Spoken language practice conversation studio

For

Language schools, university language centres and corporate learning teams running speaking practice for groups of learners

Solves

Learners get too few spoken repetitions and tutors cannot listen to every conversation or give consistent corrections.

Delivers

Tutor-approved conversation practice with linked corrections

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Increase supervised spoken repetitions per learner while keeping tutor review of corrections.

1. Run spoken AI conversations in the chosen target language.
2. Give immediate spoken and written corrections during practice.
3. Flag pronunciation errors with the learner's own audio.
4. Flag grammar errors with corrected alternatives.
5. Support several target languages per cohort.
6. Let tutors set or learners choose conversation topics.
7. Adjust difficulty from recent learner performance.
8. Build a personalised path from goals and progress.
9. Track pronunciation, grammar and fluency over time.
10. Provide exam-style tasks for IELTS, TOEIC and TOEFL.
11. Run on phone and tablet browsers.
12. Connect learners with vetted human partners for live practice.
13. Adapt the AI accent to regional varieties on request.
14. Add streaks, points and level goals for motivation.
15. Support written exercises linked to the same topic.
16. Generate practice prompts and topic variants.
17. Let learner pairs rehearse a shared scenario.
18. Suggest clearer phrasing for learner-written answers.
19. Offer reusable lesson templates per level.
20. Export results to the school's learning platform.

Everything these tools do, in one app

• AI conversation practice Lets users hold spoken conversations with an AI to practice using the language.Found in AITalk, SpeakingAI, Vocao.ai and 4 more
• Instant feedback Gives immediate corrections or comments on the user's speaking during or right after practice.Found in SpeakingAI, Vocao.ai, Univerbal (formerly Quazel) and 2 more
• Pronunciation correction Points out and helps fix pronunciation mistakes.Found in SpeakingAI, Vocao.ai, Mr. Takahashi: Speak Japanese
• Grammar correction Identifies and helps correct grammar errors in the user's speech.Found in AITalk, SpeakingAI
• Multiple language support Offers practice in more than one language.Found in AITalk, SpeakingAI, Univerbal (formerly Quazel) and 1 more
• Custom conversation topics Lets users choose or create subjects for their practice conversations.Found in Univerbal (formerly Quazel)
• Adaptive difficulty Adjusts the difficulty of lessons or conversations based on how the user is doing.Found in SpeakingAI, Talkface
• Personalized learning path Creates a customized plan or curriculum that follows the user's progress and goals.Found in Univerbal (formerly Quazel), Talkface
• Progress tracking Shows how the user's skills are improving over time.Found in Mr. Takahashi: Speak Japanese
• Exam preparation Provides targeted practice for language proficiency exams like IELTS, TOEIC, or TOEFL.Found in AITalk, Talkface
• Mobile app Lets users practice on a smartphone or tablet.Found in AITalk, SpeakingAI, Vocao.ai and 1 more
• Human conversation partners Connects users with real people for live speaking practice.Found in Aoi Speak
• Accent adaptation Adjusts the AI's accent to different regional varieties on request.Found in Vocao.ai
• Gamification elements Uses game-like features to make practice more engaging and motivating.Found in Talkface
• Writing assistance Helps users improve their written language skills.Found in AITalk
• Creative naming Provides help with generating creative names or terms.Found in AITalk
• Content generation Automatically produces written content such as articles, blogs, or marketing copy.Found in Llanai, Luqo AI
• Collaboration tools Allows multiple users to work together on content projects.Found in Llanai, Luqo AI
• Content optimization Offers suggestions to improve content for SEO and readability.Found in Llanai
• Customizable templates Provides pre-made templates that can be adapted for different writing tasks.Found in Llanai, Luqo AI
• CMS integration Connects with popular content management systems.Found in Llanai, Luqo AI

What goes in, what comes out

The workflow

1. In
   Start with

   Learner speech, tutor-set topics and curriculum goals

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect learner speech

4. 3

   Tutor-set topics and curriculum goals

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Tutor-approved conversation practice with linked corrections

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for scoring, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One target language per pilot cohort and tutor-approved topic bank; final correction and level decisions remain tutor-led. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Tutor setup and topic bank, Learner conversation room, Tutor review and progress. Use a class roster with per-learner status, a large central conversation panel with live transcript, and a right-hand panel for corrections, difficulty and goals. Let tutors compare attempts side by side. Display draft, changes requested and approved states. Provide a learner practice link with corrections anchored to the relevant utterance. Make the task-specific outcome tutor-approved conversation practice with linked corrections visible beside its evidence, review state and value baseline.

Accounts and administration

Cohort ownership, learner records, topic versions, tutor comments, approval states, usage allowances, session limits, recording history and a consent record for learner audio. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

School learning platforms, tutor-owned topic banks and permitted curriculum sources. Cloud audio storage, single sign-on and export to the school's LMS. Start with file exchange and validate destination specifications before promising direct gradebook sync. 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 spoken AI conversations in the chosen target language; give immediate spoken and written corrections during practice. 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

   2 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, university language centres and corporate learning teams running speaking practice for groups of learners use it to solve "learners get too few spoken repetitions and tutors cannot listen to every conversation or give consistent corrections"?
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: Supervised spoken minutes per learner and accepted corrections per tutor hour.
4. Measure, then decide. Track supervised spoken minutes per learner and accepted corrections per tutor hour; 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 per pilot cohort and tutor-approved topic bank; final correction and level decisions remain tutor-led. Implement one approved input format, a bounded representative case set and the first two task modules: run spoken AI conversations in the chosen target language; give immediate spoken and written corrections during practice. Support the third module with operator review: flag pronunciation errors with the learner's own audio. 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 cohort volume only after new evaluation cases pass. Build reusable school configurations and recurring value reports around tutor-approved conversation practice with linked corrections. Retain the explicit scope boundary: One target language per pilot cohort and tutor-approved topic bank; final correction and level decisions remain tutor-led.

What the build depends on. Audio upload and playback, asynchronous speech jobs, editable transcript history, tutor access and tested export formats. High-fidelity pronunciation scoring requires specialist language QA. Obtain representative authorized cases, baseline measurements, qualified tutors and a buyer-side decision owner. Specific limitation: One target language per pilot cohort and tutor-approved topic bank; final correction and level decisions remain tutor-led.

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 cohort package. Offer a monthly per-seat allowance after repeat demand. Quote specialist exam preparation or human partner sessions separately. These are test prices, not market benchmarks. Package the initial sale as one bounded tutor-approved conversation practice with linked corrections. Recurring fees must specify seat count, 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 supervised spoken repetitions per learner while keeping tutor review of corrections. Demonstrate a concrete tutor-approved conversation practice with linked corrections using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample tutor-approved conversation practice with linked corrections from a small authorized input set, with a transparent calculation of supervised spoken minutes per learner and accepted corrections per tutor hour and no promised savings.

The first 30 days

1. Week 1: interview five language schools, university language centres and corporate learning teams running speaking practice for groups of learners and inspect a recent example of learners get too few spoken repetitions and tutors cannot listen to every conversation or give consistent corrections.
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 supervised spoken minutes per learner and accepted corrections per tutor hour, reviewer 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: Supervised spoken minutes per learner and accepted corrections per tutor hour. 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

Supervised spoken minutes per learner and accepted corrections per tutor hour; accepted-output rate; material error rate; tutor correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs tutor-approved conversation practice with linked corrections. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased seat count or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved topics, level rubrics and reviewed correction examples, together with reliable delivery for a narrow education niche. Build a permissioned library of representative learner cases, tutor corrections and verified curriculum constraints for language schools, university language centres and corporate learning teams running speaking practice for groups of learners. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AITalk, Llanai, SpeakingAI, Vocao.ai, Univerbal (formerly Quazel), Aoi Speak, Talkface, Luqo AI, Talk to Dai (Day) and Mr. Takahashi: Speak Japanese, plus tutors and classroom role-play. Compare this product with the buyer's present method on supervised spoken minutes per learner and accepted corrections per tutor hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Speech processing, model calls, storage, tutor review hours, learner support and licensed content. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of tutor-approved conversation practice with linked corrections. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve learner consent, audio retention limits, source attribution and usage permissions. Tutors approve substantive corrections and level decisions. One target language per pilot cohort and tutor-approved topic bank; final correction and level decisions remain tutor-led. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.