Summary
Sonata helped an innovative startup significantly improve the robustness and accuracy of its AI-based recruitment chatbot by leveraging a design-led testing methodology. The engagement resulted in a high-performing, scalable and fraud-resistant recruitment bot that is adaptable for future use across industries like retail banking.
Client Overview
An innovative startup that built an AI-based chatbot primarily serving the recruitment industry.
Pressure Points
The client needed to ensure its AI chatbot could accurately replicate a human recruiter’s behavior, scale across industries and deliver measurable productivity improvements while minimizing defects during real-world deployment. Other key customer expectations include
Designing for industry-agnostic scalability
Generating diverse test data including audio, video and text
Eliminating false positives in bot behavior analysis
Balancing positive and negative test paths to validate learning capabilities
Solutions
Sonata engineered a rigorous design testing strategy focused on simulating real-world recruiter-jobseeker interactions. By building deep integration points and using AI-focused tools, Sonata enabled scalable, high-quality testing beyond standard functional scenarios.
Embedded hooks to test bot core functions without a UI
Simulated user interactions across audio, video and text
Crafted voluminous, diverse test data to replicate real environments
Covered bot behaviors including error management, navigation, onboarding and personality
Used Botium for automated testing with dynamic data inputs
Performed extensive design testing to validate learning abilities
Results that Speak Volumes
Delivered a robust, fraud-resistant AI chatbot
Enabled smooth scaling beyond recruitment into future industry domains
Created a learning-capable bot through advanced test simulations
