Case study
Yogen
From 0→1: an AI-driven hiring tool for recruiters and hiring managers enabling 40% cost reduction and 45% faster hiring.
I co-led the ground-up design of the platform — building a cohesive, production-ready experience, shaping the product vision, defining core AI-driven workflows, and establishing the design system. My contribution spanned product strategy and design through to brand strategy.
The problem
Hiring breaks even before it begins. Rushed intakes, unrealistic expectations, and poor candidate experience cost companies months and hundreds of thousands of dollars.
Most companies (with up to 1,000 employees) rush or skip intake meetings, causing unrealistic expectations, searches for unicorn candidates, and rejections of candidates who meet 80–90% of requirements. Struggles with sourcing and inefficient interviews further extend search times, inflate costs, and pull senior engineers from product work to navigate unstructured interviews.
Product vision — Recruit. Revolutionize. Retain.
Yogen was built to transform hiring into a strategic advantage, empowering teams to make confident, data-driven decisions.
By making hiring time and costs visible, Yogen shifts hiring from guesswork to strategy. It ensures teams build the right foundation, align on realistic expectations, and are equipped with the structure and tools needed to execute consistently — from role definition through offer — reducing wasted effort and enabling better outcomes.
Yogen in the hiring funnel: Workforce Planning → Role Definition → Sourcing & Screening → Candidate Interviews → Offer Management.
Welcome pages — setting expectations
Users are about to invest 20–60+ minutes. If they don’t know that upfront, they’ll abandon mid-flow or rush through and give bad inputs — which also ruins Yogen’s AI outputs downstream.
Decision: “Be radically transparent about time + value upfront.”
The welcome page sets context — “This takes ~60 min, here’s what you’ll get…” — so users opt in knowingly, with a clear explanation of what the AI will do with their inputs and the value exchange before committing time.
Designing for AI — transparency & trust
AI can feel like a black box. For users to trust and act on AI-generated insights, they need to understand what the AI is doing, why it’s recommending something, and how their data is being used. Without this clarity, even the best suggestions get ignored.
Decision: “Be transparent, give more context, tell them how their data will be used.”
I restructured the Responsibility Checker feedback into three tiers so recommendations feel grounded by leading with the problem:
- Risks — e.g. unrealistic expectations, hiring delays, unclear performance measurement.
- Warnings — e.g. candidate experience, team dynamics.
- Suggestions — define 30-60-90 day goals, clarify team structure, establish success metrics.
Additional AI clarity touchpoints: (1) introduced AI in the welcome page with a dedicated section on how AI assists and how data is handled; (2) a consistent AI indicator across the product so users can distinguish AI-generated content; (3) warnings clearly marking AI content as based on user inputs and needing review for accuracy, tone, and fit.
Hiring metrics — designing for two modes of thinking
Recruiters need quick validation (“Is this plan reasonable?”), but also need to justify decisions to hiring managers and leadership with detailed breakdowns.
Decision: “Quick-scan summary + detailed breakdown.”
Lead with key metrics, then let users dig into the full breakdown. Every number is traceable, every claim is backed — trust through transparency.
The audit surfaces a potential 45% reduction across hiring costs: time-to-fill 119 → 39 days (47%), total costs $10,418 → $6,261 per hire (40%), technical costs $7,168 → $3,361 (53%) — up to $93,762 saved annually across 20 hires, with a transparent “how these metrics are calculated” explanation.
Enhancing the form experience — making a 60-min form feel human
Intake forms in recruiting tools often feel like compliance checklists — clinical, detached, and exhausting. But intake meetings are high-stakes conversations that shape the entire hiring process. If the form feels like a burden, users rush and give vague answers.
I collaborated closely with the CEO to refine every question, keeping the language approachable while staying true to recruiting terminology and business goals.
Principle: “Write like a smart colleague, not a survey bot.”
Refinements included inviting reflection instead of a checkbox mentality, removing ambiguous pronouns for concrete references, surfacing visible options instead of hidden menus, and regrouping content to cut perceived effort — with logos and tags as visual companions to keep the experience warm.
Intake steps: Who are we → Define Position → Ideal Candidate & Team Fit → Our Value Prop → Salary & Benefits → Collaboration Agreements → Interview Plan.
Hiring Kit
A role-specific set of resources generated from the intake meeting, assisting the team through each stage of the interview process.
Tabs span Role Summary, Sourcing, Candidate Experience Kit, Interview Preparation, and Offer & Closing — each surfacing projected hiring investment (process time, interview cost, engineering cost, candidate time investment, candidate sentiment) with benchmarks.
How I grew as a designer
Designing Yogen 0→1 sharpened how I shape ambiguous, data-rich products into trustworthy AI experiences — balancing transparency, speed, and strategy while building and maintaining the design system that keeps it all consistent.