NEXT-GENERATION QUALITY ENGINEERING
AI Testing Services Intelligent Testing. Reliable AI.
Accelerate software quality with AI-powered testing while validating the accuracy, reliability, safety and performance of AI-driven applications.
Dawn combines AI-augmented testing services with structured AI/ML model testing services to help organizations build, validate and release intelligent software with greater confidence.
AI-Powered Testing
AI/ML Model Validation
GenAI & Agent Testing
Responsible AI
AI-POWERED QA
LLM EVALUATION
MODEL TESTING
AI AGENTS
THE NEXT EVOLUTION OF SOFTWARE QUALITY
AI Is Changing Software. Testing Must Evolve With It.
Artificial intelligence is transforming both how software is tested and how modern applications behave. Quality engineering must address both.
Traditional software testing focuses on predictable inputs and expected outputs. AI-enabled applications introduce probabilistic behavior, variable responses, data dependencies and new reliability risks.
At the same time, AI-powered software testing enables QA teams to improve test design, accelerate automation, identify patterns and prioritize quality risks more effectively.
As an AI testing company, Dawn approaches these challenges through two complementary disciplines: applying AI to strengthen software testing and evaluating AI systems to ensure they meet business, quality and safety expectations.
TWO DIMENSIONS OF AI TESTING
Test Software With AI. Test AI With Confidence.
AI testing is not a single discipline. It brings together intelligent software testing and the specialized validation of AI-powered systems.
Our AI-augmented testing services focus on improving the effectiveness of conventional QA through intelligent test generation, automation support, defect insights and risk-based execution.
Our AI/ML model testing services focus on evaluating model outputs, prediction quality, robustness, fairness and behavior under real-world conditions.
Together, these disciplines help organizations improve delivery efficiency while addressing the distinct quality challenges of intelligent applications.
AI QUALITY ENGINEERING
TWO COMPLEMENTARY PILLARS
PILLAR 01
AI-Powered Software Testing
Intelligent test design, automation, prioritization and QA insights
PILLAR 02
AI/ML Model Testing
Accuracy, fairness, robustness, reliability and model evaluation
SPECIALIZED COVERAGE
GenAI, LLM & RAG Testing
Response quality, groundedness, hallucination and safety assessment
ADVANCED COVERAGE
Agentic AI Testing
Tool-use correctness, workflow reliability and autonomous behavior
OUR AI TESTING SOLUTIONS
Comprehensive AI Testing Services.
From AI-powered software testing and intelligent test automation to AI/ML model validation, GenAI evaluation and agent testing, we address the evolving quality requirements of modern software.
01 / AI TESTING
AI-Powered Software Testing
Enhance traditional software quality assurance with AI-assisted test design, intelligent automation and data-driven defect analysis.
- AI-assisted test case generation
- Intelligent regression testing
- Defect prediction and analysis
02 / AI TESTING
AI-Augmented Test Automation
Improve the efficiency and maintainability of automated testing with intelligent test generation, prioritization and self-healing approaches.
- AI-assisted script generation
- Self-healing automation
- Risk-based test prioritization
03 / AI TESTING
AI/ML Model Testing Services
Evaluate machine learning models for functional correctness, prediction quality, robustness, fairness and production readiness.
- Model accuracy and performance
- Bias and fairness evaluation
- Robustness and drift testing
04 / AI TESTING
Generative AI & LLM Testing
Validate generative AI applications and language models for response quality, factual consistency, safety and task effectiveness.
- Hallucination and factuality checks
- Prompt and response evaluation
- Safety and guardrail validation
05 / AI TESTING
AI Agent Testing
Assess AI agents and autonomous workflows for tool-use accuracy, execution reliability, decision consistency and recovery behavior.
- Agent workflow validation
- Tool calling and integration tests
- Failure recovery and guardrails
06 / AI TESTING
RAG Application Testing
Evaluate retrieval-augmented generation systems for retrieval relevance, grounded responses and end-to-end answer quality.
- Retrieval accuracy and relevance
- Context-grounded answer evaluation
- Citation and source validation
07 / AI TESTING
AI Security & Responsible AI Testing
Assess AI applications for adversarial behavior, unsafe outputs, prompt injection risks and responsible AI considerations.
- Prompt injection assessment
- Sensitive information exposure
- Safety and policy compliance checks
08 / AI TESTING
AI Performance & Reliability Testing
Evaluate AI application latency, throughput, scalability, inference reliability and behavior under changing workloads.
- Inference latency and throughput
- Load and concurrency testing
- Reliability and fallback validation
Building an AI-powered application or looking to modernize your QA process? Let's identify the testing approach that fits your technology and business risks.
Discuss Your AI Testing Requirements
OUR AI QUALITY ENGINEERING APPROACH
Beyond Pass or Fail. Measure What Matters.
AI systems require quality evaluation that considers both measurable performance and real-world behavior. Our approach combines established QA practices with AI-specific evaluation techniques.
Risk-Based AI Quality Strategy
Align testing priorities with business objectives, model behavior, application risks and user expectations.
Evaluation Dataset Design
Create representative test datasets, edge cases and evaluation scenarios for meaningful AI validation.
Functional & Behavioral Validation
Test expected behavior, unexpected outputs, integration correctness and critical user journeys.
Quantitative & Qualitative Evaluation
Combine measurable quality metrics with human review and scenario-based assessments.
Adversarial & Negative Testing
Evaluate system responses to ambiguous, malicious, incomplete and out-of-distribution inputs.
Continuous Evaluation
Support repeatable testing across model updates, prompt changes, application releases and production feedback.
AI TESTING LIFECYCLE
A Structured Process. For Intelligent Systems.
Our AI testing process connects business requirements, evaluation criteria, representative datasets and continuous quality improvement.
Discover & Assess
Understand the AI use case, model architecture, dependencies, business risks and quality requirements.
Define Quality Criteria
Establish evaluation dimensions, acceptance thresholds and test coverage priorities.
Prepare Evaluation Data
Develop test scenarios, representative datasets, edge cases and expected behaviors.
Execute AI Evaluations
Perform functional, model, security, performance and behavioral assessments as applicable.
Analyze & Improve
Investigate failures, identify quality gaps and recommend improvements to models, prompts or workflows.
Regression & Monitoring
Revalidate changes and establish repeatable quality checks for ongoing releases.
AI APPLICATION COVERAGE
Testing Across the AI Application Landscape.
We tailor testing strategies to different AI architectures, use cases, workflows and business environments.
Generative AI Applications
Conversational assistants, content generation and enterprise GenAI applications.
AI Agents & Automation
Autonomous agents, tool-using assistants and multi-step intelligent workflows.
RAG & Knowledge Assistants
Enterprise search, document intelligence and knowledge-grounded AI applications.
Machine Learning Products
Predictive analytics, classification, recommendation and decision-support models.
Customer Experience AI
AI chatbots, virtual assistants and automated customer interaction platforms.
Enterprise AI Systems
AI-enabled enterprise applications, internal copilots and integrated business workflows.
BUSINESS VALUE
Better AI Quality. Stronger Business Confidence.
Structured AI testing helps organizations identify quality risks, improve system behavior and make more informed deployment decisions.
More Reliable AI Outcomes
Identify incorrect, inconsistent and unsafe behavior before it affects users.
Improved Model Quality
Evaluate AI systems against measurable quality criteria and business expectations.
Faster Testing Cycles
Use AI-assisted testing and repeatable evaluations to improve QA efficiency.
Responsible AI Confidence
Assess fairness, transparency-related behavior and safety considerations.
Continuous Release Readiness
Detect regressions across model, prompt and application changes.
Actionable Quality Insights
Translate evaluation results into prioritized improvement recommendations.
AI TESTING TOOLKIT
Modern Tools. Measurable AI Quality.
Testing tools and frameworks are selected based on application architecture, model type, evaluation requirements and existing engineering workflows.
AI QUALITY ENGINEERING TOOLKIT
AUTOMATION · EVALUATION · OBSERVABILITY
FLEXIBLE ENGAGEMENT MODELS
AI Testing That Fits Your Product Lifecycle.
Engage Dawn for focused AI quality assessments, dedicated testing support or ongoing evaluation programs.
AI Quality Assessment
A focused engagement to evaluate an AI application, model or intelligent workflow.
- Use-case and risk assessment
- Evaluation strategy and datasets
- Defined testing scope
- Quality findings and recommendations
Dedicated AI Testing Team
Extend your engineering team with dedicated resources for AI testing and quality engineering.
- Dedicated testing capacity
- Model and application evaluation
- Test automation and regression
- Agile delivery integration
Continuous AI Quality Assurance
Establish recurring testing for evolving AI applications, models and intelligent workflows.
- Scheduled evaluation cycles
- Prompt and model regression testing
- Quality monitoring and reporting
- Continuous improvement support
WHY DAWN
An AI Testing Company. Grounded in Quality Engineering.
Dawn brings an established software testing background and a structured quality engineering perspective to the challenges of AI-enabled applications.
Proven Track Record
An established software testing and quality engineering delivery background.
Experienced Testing Team
An experienced QA organization supporting diverse testing requirements.
100+ Product Companies
Experience supporting product engineering teams and software release quality.
AI & Quality Engineering Perspective
An approach that brings software QA disciplines into AI-enabled applications.
End-to-End Testing Coverage
Support for application behavior, integrations, models, security and performance.
Flexible Engagement Models
Project-based assessments, dedicated QA capacity and ongoing testing support.
BUILD CONFIDENCE IN INTELLIGENT SOFTWARE
Discuss Your AI Testing Needs
Your AI Should Be Intelligent. And Dependable.
Whether you need AI-powered software testing, AI-augmented testing services or specialized AI/ML model testing, let's define a quality strategy for your application.
FREQUENTLY ASKED QUESTIONS
AI Testing FAQs.
Common questions about AI-powered software testing, AI/ML model validation, GenAI evaluation and AI testing engagements.
01 What are AI testing services?
AI testing services include using artificial intelligence to improve software testing and evaluating AI-powered applications for quality, reliability, safety and performance.
These services may cover intelligent test automation, AI/ML model testing, LLM evaluation, RAG testing and AI agent validation.
02 What is AI-powered software testing?
AI-powered software testing applies artificial intelligence to traditional quality assurance activities such as test case generation, test automation, regression prioritization and defect analysis.
It can help testing teams improve efficiency, identify important scenarios and reduce repetitive work, while retaining human review and quality oversight.
03 What is the difference between AI-powered testing and AI/ML model testing?
AI-powered testing uses AI to improve the testing process for software applications.
AI/ML model testing evaluates the behavior and quality of machine learning models, including prediction accuracy, robustness, fairness and reliability.
04 How do you test Generative AI and large language models?
Generative AI testing evaluates model responses against defined quality criteria, representative prompts and expected behavior.
Common evaluation dimensions include factual correctness, hallucination risk, relevance, response consistency, safety, instruction adherence and application-specific outcomes.
05 What is RAG testing?
Retrieval-Augmented Generation (RAG) testing assesses both information retrieval quality and the responses generated from retrieved content.
It can include retrieval relevance, context coverage, groundedness, factual consistency and source attribution checks.
06 How do you test AI agents?
AI agent testing evaluates whether an agent selects appropriate actions, uses tools correctly and completes multi-step workflows reliably.
It also examines error recovery, permission boundaries, unsafe actions, integration behavior and task completion against defined expectations.
07 Can AI testing be automated?
Yes. Many AI evaluation scenarios can be automated using test datasets, evaluation frameworks, regression pipelines and measurable quality criteria.
Human evaluation remains important for subjective responses, complex business contexts, safety assessments and ambiguous model behavior.
08 How do you evaluate AI model accuracy and reliability?
Evaluation depends on the model type and business use case. Predictive models may be assessed using accuracy, precision, recall and other suitable metrics.
Generative models may require response quality, factuality, relevance, robustness and human evaluation criteria. Testing should also cover edge cases and changing inputs.
09 How much do AI testing services cost?
AI testing costs depend on application complexity, model type, evaluation scope, dataset requirements, integrations, testing frequency and automation needs.
Dawn can structure an engagement as a focused AI quality assessment, dedicated testing capacity or an ongoing quality assurance program.
10 Why choose Dawn as your AI testing company?
Dawn brings a 10+ year software quality engineering background, a 300+ member testing team and experience supporting more than 100 product companies.
Our approach combines established QA practices with AI-specific evaluation methods to address both conventional software quality and intelligent application risks.