Multilingual speaking and writing practice studio

For

Language schools, corporate learning teams and independent tutors running speaking and writing practice

Solves

Learners get limited speaking time, scattered vocabulary and writing feedback, and tutors cannot review every attempt.

Delivers

Tutor-reviewed practice records linked to learner progress

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Increase reviewed practice attempts per learner while keeping tutor oversight.

1. Run interactive speaking practice with pronunciation feedback.
2. Set up conversation and debate scenarios.
3. Simulate real-life roleplay situations.
4. Give personalized teaching feedback on grammar, pronunciation and vocabulary.
5. Correct grammar and spelling in real time.
6. Provide instant translations in context.
7. Build vocabulary with flashcards, saved words and exercises.
8. Assist writing with instant feedback on emails, articles and essays.
9. Generate personalized stories at the learner's level.
10. Analyze spoken or written retellings for grammar, vocabulary, spelling and completeness.
11. Run interactive quizzes and gap-fill exercises.
12. Track progress with clear insights.
13. Adjust speaking tempo to the learner's pace.
14. Recognize native-language questions and respond in the target language.
15. Suggest context-aware text that improves clarity and coherence.
16. Adjust writing tone settings.
17. Support several learning languages.
18. Work mobile-first without downloads.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned tutor-reviewed practice record linked to learner progress with source references and unresolved questions.

Everything these tools do, in one app

• Interactive speaking practice Lets learners speak the language to build pronunciation and conversational fluency.Found in Gliglish
• Conversation and debate practice Provides real-time conversation scenarios and debates to practice the language in varied contexts.Found in Soofy
• Real-life roleplay Simulates everyday conversations so learners can rehearse practical situations.Found in Gliglish
• Personalized AI teaching Acts like a teacher and gives feedback on grammar, pronunciation, and vocabulary.Found in Gliglish
• Grammar and spelling feedback Corrects grammar and spelling in real time to improve accuracy.Found in Gliglish, Univerbal
• Instant translations Translates words or sentences on the spot to help learners understand meaning and context.Found in Soofy, Gliglish, Toritark
• Vocabulary building tools Offers flashcards, saved words, and exercises to learn and retain new vocabulary.Found in Soofy, Toritark
• AI writing assistance Helps write emails, articles, and essays with instant feedback to improve written communication.Found in Soofy, Univerbal
• Personalized story generation Creates unique stories tailored to the learner's level and topic preferences.Found in Toritark
• Retelling with feedback Analyzes spoken or written retellings and gives detailed feedback on grammar, vocabulary, spelling, and completeness.Found in Toritark
• Interactive quizzes and exercises Reinforces vocabulary and comprehension through quizzes and gap-fill exercises.Found in Toritark
• Progress tracking Shows instant, clear insights into learning progress to guide improvement.Found in Toritark
• Adjustable learning speed Lets learners customize the speaking tempo to match their pace.Found in Gliglish
• Multilingual speech recognition Allows asking questions in the native language and receiving responses in the target language.Found in Gliglish
• Context-aware text suggestions Suggests text that improves clarity and coherence based on context.Found in Univerbal
• Customizable tone settings Adjusts the writing style to match different tones.Found in Univerbal
• Multi-language support Enables learning and content creation in several languages.Found in Gliglish, Univerbal
• Mobile-first design Provides easy access on mobile devices without downloads.Found in Toritark

What goes in, what comes out

The workflow

1. In
   Start with

   Learner speech, writing, vocabulary lists and tutor constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect learner speech

4. 3

   Writing

5. 4

   Vocabulary lists and tutor constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Tutor-reviewed practice records linked to learner progress

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for scoring arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One target language pair and one tutor review policy; final assessment and certification remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Learner practice room, Tutor review queue, Progress and reports. Use a course gallery, a large central practice canvas with microphone and text input, and a right-hand panel for vocabulary, corrections and comments. Let learners compare attempts side by side. Display draft, changes requested and approved states. Provide a tutor preview link with comments anchored to the relevant attempt. Make the task-specific outcome tutor-reviewed practice records linked to learner progress visible beside its evidence, review state and value baseline.

Accounts and administration

Cohort ownership, learner records, tutor comments, approval states, usage allowances, practice limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

School-owned learner records, authorized curriculum material and permitted practice sources. Cloud storage, learning management system import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct integration. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: run interactive speaking practice with pronunciation feedback; set up conversation and debate scenarios. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will language schools, corporate learning teams and independent tutors running speaking and writing practice use it to solve "learners get limited speaking time, scattered vocabulary and writing feedback, and tutors cannot review every attempt"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Reviewed practice attempts per tutor hour and learner retention of corrected items.
4. Measure, then decide. Track reviewed practice attempts per tutor hour and learner retention of corrected items; accepted-output rate; material error rate; tutor correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One target language pair and one tutor review policy; final assessment and certification remain human. Implement one approved input format, a bounded representative learner set and the first two task modules: run interactive speaking practice with pronunciation feedback; set up conversation and debate scenarios. Support the third module with operator review: simulate real-life roleplay situations. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported languages and learner volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around tutor-reviewed practice records linked to learner progress. Retain the explicit scope boundary: One target language pair and one tutor review policy; final assessment and certification remain human.

What the build depends on. Learner upload and preview, asynchronous practice jobs, editable version history, tutor access and tested export formats. High-fidelity assessment requires specialist language QA. Obtain representative authorized cases, baseline measurements, qualified tutors and a buyer-side decision owner. Specific limitation: One target language pair and one tutor review policy; final assessment and certification remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined learner cohort. Offer a monthly practice allowance after repeat demand. Quote complex multi-language or certification work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded tutor-reviewed practice record linked to learner progress. Recurring fees must specify learner volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Increase reviewed practice attempts per learner while keeping tutor oversight. Demonstrate a concrete tutor-reviewed practice record linked to learner progress using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Language schools, corporate learning teams and independent tutors professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample tutor-reviewed practice record linked to learner progress from a small authorized input set, with a transparent calculation of reviewed practice attempts per tutor hour and learner retention of corrected items and no promised savings.

The first 30 days

1. Week 1: interview five language schools, corporate learning teams and independent tutors running speaking and writing practice and inspect a recent example of limited speaking time, scattered vocabulary and writing feedback, and tutors cannot review every attempt.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure reviewed practice attempts per tutor hour and learner retention of corrected items, tutor effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Reviewed practice attempts per tutor hour and learner retention of corrected items. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Reviewed practice attempts per tutor hour and learner retention of corrected items; accepted-output rate; material error rate; tutor correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs tutor-reviewed practice records linked to learner progress. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved scenarios, correction examples and tutor review policies, together with reliable delivery for a narrow language-learning niche. Build a permissioned library of representative learner cases, tutor corrections and verified operating constraints for language schools, corporate learning teams and independent tutors running speaking and writing practice. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Soofy, Gliglish, Toritark and Univerbal, plus tutors, classroom practice and generic study apps. Compare this product with the buyer's present method on reviewed practice attempts per tutor hour and learner retention of corrected items. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Speech and text processing, storage, tutor hours, learner revision rounds and licensed content. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of tutor-reviewed practice records linked to learner progress. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve learner voice, source attribution, quotation accuracy and usage permissions. Tutors approve substantive corrections and assessment scope. One target language pair and one tutor review policy; final assessment and certification remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.