Services
Today’s Conversational AI systems operate in a dynamic, probabilistic environment where identical inputs can produce varied outputs. A robust testing framework validates intent recognition, dialogue flow, tone, and persona, safeguarding customer experience, and brand reputation. By simulating real-world scenarios and automating test cases, businesses can reduce errors, accelerate release cycles, and scale confidently. Because agentic systems are adaptive and tool-using, continuous oversight and tailored governance are essential, with blackbox and whitebox testing approaches working together to ensure reliability, safety, and trust in conversation quality and personalization.
Synth DiLogQA offers a structured black box testing framework specifically tailored for conversational AI agents, guiding practitioners through a comprehensive validation cycle.
Synth DiLogQA ensures a foolproof conversational AI system by leveraging a comprehensive set of features including:
Black-Box Testing
Validates the system purely from a user’s perspective without requiring access to internal models or logic.
Automated Playbook-Driven Testing
Automatically generates conversational test cases based on predefined playbooks of flows, personas, and tones.
Persona Simulation
Tests AI responses against varied user types and emotional tones (e.g., polite, frustrated, casual) to ensure natural, context-sensitive engagement.
Comprehensive Coverage
Evaluates topic shifts, tone maintenance, edge cases, and functional flows in real-world usage contexts.
Human-in-the-Loop (HITL) Interface
Gives testers direct control to pick, customize, or extend test cases for flexible, hands-on validation.
Automated Validation and Reporting
Compares actual vs. expected outputs, generates pass/fail results, and logs defects with clear traceability.
Secure and Scalable Workflows
Enables teams to accelerate releases while maintaining data integrity and consistency across testing cycles.
The Mphasis Synth DiLogQA framework designs and validates personalized AI conversations through a systematic step-by-step process.
Accelerated Time-to-Market :
Cuts test case creation time by ~70% and execution time by ~60–75%, enabling faster product release cycles.
Improved Conversation Quality :
Ensures bots consistently deliver natural, safe, and goal-oriented interactions across different user personas and scenarios.
Reduced Manual Effort :
Automates routine testing to free up tester bandwidth, while still allowing expert intervention where judgment is needed.
Risk Mitigation :
Identifies defects and behavioral gaps early, reducing costly fixes post-deployment and improving compliance with business standards.
Enhanced User Experiencen :
Strengthens trust and engagement by ensuring AI responses are accurate, polite, and aligned with user expectations.
Scalable QA Process :
Provides a repeatable framework that grows with the complexity of conversational AI systems, keeping testing efficient without compromising coverage.