Generative AI Model Testing for a Global Social Media Giant

Client

A Leading Global Digital Platform Provider

A multinational technology company operating large‑scale digital platforms with a vast global user base. Its ecosystem spans multiple interconnected consumer services, supporting high‑volume interactions and shaping everyday digital engagement across regions and devices.

Challenge

Racing to Market While Getting Everything Right

• Relentless Competitive Pressure
  The generative AI (GenAI) market was already defined by high-performing foundation models setting the bar for quality, reasoning, and trust. For the client, launching proprietary models was a strategic mandate. However, speed without quality was untenable—any shortfall in accuracy or relevance would weaken credibility and stall broader AI ambitions.
• Time-to-Market Was Non‑Negotiable
  The client needed to move from development to deployment at pace, without compromising rigor. Skipping or compressing validation was not an option—every shortcut threatened user experience, product stability, and brand equity.
• Always‑On Release Velocity
  Continuous delivery was core to the client’s operating model. Frequent product updates introduced constant change in how AI models interacted with applications. Maintaining consistent, correct behavior across releases required a testing approach that could scale and evolve as fast as the business shipped.
• Evolving Global Compliance Mandates
  Regulatory expectations for GenAI were rapidly expanding across regions. The client needed confidence that every model met diverse requirements—from privacy and safety to content governance. A single compliance lapse could trigger regulatory action, reputational damage, or platform constraints.
• Dual Model Complexity
  The AI landscape spanned two demanding model families: language models driving reasoning and generation, and multimodal models powering visual and video experiences. Each required specialized validation, yet both had to function seamlessly within a unified product ecosystem—raising the bar for testing depth, consistency, and coordination.

Solution

Hexaware Builds an End-to-End AI Quality Engine

To meet the scale and sophistication of this challenge, the client turned to Hexaware—a trusted technology partner with deep expertise in AI quality engineering. Hexaware deployed a comprehensive suite of testing capabilities purpose-built for GenAI environments, including:

• AI Quality Engineering: End-to-end validation frameworks for foundational model performance
• Responsible AI (RAI) Testing: Evaluation against ethical, safety, and compliance benchmarks
• Functional and Usability Testing: Ensuring models behaved correctly and intuitively across product workflows
• Persona-Based Testing: Validating AI tone, behavior, and alignment with real-world user personas
• E2E Test Design: Full-stack test architecture for multimodal product experiences
• Continuous Feedback Loop Testing: Verifying adaptive learning and iterative model improvement
• Multimodal Product Testing: Specialized validation for image, video, and language generation capabilities

Together, these services formed a holistic quality assurance (QA) layer that wrapped around every dimension of the client’s AI program. Let’s dive in to see Hexaware’s approach:

Building the Test Foundation

The engagement began by establishing a robust, scalable test infrastructure. Hexaware collaborated closely with the client’s engineering teams to understand the architecture of the foundational models and map all integration touchpoints across their product ecosystem. From this foundation, the team generated contextual prompts and utterances specifically designed to surface real-world failure modes—not just surface-level errors, but the subtle, scenario-specific breakdowns that only emerge under carefully constructed conditions. Automation was embedded from the start, enabling broad scenario coverage at the speed the client’s release cycle demanded.

Functional and Usability Testing: Getting the Basics Exactly Right

With the test framework in place, Hexaware turned to the fundamentals. Functional testing validated that the AI models processed inputs accurately and returned outputs that matched expected behaviors across a wide range of workflows. Usability testing went further, assessing whether the AI experience felt natural, intuitive, and genuinely useful to the end user. Special attention was paid to text comprehension, problem-solving capability, and tone adaptability, ensuring the models could flex appropriately across different user contexts and content types.

Persona and Scenario Testing: Making AI Feel Human

AI models don’t interact with abstract users—they interact with people, each with distinct expectations, communication styles, and needs. Hexaware introduced persona and scenario-based testing to validate that the AI’s tone, behavior, and response patterns were genuinely aligned with real user profiles. By designing test scenarios around specific personas—from casual social media users to power creators and enterprise clients—the team was able to surface misalignments that standard functional tests would never catch. This layer of testing ensured the AI didn’t just work correctly; it felt right.

Continuous Feedback Loop Testing: Teaching the AI to Learn

The client’s models were designed to evolve based on user feedback—improving recommendations, refining responses, and becoming more relevant over time. Hexaware implemented continuous feedback loop testing to verify that this adaptive mechanism was functioning as intended. The team assessed whether the AI was genuinely incorporating feedback, learning from interaction patterns, and improving in measurable, repeatable ways. This wasn’t a one-time validation—it was an ongoing discipline embedded into the QA process.

Multimodal E2E Testing: Validating a Visual AI Universe

The final, and perhaps most technically demanding, dimension of the engagement focused on the client’s linear mixed model (LMM) capabilities. Hexaware designed and executed end-to-end tests for a full suite of multimodal products—covering image generation, image editing, video editing, and video language translation. Each product type required its own validation logic, acceptance criteria, and failure taxonomy. The E2E test design brought these together into a unified framework that could assess quality, coherence, and compliance across the client’s entire visual AI portfolio—ensuring that the same standards applied to language also held in the visual domain.

Benefits

Measurable Quality Gains Across Every AI Dimension

• 600+ RAI Defects Detected — Responsible AI Embedded
  Hexaware identified and mitigated 600+ critical Responsible AI (RAI) issues across safety, bias, fairness, and compliance—reducing regulatory risk and operational exposure before production deployment.
• 150+ Integration Defects Resolved — Ecosystem Stability Protected
  Over 150 integration failures were detected early across application touchpoints, preventing feature breakage, experience degradation, and cross‑product inconsistencies at scale.
• 3X Faster Time‑to‑Market — Competitive Advantage Delivered
  Automation‑first testing and continuous QA cut release cycles by threefold—enabling faster launches, quicker market response, and sustained momentum in the GenAI race.

Summary

A Platform Now Ready for the AI-First Future

The client is actively rolling out proprietary foundational AI models across their ecosystem at unmatched speed and scale. With Hexaware as a strategic QA partner, automation‑driven AI quality engineering now keeps pace with accelerating release cycles. Responsible AI is embedded, continuous, and operational. Positioned to scale confidently, the client is set not just to compete—but to lead in the GenAI era.

Explore Our Generative AI Capabilities
Discover how Hexaware helps enterprises test, scale, and govern GenAI with automation‑first quality engineering.