A Space for Thoughtful 
About the Client
A global provider of business process management and digital transformation services that leverages AI, automation, and analytics to deliver intelligent customer experience and operational efficiency solutions. The organization supports global enterprises in optimizing workflows, improving decision-making, and accelerating innovation through data-driven technologies and AI-powered platforms.
Challenges They Faced
The organization encountered multiple challenges while evaluating AI-generated videos for text-to-video workflows:
- Ambiguity in Video Selection Criteria – Determining the most appropriate AI-generated video from multiple outputs based on a text prompt required consistent and objective evaluation standards.
- Balancing Prompt Alignment and Visual Quality – Videos often varied in how accurately they matched the prompt versus how realistic and visually coherent they appeared.
- Short and Low-Clarity Outputs – Generated videos were brief and lacked clarity, making it difficult to assess context, motion accuracy, and scene fidelity.
- Subjective Decision-Making – Inconsistent reviewer judgments led to variability in video selection outcomes and reduced reliability of the evaluation process.
- Mismatch Between Prompt Intent and Visual Output – Selected videos sometimes failed to fully capture key entities, actions, or scene dynamics described in the prompt.
Solutions We Offered
A structured evaluation framework was implemented to standardize video selection and improve alignment between prompts and AI-generated outputs.
- Video–Text Alignment as the Primary Filter – Reviewers first verified that each video accurately reflected the core elements of the prompt, including key entities, actions, environment, and camera movement, ensuring the selected output matched the intended narrative and context.
- Comprehensive Visual Quality Assessment – When multiple videos aligned with the prompt, evaluators compared realism, motion smoothness, visual stability, and overall coherence to identify the most natural and production-ready output.
- Tie-Breaker Evaluation Criteria for Close Matches – In scenarios where alignment and quality were comparable, reviewers selected the video that appeared most natural, aesthetically appealing, and contextually complete, ensuring a clear and confident final choice.
- Standardized Decision Framework for Consistency – Clearly defined evaluation guidelines minimized subjectivity, enabled consistent decision-making across reviewers, and improved the reliability and repeatability of video selection outcomes.
Results We Delivered
- Established a clear and repeatable evaluation framework for selecting AI-generated videos based on prompt alignment and visual quality.
- Improved consistency in reviewer decisions, reducing subjectivity and enhancing the reliability of video selection outcomes.
- Increased accuracy in matching video outputs to prompt intent, ensuring better representation of entities, actions, and scene dynamics.
- Enhanced overall visual quality of selected videos by prioritizing realism, coherence, and smooth motion.
- Strengthened workflow efficiency by enabling faster and more confident video evaluation and selection.
- Provided a scalable evaluation model that supports ongoing improvements in AI-generated video quality and usability.
- Evaluated 8,693 AI-generated video tasks, ensuring improved consistency, accuracy, and alignment with prompts and visual quality