Helped ARCIS AI to validate AI accuracy and video performance at scale

PerfectQA partnered with ARCIS AI to validate advanced AI camera features, improve test coverage, and simplify QA handoff for complex edge-case scenarios—resulting in better platform resilience, smoother responsiveness, and improved real-world performance

99+

AI scenario coverage

80% +

Defect reduction

90%

Feature coverage

Industry

Platform

Web

Tools

Google Sheet

Services

Functional Testing, Regression Testing, Performance & Load Validation, AI Analytics & Edge-case Testing

AI-Driven camera testing, made predictable

ARCIS AI’s camera platform combines live video, AI detection, and GPT integration into a single high-performance product. QA needed to validate complex functionality, from frame-by-frame analysis to failover behavior—all under varying workloads

Challenge

The QA team had to handle

AI-enabled camera modules with multi-step logic and real-time decision trees
GPT-integrated features that required contextual and adaptive response validation
High variance in test scenarios due to environmental conditions and data types

Structured QA for advanced AI Use cases

PerfectQA created a clear documentation system for AI and GPT-integrated features, making it easy to reproduce and expand testing over time. We built out regression coverage while prioritising edge-case, load, and UX testing across modules

Solution

Scenario-based test design simplified coverage across AI analytics and video streaming
Real-time communication with devs enabled fast iterations and resolution cycles
Mobile and UI responsiveness testing ensured usability on all supported devices
Failover testing supported system resilience and recovery validation

PerfectQA helped us simplify and validate some of the most complex AI features in our platform. Their daily feedback and structured testing approach made our delivery process much smoother

Hardik

ArcisAI

Higher confidence in performance under real world workloads

PerfectQA’s QA strategy gave ARCIS AI a clearer view of platform stability under complex AI and streaming workloads. Our approach delivered both technical clarity and operational confidence—ensuring edge-case reliability without slowing development

Result

90% of features tested across AI, video, and GPT logic
Defects were caught earlier, reducing disruption and manual rework
Real-time and performance features were stabilized across device types
Client teams operated with more confidence during deployment and updates

Every product operates with different constraints, timelines, and risks. While the outcome here was achieved in a specific context, the approach is designed to adapt to varying architectures, team structures, and release cycles

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Published

Jan 27, 2026

Updated

January 27, 2026