AI-Assisted Project Intake Platform for Design Software Company

Designed an AI-powered intake solution that improved designer win rates by drastically increasing client confidence that designs would meet expectations. The natural language-driven platform currently in implementation represents a significant interaction paradigm shift, accelerating the path from client meeting to accurate proposal.

Role: UX Lead | Timeline: Several months (phased) | Year: 2024-2025

Situation: A leading design software company sought competitive differentiation beyond core products by addressing inefficiencies in how interior designers and architects initiate client projects. Manual note-taking during meetings risked missing details, while time-intensive project setup and design drafts occurred before contracts, putting designers at financial risk. Information loss between stakeholders caused budget, timeline, and scope conflicts across complex construction project networks.

Task: Lead UX strategy for AI-assisted project intake solution automating data capture and organization while preserving designer creative ownership and professional value perception, helping designers move from meetings to accurate proposals faster.

Action: I co-facilitated executive workshops with product, business, and technology leaders from both the company and AWS consultants, identifying stakeholder communication in smaller construction projects as addressable with their existing tools enhanced by AI. Encountering initial skepticism about AI devaluing creative work, I positioned AI strictly as assistant for data organization—never as creator—preserving designer ownership. I designed real-time meeting transcription and automatic project setup, created natural language interface for custom widget creation on adaptive project canvases, and developed decision logging to track choices and identify conflicts. I built client engagement features where AI guides inspiration capture for mood boards while human designers maintain all client-facing creative control, and created storyboards visualizing the intake-to-proposal journey demonstrating reduced time and improved alignment.

Result: The AI-assisted intake solution delivered measurable improvements in both designer efficiency and business outcomes. Designers reported significantly accelerated design processes through AI-organized project data, reducing time spent on administrative setup and allowing faster movement to creative work. More critically, clients expressed drastically increased confidence that final designs would meet their expectations and criteria—directly impacting designer win rates by improving clients' willingness to proceed rather than seeking alternatives. This confidence boost stemmed from better requirement capture, transparent mood boards, and comprehensive decision logging that prevented the misalignments common in traditional intake processes. Company stakeholders showed genuine excitement about the concept's potential, identifying opportunities for enhanced integrations with existing products and partners that would further strengthen the value proposition. The project is currently in implementation, with the natural language-driven adaptive canvas representing a significant interaction paradigm shift for professional design tools—moving from rigid interfaces to flexible, query-driven workspaces that adapt to individual designer workflows and project needs.