From Idea to SaaS: How TestIQ Went From Prototype to Product
TestIQ started as a weekend project. A friend who teaches high school AP courses mentioned they spent 6 hours a week writing practice tests. Six months later, TestIQ is a production SaaS serving educators and corporate trainers.
The Prototype (Week 1)
The first version was a single-page app: paste text, pick a question count, get multiple-choice questions. No accounts, no formatting, no analytics. Just GPT-4 with a structured output prompt.
It worked well enough that the teacher friend used it for two weeks straight and asked for more features.
The MVP (Months 1-2)
The MVP added:
- User accounts with email authentication
- Three question types: multiple choice, true/false, short answer
- PDF export with clean formatting
- Source material upload (PDF, DOCX, plain text)
We launched on a teacher subreddit and got 200 signups in the first week. Conversion to paid was 3% — enough to justify continued development.
Scaling Challenges
Cost management
Each test generation costs API tokens. At scale, this becomes the dominant cost. We implemented:
- Token usage tracking per user
- Tiered generation limits ($3.99/mo: 10 tests, $9.99: 50 tests, $24.99: unlimited)
- Caching for identical source material
Quality consistency
AI-generated questions vary in quality. We added a post-generation validation step that checks for:
- Answer correctness (re-query the model to verify)
- Question clarity and unambiguity
- Appropriate difficulty level for the selected grade/context
Latency
Generating a 30-question test takes 15-20 seconds. Users perceived this as broken. We added streaming progress indicators and parallel question generation to reduce perceived wait time.
Current State
TestIQ now generates tests from text, audio, video, and image sources. The AI homework help feature provides step-by-step explanations for incorrect answers.
Revenue is modest but growing. The insight: build for a specific pain point, price fairly, and iterate based on actual usage data.