Improving AI Accuracy & Trust in Search Through Human-in-the-Loop Intelligence

A global leader in search partnered with GlobalLogic to improve the accuracy and relevance of its AI-powered search experience.

Through human-in-the-loop validation and content engineering, GlobalLogic enhanced how the model interprets, grounds, and presents information—significantly improving the factuality and quality of AI-generated results.

Context

A global leader in internet search is advancing its AI-powered search experience to deliver more personalized, context-aware results while maintaining high standards for accuracy and relevance.

As search evolves from link retrieval to direct answers, the challenge is no longer just generating responses—it is ensuring those responses are factually correct, properly grounded, and contextually appropriate across languages, formats, and user intent.

To support this shift, the client focused on improving how its AI systems interpret queries, connect information across sources, and present results in a way users can trust.

Improving how AI understands, grounds, and delivers search results through human-in-the-loop intelligence.

Our Role

GlobalLogic partnered with the client to improve AI model performance through human-in-the-loop validation and content engineering.

Our teams worked across the full lifecycle of AI response generation, combining structured data workflows with expert human judgment to strengthen how the model understands, validates, and delivers information.

Key areas of contribution included:

• Conversational data workflows: Designing and refining multi-turn interactions to better reflect real user behavior and query patterns
• Attribution and content grounding: Ensuring responses are supported by accurate sources, with clear and reliable citations
• Multi-turn reasoning and query expansion: Enhancing the model’s ability to handle complex, evolving queries and generate relevant follow-up context
• Cross-source content analysis: Evaluating both on-page and external content to improve completeness and accuracy of responses
• Multimodal understanding: Supporting features that enable users to ask questions about video content and receive meaningful, context-aware answers
• Content quality and formatting validation: Identifying and correcting citation, formatting, and structural issues to ensure outputs are clear and trustworthy

This approach enabled continuous refinement of the model in real-world conditions, improving not just outputs, but the system’s ability to consistently deliver high-quality responses at scale.

Impact

GlobalLogic’s contribution strengthened the quality, reliability, and contextual relevance of the client’s AI-powered search experience.

• Achieved the highest alignment across vendors based on CAI validation
• Improved factual accuracy and grounding of AI-generated result
• Delivered more relevant, context-aware responses across search experiences

As AI becomes the primary interface for search and discovery, performance is no longer defined by speed or generation alone. It is defined by accuracy, trust, and the ability to deliver the right answer in the right context.

This case demonstrates how GlobalLogic helps organizations move beyond model outputs to engineered AI systems that are grounded, explainable, and reliable in real-world use.