AI SaaS - AgileVerify

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315, Ganesh Glory, Jagatpur Road, Off SG Highway, Gota, Ahmedabad, Gujarat - 382470
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info@agileverify.com
+1 650 431 2251

QA Engineering for AI SaaS

QA Automation and Testing for an AI SaaS Platform.

A leading AI SaaS platform partnered with us to enhance the quality and reliability of their web and mobile applications. With rapid product development and AI-driven features, the client needed a robust automation testing strategy to ensure seamless performance, accelerate releases, and maintain platform stability.

Client Challenges

The client faced multiple critical challenges that were affecting product quality, delivery speed, and overall customer experience:

No structured regression suite, leading to recurring defects

Delayed defect detection due to lack of proactive testing

Limited depth in functional, UI, performance, and AI-specific testing

No Agile QA methodology aligned with rapid development cycles

High reliance on manual testing, reducing efficiency

UI inconsistencies due to missing Figma design verification

Client Goals

To overcome these challenges, the client defined the following objectives:

Implement scalable automation for web and mobile applications

Increase test coverage and reduce manual effort

Ensure accuracy, reliability, and stability of AI-driven features

Integrate continuous testing into CI/CD pipelines

Improve UI consistency and accessibility compliance

Introduce specialized AI model monitoring and validation processes

Products Under Testing

Our automation efforts focused on the client’s core digital platforms:

Web Application

Mobile Application (iOS & Android)

AI-powered Chatbot and Backend Services

Our Approach

We implemented a complete end-to-end QA strategy, including automation, UI/UX validation, accessibility testing, and AI-specific quality engineering.

1. Automation Engineering

Developed scalable automation frameworks for web and mobile

Created modular, reusable regression and smoke test suites

Integrated test automation into Jenkins CI/CD pipelines

2. Functional, UI, and Performance Testing

Comprehensive functional and regression testing

API validation for backend service reliability

Load and performance testing to ensure stability under heavy traffic

3. Figma Design Verification

Compared implemented UI with Figma designs for visual accuracy

Validated spacing, color consistency, typography, and component usage

Ensured adherence to the design system across platforms

4. Accessibility Testing

Performed WCAG 2.1 AA accessibility checks

Tested keyboard navigation, screen reader compatibility, and color contrast

Validated ARIA labels, focus management, and accessible error states

5. AI-Specific Quality Engineering Functional Accuracy & Response Validation

Verified intent detection, entity extraction, and chatbot response accuracy

Developed datasets for automated accuracy scoring

- Behavior Consistency & Edge-Case Handling

Tested multilingual inputs, slang, ambiguous queries, and corner cases

Ensured predictable fallback and stable behavior

- NLP Model Evaluation

Measured precision, recall, and F1-score

Checked contextual relevance and hallucination frequency

- Performance & Scalability Under Load

Load-tested AI inference APIs for throughput and latency

Ensured stable and fast responses under high concurrency

- Security & Data Privacy Testing

Validated AI endpoint security and data protection mechanisms

Ensured safe handling of user data, logs, and PII

- AI Model Drift & Regression Monitoring

Monitored model performance trends over time

Set up alerts for accuracy drops or unexpected behavior changes

Result Highlights

50%+ reduction in manual testing through automation

Faster and more reliable releases supported by CI/CD pipelines

Improved UI consistency with Figma design validation

Higher accessibility compliance aligned with WCAG 2.1 standards

Enhanced accuracy and stability of AI-driven functionalities

Scalable QA framework supporting continuous innovation

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QA Engineering for AI SaaS

QA Automation and Testing for an AI SaaS Platform.

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