CAI (Clockwork AI Feature)
The CAI project was a groundbreaking 0-to-1 initiative, marking the launch of Clockwork's first AI product. Spanning 5–6 months of effort over two years, it served as the MVP for this new business vertical. I led the design process as the sole designer, collaborating closely with key stakeholders, including the Product Manager, Developers, VP of Product, Customer Success, Account Managers, and Attorneys. My contributions encompassed conducting research to define the product roadmap, establishing a research and iteration process, and executing the design to ensure a seamless user experience.
RESULTS
RESEARCH OVERVIEW
For this project, I worked closely with teams across Customer Success, Sales, Marketing, and Support—marking my first collaboration with them. Initially, feedback was scattered across Google Docs and Sheets, leading to inefficiencies and longer meetings.
I took ownership of streamlining the process by identifying the Idea Portal in Aha!—an underutilized resource with 200–280 pieces of feedback. After proposing it as a centralized hub, I led the effort to clean and migrate the data, creating standardized guidelines for logging feedback.
Results:
Reduced meeting times from 3 hours to 45 minutes.
Increased the number of ideas collected from 200 to over 1,000.
Established the portal as a key resource for all future product research and project kickoffs.
ROADMAP IDENTIFICATION PROCESS
1. Research & Discovery
Mapped Recruiting Workflow: I analyzed the entire recruiting process, breaking it down into 8 stages (from business discovery to candidate evaluation).
2. Prioritization
Aligning Needs with Business Goals: I mapped user needs to the product’s business objectives to ensure solutions would have the highest impact.
Focused on 4 Critical Stages: Based on urgency and impact, I prioritized:
3. Roadmap Pivot
Shift in Focus to Candidate Evaluation: Based on emerging trends in user feedback, we also prioritized enhancing candidate evaluation for the 2024 roadmap, improving consistency and reducing bias
4. Roadmap Defined
Auto-Formatting Job Details
Users encounter manual tasks when formatting job descriptions, job requirements, and research criteria.
CAI could auto-generate job descriptions, requirements, and research criteria for users based on project data.
RESULTS
Auto-Generating Meeting Notes
Users face labor-intensive tasks when manually typing meeting summaries.
AI-powered meeting notes save recruiters time by automating note-taking.
RESULTS
Candidate Evaluation
Users now face challenges in efficiency and quality when evaluating high volumes of candidates quickly, resulting in rushed and inconsistent decisions.
Candidate Fit evaluates candidates as high, medium, low, or not a match, with insights based on hard and soft criteria.
