AI Design
B2B Enterprise
ERP SaaS
Shipped
Scaling Interview Questionnaires with AI
OVERVIEW
FlairX is an interview-as-a-service platform where questionnaires are used to hold AI Interviewers, Expert Interviews, AI Phone Screenings, and Resume Screenings. Hand-drafted questionnaires was a model that couldn't survive 100+ requests a day.
GOAL
Scale questionnaire creation by replacing manual admin drafting with customer-led, AI-powered creation.
Hand drafted questionnaires couldn't scale to 100s a day.
CHALLENGES
I Identified Three Barriers To Scaling
01
SPEED
Nobody wanted to spend 30 minutes or 5+ hours building a questionnaire.
02
ADOPTION
A 25-field form of "Isn't this already in the job description I added?"
03
TRUST
One-lick AI wasn't specific enough to trust.
SOLUTIONS
Designed A Flow Made Scalable Through a Review and Refine Model
01
Human-in-the-Loop Flow
IMPACT
Improved customer satisfaction since AI handles the heavy lifting while users stay in control of the output.
02
Automated Requirement Extraction
IMPACT
Increased adoption since AI fills the setup form automatically, eliminating redundant data entry at both stages.
OLD
NEW
03
Single-Page View Over Multi-Tab Navigation
IMPACT
Reduced time on task when users shifted from creating to reviewing with fewer clicks and less context switching.
OLD
NEW
IMPACT
Results That Validated the Approach
7x
Efficiency
End-to-end drafting dropped from at least 30 minutes of manual work to under 3 minutes of AI-assisted review.
4x
Increased Adoption
Customers moved off of shadow workflows and into the native platform once the friction points were removed.
PROCESS
I Worked Across Design, Product, Engineering, and with the Founder to Take This Feature from Concept to Shipped
2-Day Warroom
An ideation sprint with the full cross-functional team.
Contextual Inquiry
Observed and interviewed users using competitions.
Customer Reviews
Tracked and implemented customer feedback since MVP release.
Guerrilla Research
Self-initiated interviews conducted as access and opportunity allowed.
Rapid Iteration and Validation with AI
Utilized Claude and Figma Make for prototyping resulting in faster validation and less wasted build time.
Claude
Figma
Figma Make
DISCOVERIES
I Mapped the Competitive Landscape to Find FlairX's Opportunity
DESIGNING FOR AI
I Advocated for Human in the Loop Over Full Automation
Users should feel in control, not ambushed by automation. A pre-generation calibration step led to more nuanced questions tailored for the role.
I Dropped Features That No Longer Provided Value
AI INTERACTION
Choosing the Right Interaction Model
LATE BUSINESS PIVOT
Adapting to a New User in a Last-Minute Service Model Shift
OLD USER
Internal Admin
Core task · High familiarity · Low time pressure · Quality-focused
NEW USER
Customer
Obstacle task · Low familiarity · High time pressure · Just needs it done
DELIVERABLES
End to End Flow in Action
Notable Design Nuances
AI REASONING TO BUILD TRUST
SKIP WHILE GENERATING
COMPLETED GENERATION NOTIFICATION
EDITABLE ANSWER GUIDE
QUESTION BANK
REFLECTION
What I Learned
Clarity beats visual cleanliness
Explicit controls outperform elegant but hidden interactions.
AI UX patterns don't always transfer
Mental models matter more than visual familiarity (our questionnaire ≠ a document)
Break complexity into steps
Sequential generation is more predictable than all-at-once.