Real-time speech generation and voice control workspace

For

Product and platform teams adding natural-sounding spoken audio to their own applications

Solves

Teams rent several speech tools for latency, cloning, languages and deployment, and cannot combine them into one owned workflow.

Delivers

Reviewed speech configurations and generated audio linked to their integration

Built in

about 6 weeks of creation time, MVP in 7 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

Reduce the number of rented speech subscriptions while keeping one owned pipeline for real-time spoken audio.

1. Convert written text into spoken audio.
2. Generate audio with low latency for immediate responses.
3. Stream text in and audio out as it is produced.
4. Clone a specific voice or accent from approved samples.
5. Offer a range of voices, accents and tones.
6. Adjust emotional tone of the generated speech.
7. Generate speech in multiple languages.
8. Expose an API for integration into other software.
9. Provide SDKs and adapters for easier integration.
10. Support on-premise deployment on customer infrastructure.
11. Handle managed compute so buyers do not manage GPUs.
12. Adjust speed, pitch and emphasis parameters.
13. Play real-time preview before finalizing.
14. Normalize numbers, addresses and formatted text correctly.
15. Return word-level timestamps for each word.
16. Support SSML markup for pronunciation and pacing.
17. Keep the model open source and customizable.
18. Provide a user-friendly interface for managing speech generation.
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 reviewed speech configuration and generated audio with source references and unresolved questions.

Everything these tools do, in one app

• Text-to-speech conversion Turns written text into spoken audio.Found in PlayHT-Turbo, Kyutai TTS, Lightning V3 and 4 more
• Low-latency generation Produces audio very quickly so responses feel immediate.Found in PlayHT-Turbo, Kyutai TTS, Lightning V3 and 2 more
• Streaming input and output Processes text as it arrives and plays audio as it is generated.Found in PlayHT-Turbo, Kyutai TTS, Lightning V3 and 3 more
• Voice cloning Replicates a specific voice or accent from audio samples.Found in PlayHT-Turbo, Lightning V3, Vogent Voicelab and 2 more
• Multiple voice options Offers a range of voices, accents, and tones to choose from.Found in PlayHT-Turbo, Kyutai TTS, Lightning V3 and 1 more
• Emotional tone control Lets you adjust the emotion or tone of the generated speech.Found in PlayHT-Turbo, Kyutai TTS, Simba Voice Agents
• Multilingual support Generates speech in multiple languages.Found in Lightning V3, Orpheus TTS, KugelAudio
• API access Provides an API for integrating speech generation into other software.Found in PlayHT-Turbo, Lightning V3, Vogent Voicelab and 3 more
• SDKs and adapters Offers software development kits and adapters for easier integration.Found in KugelAudio, Simba Voice Agents
• On-premise deployment Allows the software to be hosted on your own infrastructure.Found in KugelAudio
• Managed compute infrastructure Handles the underlying compute so you don't need to manage GPUs.Found in Vogent Voicelab
• Customizable speech parameters Lets you adjust speed, pitch, and emphasis of the speech.Found in Orpheus TTS
• Real-time preview Plays audio instantly so you can hear changes before finalizing.Found in Orpheus TTS
• Grammar-aware normalization Correctly reads numbers, addresses, and other formatted text.Found in KugelAudio
• Word-level timestamps Provides precise timing for each word in the audio.Found in KugelAudio
• SSML support Allows fine-tuning of pronunciation and pacing using SSML markup.Found in Simba Voice Agents
• Open-source availability The model is open source and can be customized by the community.Found in Kyutai TTS
• User-friendly interface Provides an easy-to-use interface for managing speech generation.Found in Orpheus TTS, Vogent Voicelab

What goes in, what comes out

The workflow

1. In
   Start with

   Approved scripts, voice samples, language settings and latency targets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved scripts

4. 3

   Voice samples

5. 4

   Language settings and latency targets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed speech configurations and generated audio linked to their integration

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 arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed language set and approved voice library; final voice consent and content checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Voice and script setup, Editable generation preview, Integration and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for voices, parameters and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant audio segment. Make the task-specific outcome reviewed speech configurations and generated audio linked to their integration visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision 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

Customer-owned scripts, authorized voice samples and permitted research sources. Cloud audio storage, application import/export and delivery destinations. Start with file exchange and validate destination specifications before promising direct publishing. 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

   7 days

   One buyer segment, one recurring use case; first modules: convert written text into spoken audio; generate audio with low latency for immediate responses. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 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 product and platform teams adding natural-sounding spoken audio to their own applications use it to solve "teams rent several speech tools for latency, cloning, languages and deployment, and cannot combine them into one owned workflow"?
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: Accepted audio minutes per delivery hour and corrections after integration.
4. Measure, then decide. Track accepted audio minutes per delivery hour and corrections after integration; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed language set and approved voice library; final voice consent and content checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: convert written text into spoken audio; generate audio with low latency for immediate responses. Support the third module with operator review: stream text in and audio out as it is produced. 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 inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed speech configurations and generated audio linked to their integration. Retain the explicit scope boundary: One fixed language set and approved voice library; final voice consent and content checks remain editorial.

What the build depends on. Audio upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist audio QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed language set and approved voice library; final voice consent and content checks remain editorial.

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 audio package. Offer a monthly production allowance after repeat demand. Quote complex multi-language or on-premise work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed speech configuration and generated audio linked to their integration. Recurring fees must specify 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

Reduce the number of rented speech subscriptions while keeping one owned pipeline for real-time spoken audio. Demonstrate a concrete reviewed speech configuration and generated audio linked to their integration using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams adding natural-sounding spoken audio to their own applications professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed speech configuration and generated audio linked to their integration from a small authorized input set, with a transparent calculation of accepted audio minutes per delivery hour and corrections after integration and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams adding natural-sounding spoken audio to their own applications and inspect a recent example of teams renting several speech tools for latency, cloning, languages and deployment, and cannot combine them into one owned workflow.
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 accepted audio minutes per delivery hour and corrections after integration, 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: Accepted audio minutes per delivery hour and corrections after integration. 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

Accepted audio minutes per delivery hour and corrections after integration; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed speech configurations and generated audio linked to their integration. 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 voices, latency profiles and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and platform teams adding natural-sounding spoken audio to their own applications. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

PlayHT-Turbo, Kyutai TTS, Lightning V3, Vogent Voicelab, Orpheus TTS, KugelAudio, Sesame and Simba Voice Agents. Compare this product with the buyer's present method on accepted audio minutes per delivery hour and corrections after integration. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, audio processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed speech configurations and generated audio linked to their integration. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve speaker consent, source attribution, pronunciation accuracy and usage permissions. Speakers approve voice cloning and publication scope. One fixed language set and approved voice library; final voice consent and content checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.