detection, OCR, and real-time video analysis — tested for accuracy, performance, and adaptability across device types.
Enterprise AI
AI systems used in finance, healthtech, logistics, and other complex environments — tested for reliability, compliance, and integration with critical workflows.
Get Your Role-Specific AI Product Testing Checklist
Don’t guess what to test. Our comprehensive checklist breaks down the most critical AI QA tasks for your role — from validating models and data to ensuring unbiased outcomes.
Our AI ML Testing Approach
Standard software testing misses AI-specific risks like model drift, bias, and adversarial attacks. Our AI testing methodology validates model accuracy, data quality, and system reliability using approaches designed for machine learning systems.
Model-aware testing strategy
We tailor our artificial intelligence testing approach to your specific AI architecture — from LLMs and natural language processing models to computer vision, time-series prediction, or rule-based hybrids.
- Performance testing optimized for your AI system’s requirements
- Custom testing frameworks for different AI model types
- Specialized validation for generative AI applications and ML models
- Testing methodologies adapted to your AI solution’s complexity
Data-driven validation
We simulate diverse input conditions using synthetic data, noisy datasets, edge cases, and adversarial scenarios — validating output accuracy, detecting bias, and ensuring stability across different data quality conditions.
- Data quality assessment throughout the testing process
- Comprehensive testing with clean and corrupted data inputs
- Edge case validation using amounts of data your system will encounter
- Adversarial testing to identify potential security vulnerabilities
Security & Robustness testing
AI systems face unique security challenges. We test for prompt injection, data poisoning, model extraction attacks, and other AI-specific vulnerabilities that could compromise your system.
- Testing AI applications for data privacy and secure processing
- AI-driven security testing for prompt injection and adversarial attacks
- Model robustness validation against malicious inputs
- API testing for AI endpoints and data access controls
Explainability & Compliance checks
Where required, we test for traceability and explainability. We validate model predictions, alignment with business expectations, and compliance with ethical AI artificial intelligence software testing and market guidelines, including regulatory requirements.
- Bias detection and fairness validation across user groups
- Model behavior analysis and prediction validation
- Testing helps ensure AI decisions are transparent and justifiable
- Compliance testing for AI regulations and industry standards
Feedback loop & Integration testing
We test how your intelligent systems behave when live inputs change the model over time — validating retraining processes, protecting against model drift, and ensuring seamless integration with existing software.
- Continuous monitoring setup for production AI performance
- Testing AI systems within broader DevOps pipelines
- Model drift detection and retraining validation
- Integration testing for AI-based components and APIs
User experience & Output validation
AI-powered doesn’t mean user-proof. We test how your AI behaves in actual user flows across different channels — ensuring responses are accurate, usable, safe, and consistently helpful.
- Cross-platform testing for AI applications and services
- Manual testing of AI interactions and user scenarios
- Output quality validation for generative and predictive AI
- User interface testing for AI-driven features
The Impact of Risk-Focused AI Product Testing
For the Head of QA
Gain full control and confidence over your AI pipeline by managing key risks like model bias and drift for every release.
For the CEO
Mitigate brand and business risks, from compliance failures to security issues, so you can scale and grow with confidence.
For the Product Manager
Ensure a superior user experience by eliminating hallucinations and guaranteeing your AI product performs exactly as designed.
For the Dev Team
Get clear, actionable bug reports that help you quickly fix unpredictable AI behavior and get back to building.
Need to discuss a specific AI project testing or your current AI QA framework? We’re here to help.
Our Awards and Achievements
“Thanks to TestFort QA Lab’s efforts, the client has seen reduced production defects, improved test coverage, and faster release cycles. The team is effective in project management, timely delivering test reports, and responsive to the client’s needs. The team’s testing thoroughness is remarkable.”
“TestFort has played a critical role in the development of HuffingtonPost.com. They have been able to become a part of the core team very quickly and develop amazing features that perform under the highest performance and demand requirements possible. They possess the highest level of business cooperation, an outstanding sense of responsibility and delivery of quality work…”
“After working with numerous companies around the world and resulting in differing experiences for our holding, we came across TestFort and started to work with them on a single project and limited resources. Thanks to very professional technical strategy, well-managed approach and methods in implementation and taking our solution to market and the team’s exceptional dedication, since that time our cooperation significantly evolved, we extended our team with more professional resources…”
“TestFort QA Lab’s work was productive and highly critical for the client’s success. The team communicated regularly with the client, allowing them to provide their feedback about the progress. They’ve met the company’s expectations and they were always willing to help the client.”
Why TestFort for AI Testing Services
Adopting new tech since 2001
Over two decades of QA experience means we quickly understand new technologies like AI and build effective testing strategies.
Smart
coverage
From model validation to bias testing — we cover all aspects of AI quality assurance using proven testing methodologies.
Real-world AI
testing
We test with realistic data and scenarios, not just clean lab conditions — ensuring your AI works for actual users.
Flexible QA
models
Custom testing strategies for your AI stack — whether you need model validation, full-cycle QA, or specialized generative AI testing.
