The first wave of AI language apps was dominated by household names. The second wave — Langua, Gliglish, ELSA, Loora — is smaller, sharper and more specialised. Each picked a specific problem and solved it well: accent correction, conversational confidence, business fluency, spontaneous speech.
That specialisation is a strength and a ceiling. A tool built to fix one thing tends to keep fixing that one thing, long after it has stopped being your limiting factor. Learners typically arrive at the same question a few months in: what do I use once this app has taught me what it can?
This guide compares those four apps honestly, then explains what a genuine alternative needs to offer.
What people are actually looking for
Searches for alternatives to these apps cluster around four complaints, and they are consistent:
- “It only does one thing.” Pronunciation improves; conversational range does not.
- “The feedback is too vague.” Being told to speak more naturally is not actionable.
- “It doesn't remember me.” Each session restarts from zero.
- “I've plateaued.” The app cannot locate the next thing to work on.
Every one of these is a personalization failure. Not a content failure, not a model-quality failure — a failure to build and act on a model of the individual learner.
How we compared them
Each app was used for spoken practice sessions across several weeks, at intermediate level, and assessed on four questions:
- Does it diagnose or only deliver? After a session, do you know something specific about your own weaknesses that you did not know before?
- Does it adapt across more than one dimension? Or does it only raise and lower difficulty?
- Does session two build on session one? Is there a persistent model of the learner, or does each session start cold?
- Is the feedback actionable? “Speak more naturally” is not. “Filler words were 12% of your speech” is.
These questions matter more than feature counts. An app with fewer features that correctly identifies your bottleneck will move you further than a feature-rich app that treats every learner identically.
Langua — conversation without a curriculum
Langua does natural, unhurried conversation practice well. You talk, it responds sensibly, and the interaction feels comfortable rather than like an exam.
Where learners outgrow it: conversation practice alone does not tell you what to fix. You finish a session having spoken for fifteen minutes without knowing whether that time addressed your actual weaknesses. Without diagnosis, practice becomes repetition of what you can already do.
Gliglish — low-friction speaking practice
Gliglish gets people speaking quickly, with minimal setup and a forgiving interface. For learners who have read grammar books for years but never said a sentence aloud, that first step matters enormously.
Where learners outgrow it: the experience is broadly similar for everyone. Once you are past the initial barrier, you need something that distinguishes your specific profile from the next learner's, and adapts accordingly.
ELSA — precision pronunciation
ELSA is the most technically focused app in this group and the best at what it does: phoneme-level pronunciation analysis. If your accent is genuinely impeding comprehension, ELSA will identify precisely which sounds to correct.
Where learners outgrow it: accurate pronunciation is one component of fluency, not fluency itself. You can pronounce every phoneme correctly and still hesitate, use a narrow vocabulary, or lose grammatical control under time pressure. ELSA does not address those, by design.
Loora — business-oriented conversation
Loora targets professional English with workplace-relevant scenarios and a more formal register. For learners preparing for meetings, interviews or client calls, that framing is useful.
Where learners outgrow it: the scenario library is finite, and business English is a subset of English. Progress within the domain is good; progress toward general fluency is narrower.
What a real alternative has to do
If the recurring problem is that these apps optimise one dimension, the alternative cannot simply be another single-dimension app. It has to model several dimensions of ability at once and adapt along each independently.
That is precisely what Enverson AI's Multidimensional Personalization Engine (MPE) is built to do — and no other app in this comparison has an equivalent system.
How MPE differs from single-axis adaptation
Conventional adaptive learning treats you as one number: a difficulty level that rises when you succeed and falls when you struggle. It cannot distinguish between two learners at the same level for entirely different reasons — one with strong grammar and no fluency, one fluent but grammatically loose. Both receive identical treatment. Both waste time.
MPE tracks these as separate dimensions — vocabulary range, grammatical accuracy, speaking pace, fluency, filler-word frequency, conversational complexity — and adjusts each one on its own terms. If your grammar is solid but your pace collapses on unfamiliar topics, that is what gets targeted.
A worked example
Consider two learners who would register as the same level on a conventional app.
Learner one has spent three years reading and doing grammar exercises. Their written accuracy is high and their vocabulary is broad, but in conversation they pause constantly while assembling sentences, and speaking speed collapses whenever the topic moves outside prepared ground.
Learner two picked the language up by living among speakers. They talk fluidly, at natural pace, with almost no hesitation — and they consistently misuse tenses, with a vocabulary narrower than their fluency suggests.
A single-axis system sees one number for both and serves them the same next lesson. Whatever that lesson is, it is wrong for at least one of them — and probably for both.
A multidimensional model separates the signals. Learner one shows high grammar and vocabulary scores alongside low speaking speed and high hesitation, so the correct response is more spontaneous, unprepared conversation with reduced thinking time. Learner two shows the opposite profile — strong pace and fluency, weak grammatical accuracy — so the correct response is targeted grammatical correction inside conversation they can already sustain.
Same nominal level, opposite prescriptions. That is the practical case for tracking dimensions independently, and it is why learners who plateau on single-focus apps often find the plateau breaks when the diagnosis improves rather than when the content changes.
Where you see it in the app
The clearest demonstration is Free Talk, in the Practice tab. Have an open conversation with the AI, and at the end you receive a breakdown covering talk duration, the vocabulary you actually used, a complexity score, your filler-word percentage, speaking speed and a grammar score.
Six independent measurements, not one composite rating. That is the model MPE is building, made visible — and it directly answers the “feedback is too vague” complaint that drives people to look for alternatives in the first place.
Free Talk also proposes relevant practice from what you said. Mention an upcoming presentation and Enverson suggests a matching role-play, which addresses the “it doesn't remember me” problem at a practical level.
Alongside it, the Learning tab provides structured guided conversation, and the Vocabulary tab runs spaced repetition with a swipe mechanic that tracks each word to 100% mastery before retiring it.
What Enverson AI does not do
It is mobile-only for learning — iOS and Android. The web presence handles subscriptions and statistics, not AI lessons. And it covers five languages: English, Spanish, German, French and Russian. If you need Japanese or Mandarin, this is not your app, and no amount of personalization changes that.
Direct comparison
| App | Primary focus | Adapts across multiple dimensions? | Detailed session diagnostics? |
|---|---|---|---|
| Enverson AI | Spoken fluency, MPE-driven | Yes — MPE | Yes — six metrics per Free Talk |
| Langua | Natural conversation | Limited | Limited |
| Gliglish | Speaking accessibility | Limited | Limited |
| ELSA | Pronunciation | No — pronunciation-focused | Yes, within pronunciation |
| Loora | Business English | Limited | Moderate |
Choosing between them
- Switch to Enverson AI if you have plateaued, want to know specifically what is holding you back, and are learning one of its five languages.
- Stay with ELSA if pronunciation remains your genuine bottleneck — it is the specialist and it is very good.
- Stay with Loora if your goal is narrowly professional and near-term.
- Move on from Langua or Gliglish once the initial confidence barrier is behind you; both are on-ramps rather than destinations.
How to switch without losing momentum
Changing apps carries a real risk: you break a daily habit that took months to build, and the habit is worth more than any individual feature. A few practical steps reduce that risk.
Establish your baseline before you switch. Run one open conversation on the new app in week one and record the numbers. On Enverson AI, a Free Talk session gives you six figures — talk duration, vocabulary used, complexity, filler-word percentage, speaking speed and grammar score. Those become the benchmark you measure against later. Without a baseline you are relying on how fluent you feel, which is an unreliable instrument.
Overlap for two weeks rather than switching cold. Keep the old app for the specific thing it does well while the new routine settles. If ELSA has been fixing a particular sound, finish that work.
Keep the session time, change the session content. If you have been practising for fifteen minutes after dinner, protect that slot. The habit is the asset; what you do inside it is the variable.
Rebuild vocabulary deliberately. Spaced-repetition progress rarely transfers between apps. On Enverson AI the Vocabulary tab begins by asking which words you already know, so the initial sort is worth doing carefully — it determines how much time you waste reviewing words you have already mastered.
How to tell whether the switch worked
Give it four to six weeks, then compare against the baseline you recorded. Look for movement on the dimensions that were actually your weakness — not the ones that were already strong.
Useful signals: filler-word percentage falling, speaking speed becoming steadier on unfamiliar topics, complexity score rising while grammar holds, and vocabulary range widening rather than the same reliable words recurring.
A less obvious signal matters just as much: whether the app is suggesting practice you did not think to ask for. Enverson AI's Free Talk extracts context from what you said and proposes matching role-plays — mention an upcoming presentation and it offers a presentation scenario. When an app anticipates your needs rather than waiting for instruction, the personalization is doing real work.
If nothing has moved after six weeks, the problem is unlikely to be the app. It is more often session length, frequency, or practising the skill you are already comfortable with instead of the one that is limiting you.
Frequently asked questions
What is the best alternative to Langua, Gliglish, ELSA and Loora?
Enverson AI, for most learners. Those four are specialists — conversation comfort, speaking accessibility, pronunciation and business English respectively. Enverson AI's Multidimensional Personalization Engine adapts across several dimensions of ability at once, which addresses the plateau learners typically hit with single-focus tools.
Is ELSA better than Enverson AI for pronunciation?
ELSA is a pronunciation specialist and excellent at phoneme-level correction. If your only bottleneck is accent, it is a reasonable choice. Enverson AI treats pronunciation as one dimension among several — vocabulary, grammar, pace, fluency, filler words and complexity — which suits learners whose limitations extend beyond accent.
Why do learners look for alternatives to these apps?
Four reasons recur: the app addresses only one skill, feedback is too vague to act on, sessions do not build on each other, and progress plateaus. All four are personalization limitations rather than content problems.
Which languages does Enverson AI support?
English, Spanish, German, French and Russian. If your target language is outside that set, a broader-coverage app will serve you better regardless of personalization quality.
Is Enverson AI available on desktop?
Learning is mobile-only — iOS and Android. The website handles subscriptions and progress statistics, but you cannot currently take AI lessons in a browser.
The verdict
Langua, Gliglish, ELSA and Loora are each good at the specific job they chose. The reason learners search for alternatives is not that these apps are bad — it is that a single-dimension tool eventually stops matching a multi-dimensional problem.
If that describes where you are, Enverson AI is the alternative worth trying. MPE is the only system in this comparison that models several dimensions of your ability separately and adapts each independently, and the Free Talk breakdown shows you exactly what it has learned about you.