Designing Cord’s Market Intelligence tool meant translating complex recruitment data into a seamless, conversational interface—similar to ChatGPT—that recruiters could trust for fast, actionable insights. My approach focused on balancing usability, trust, and clarity while working closely with engineers, product managers, and end users.

I began with user research: speaking directly with recruiters to understand how they currently gather market insights like salary data, role demand, and competitor benchmarks. The common thread was inefficiency—users were jumping between tools, reports, and Google searches. There was also a high cognitive load when trying to interpret raw data.

Armed with these insights, I mapped out key use cases and user intents, then worked on crafting natural language interaction flows that felt approachable but remained focused. I created a series of wireframes and prototypes to explore different interface models—ranging from a full conversational UI to a hybrid input with structured suggestions and filters. Ultimately, I designed a clean, prompt-based interface that allowed for free-form questions, but offered smart autocomplete suggestions to guide users and reduce errors.

I also worked closely with engineers to shape how responses were presented. Raw text outputs felt too opaque, so I designed modular response components—tables, charts, and concise summaries—so data was visually scannable, credible, and easy to act on.

Problems Faced & How I Solved Them

1. Ambiguity in User Queries
Users often asked vague or broad questions (e.g., “What should I pay a software engineer?”). This made responses inconsistent and less useful.
Solution: I designed contextual prompts and suggested queries to guide users toward more precise inputs. I also included lightweight follow-up questions from the AI to clarify ambiguous queries.

2. Trust in AI-Generated Data
Recruiters were hesitant to trust AI-generated answers without knowing the source.
Solution: I added transparency features, like “data sourced from…” tooltips, and made it clear when data was based on real-time insights vs. historical averages. I also used UI treatments (color, tone, spacing) to separate facts from suggestions.

3. Speed vs. Usefulness
There was a tension between delivering fast responses and delivering good ones.
Solution: I introduced skeleton loaders and subtle conversational delays that gave the system enough time to generate high-quality answers, while still feeling responsive and fluid to the user.

This project challenged me to design not just a product interface, but a conversation model—ensuring that the AI felt helpful, transparent, and trustworthy in a space where accuracy is critical. By staying close to real user needs and iterating quickly with the team, we built a tool that meaningfully improves how recruiters make market-driven decisions.