Designing an AI Interview Preparation Platform From Credit Economics to Launch
Designed the complete product, business, and technical foundation for an AI-native mock-interview platform: a pay-per-use credit economy verified above 70% gross margin, dual-model AI failover, and an institutional tier for colleges and placement cells.
About the Project
The First Interview (thefirstinterview.ai) is an AI-native EdTech product helping job seekers, students, and career switchers prepare for real interviews through personalised, AI-generated mock sessions. Rorix partnered on the end-to-end build: business model design, technical architecture, pricing strategy, and the go-to-market website. Sessions are generated from a candidate’s actual job description, resume, or target technology, evaluated against AI-built benchmarks, and monetized through a pay-per-use credit economy instead of subscriptions — with every tier margin-verified against real AI token costs before launch.
- Pay-per-use credit economy — session cost scales with actual AI compute, credits never expire
- Two-call AI pipeline: question generation with internal ideal answers, then benchmark-based evaluation
- Gemini Pro 1.5 primary with Claude Sonnet 4 automatic fallback behind a circuit breaker
- Institutional (TPO) tier: shared credit pools, per-student caps, admin dashboard, GST invoicing
Challenges We Solved
A Market Split Between Two Broken Models
Existing tools are either static question banks that ignore the role and the candidate, or subscription courses charging monthly for naturally bursty usage. The product had to personalise to a real job description or resume and monetize per use.
Evaluation, Not Just Question Delivery
The value is feedback: every session had to score the candidate’s submitted answers against a benchmark and produce a full performance report, at a cost that stays predictable session to session.
Two Buyer Types From Day One
Individual job seekers buy focused bursts before an interview; colleges and placement cells run mock-interview drives for hundreds of students at once. Both needed first-class flows, not a retrofit.
Volatile AI Costs and Provider Risk
Session cost varies with generation type, difficulty, question count, and behavioral rounds — and a single-provider outage or price change could take the product down or destroy margins.
How We Delivered
Credit Economy Design
Built a granular pay-per-use system where session cost derives from generation type (technology / JD / resume / resume+JD), difficulty, question count, and optional behavioral rounds — pricing that scales precisely with AI compute cost.
Dual-Model AI Architecture
Designed the two-call pipeline (generate questions + internal ideal answers, then evaluate submissions into a report) on Gemini Pro 1.5, with Claude Sonnet 4 as automatic fallback and a circuit breaker protecting against outages.
Institutional Product Tier
Designed the bulk tier for colleges and training institutes: shared credit pools, per-student usage caps, an admin dashboard, and GST-compliant invoicing, priced to make bulk drives cheaper per student than individual signups.
Margin-Engineered Pricing
Modeled every credit pack and tier against real AI token costs across 60+ session-type combinations, verifying consistent gross margins before a single price was published.
Growth Mechanics
Designed a first-purchase bonus window, time-boxed signup credits, referral rewards, and a streak-and-badge system to drive conversion and retention without subscription lock-in or dark patterns.
Platform & Marketing-Site Architecture
Scoped the Next.js marketing and lead-generation site with a token-based themable design system and programmatic SEO pages per technology and role, over NestJS services and PostgreSQL — with schema, security model, refund policy, and API architecture fully documented.
Results That Speak for Themselves
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