NLU Disambiguation Feature Redesign / AI

Callebe Silva

NLU Disambiguation Feature Redesign

Problem

Our AI platform required evolution toward generative capabilities, but risked losing major enterprise clients (Verizon, JP Morgan) who depended on our data labeling disambiguation workflows. The challenge: maintain familiar interaction patterns while enhancing functionality in a cleaner, more focused interface.

Impact

Preserved enterprise relationships with seamless transition that maintained all client contracts
Successful client migration from legacy to new platform with zero workflow disruption
✓ Product Owner feedback: "It went really smooth and worked perfectly"
✓ The disambiguation feature redesign became the final piece enabling our biggest clients to adopt the new platform

Solution

The redesign transformed a cluttered interface with scattered similarity metrics into a purpose-built disambiguation tool that:
Clarified context - Users now immediately understand they're in disambiguation mode with clear indication of what intent they're working with
Improved information hierarchy - Separated conflicting intents from utterances with clear visual distinction
Enhanced data representation - Redesigned conflict percentages to be more scannable with visual indicators
Simplified decision-making - Created a cleaner layout that focuses attention on resolving conflicts
Conducting deep user research with power users and employing an atomic research approach was fundamental. I designed a solution that preserved critical NLU capabilities while seamlessly integrating new generative AI features - including contextual prompt capabilities that enhanced rather than disrupted existing workflows.
My Role: UX/Product Designer working directly with engineering Timeline: 2 weeks Tools: Figma
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Posted Mar 26, 2025

Redesigned critical NLU interface, preserving workflows for enterprise clients while enabling new AI capabilities. 100% client retention achieved.

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Mentory: Reimagining Professional Connections