GlobalExam
Adaptive language exam prep that knows your weaknesses and how ready you really are.
Which is more popular?
Website, pricing, platforms and company facts side by side.
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|---|---|---|
| Website | lingaly.com | eatsready.com |
| Pricing | — | |
| Company | Startup from Spain · 1 - 9 employees · 2026 | Startup from Italy · 1 - 9 employees |
| Listed in |
In their own words, as submitted to SaaSHub.
lingaly prepares you for language exams (Cambridge B2 First, C1 Advanced, Aptis General). Unlike exercise banks where you pick a topic and practice, lingaly decides each session for you. It tracks 100 competencies — from grammar points like conditionals or phrasal verbs to deeper skills like...
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As answered by people managing lingaly and EatsReady.
lingaly's answer
Most exam prep tools are exercise banks — you pick a topic, do exercises, repeat. lingaly flips that. The system tracks 100 individual competencies and decides what you should practice next. It finds weaknesses you didn't know you had and gives you an honest measure of how prepared you are, not just how many exercises you've completed.
lingaly's answer
Competitors like GlobalExam give you a library of exercises sorted by topic. You still have to figure out what to study. lingaly removes that guesswork — it adapts every session to your actual gaps, calibrates difficulty to where you learn fastest, and predicts your real exam readiness.
lingaly's answer
Students preparing for Cambridge B2 First (FCE), C1 Advanced (CAE), or British Council Aptis General — typically adults who need the certificate for work, university, or immigration and want to make the most of their study time.
lingaly's answer
I watched students spend months preparing for Cambridge exams without knowing if they were studying the right things. Most showed up to exam day hoping for the best. I built lingaly to replace that uncertainty with data — a system that measures exactly where you stand and tells you what to focus on.
lingaly's answer
Next.js, React, TypeScript, Supabase (PostgreSQL), Tailwind CSS. The adaptive engine uses Bayesian Knowledge Tracing, Item Response Theory, and Monte Carlo simulation. Exercise generation runs through a multi-step LLM pipeline with quality validation.
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