Source-linked dictation and writing console

For

Writers and knowledge workers who draft text by speaking into the applications they already use

Solves

Spoken drafts are scattered across several rented dictation tools, so text, history and cleanup rules are split between apps and vendors.

Delivers

Reviewed, source-linked written text inserted into the target application

Built in

about 4 weeks of creation time, MVP in 4 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 tool switching and rework while keeping the writer's own words and history under their control.

1. Dictate into any app.
2. Run speech recognition on-device.
3. Transcribe offline after installation.
4. Produce punctuated, formatted output.
5. Transcribe in real time as you speak.
6. Start and stop dictation by keyboard shortcut.
7. Remove filler words and resolve self-corrections.
8. Match tone and vocabulary to the target app or context.
9. Keep a local dictation history of raw and cleaned versions.
10. Keep a floating microphone above other windows.
11. Recognize many languages and regional variants.
12. Support custom spoken commands for actions and formatting.
13. Create reminders, notes and calendar items by voice.
14. Optionally send text to cloud AI to rewrite, summarize, translate or turn into emails and tickets.
15. Let administrators choose speech models and edit system prompts.
16. Work across phones, tablets and desktops.
17. Provide inspectable, modifiable, self-hostable source code.
18. Send web push, mobile push and in-app notifications.
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, source-linked written text inserted into the target application with source references and unresolved questions.

Everything these tools do, in one app

• Dictate into any app Lets you speak text directly into whatever application or text field you are already using.Found in Ito, Supavoice, Epilude and 4 more
• On-device processing Runs speech recognition locally on your machine so audio does not need to be sent to the cloud.Found in Epilude, Voice Anywhere, Harker 2.0 and 3 more
• Offline transcription Works without an internet connection after installation.Found in Harker 2.0, Dictation, OpenWispr
• Polished formatted output Cleans up raw speech into punctuated, formatted text that is ready to use.Found in Ito, Supavoice, Epilude and 2 more
• Real-time transcription Converts speech to text as you speak with minimal delay.Found in Willow Voice, Epilude, Megaphone
• Keyboard shortcut control Starts and stops dictation with a key press or toggle instead of clicking buttons.Found in Epilude, Voice Anywhere, Harker 2.0 and 1 more
• Filler and correction cleanup Removes filler words and resolves self-corrections so the final text reads cleanly.Found in Epilude, Megaphone
• Tone and context matching Adjusts the style and vocabulary of the text to fit the app or context you are writing in.Found in Epilude, Megaphone
• Local dictation history Keeps a record of past dictations, including the original and cleaned versions, so you can review changes.Found in Epilude
• Floating microphone Keeps a microphone control visible above other windows so it is always available while you work.Found in Voice Anywhere
• Multi-language support Recognizes speech in many languages and regional variants.Found in Willow Voice, Voice Anywhere
• Custom voice commands Lets you define your own spoken commands for actions or formatting.Found in Willow Voice
• Task and calendar actions Uses voice to set reminders, create notes, and manage calendar items.Found in Willow Voice
• Cloud text transformations Optionally sends transcribed text to cloud AI to rewrite, summarize, translate, or turn it into emails or tickets.Found in Harker 2.0
• Model and prompt customization Lets you choose different speech models and edit system prompts to tailor recognition and formatting.Found in OpenWispr
• Cross-device support Works across phones, tablets, and desktops rather than a single platform.Found in Willow Voice
• Open source Provides source code that can be inspected, modified, and self-hosted.Found in Ito, Megaphone, OpenWispr
• Multi-channel notifications Sends messages to users through web push, mobile push, and in-app channels.Found in Najva

What goes in, what comes out

The workflow

1. In
   Start with

   Microphone audio, app context, personal vocabulary and formatting rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect microphone audio

4. 3

   App context

5. 4

   Personal vocabulary and formatting rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked written text inserted into the target application

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 supported operating system set and one approved speech model set; final wording and meaning checks remain with the writer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Dictation and target app, Editable transcript review, Admin console. Use a floating microphone control over the active window, a large central transcript canvas, and a right-hand panel for cleanup rules, commands and history. Let users compare raw and cleaned versions side by side. Display draft, changes requested and approved states. Provide an admin view of models, prompts, languages, retention and export logs. Make the task-specific outcome reviewed, source-linked written text inserted into the target application 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

Author-owned manuscripts, authorized interviews and permitted research sources. Cloud asset storage, design-file import/export and publishing 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

   4 days

   One buyer segment, one recurring use case; first modules: dictate into any app; run speech recognition on-device. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 writers and knowledge workers who draft text by speaking into the applications they already use use it to solve "spoken drafts are scattered across several rented dictation tools, so text, history and cleanup rules are split between apps and vendors"?
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 inserted words per dictation hour and corrections after insertion.
4. Measure, then decide. Track accepted inserted words per dictation hour and corrections after insertion; 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 supported operating system set and one approved speech model set; final wording and meaning checks remain with the writer. Implement one approved input format, a bounded representative case set and the first two task modules: dictate into any app; run speech recognition on-device. Support the third module with operator review: produce punctuated, formatted output. 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, source-linked written text inserted into the target application. Retain the explicit scope boundary: One supported operating system set and one approved speech model set; final wording and meaning checks remain with the writer.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One supported operating system set and one approved speech model set; final wording and meaning checks remain with the writer.

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 asset package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked written text inserted into the target application. 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 tool switching and rework while keeping the writer's own words and history under their control. Demonstrate a concrete reviewed, source-linked written text inserted into the target application using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Writers and knowledge workers who draft text by speaking into the applications they already use professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked written text inserted into the target application from a small authorized input set, with a transparent calculation of accepted inserted words per dictation hour and corrections after insertion and no promised savings.

The first 30 days

1. Week 1: interview five writers and knowledge workers who draft text by speaking into the applications they already use and inspect a recent example of spoken drafts scattered across several rented dictation tools.
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 inserted words per dictation hour and corrections after insertion, 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 inserted words per dictation hour and corrections after insertion. 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 inserted words per dictation hour and corrections after insertion; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked written text inserted into the target application. 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 styles, production constraints and review examples, together with reliable delivery for a narrow creative niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for writers and knowledge workers who draft text by speaking into the applications they already use. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Ito, Willow Voice, Najva, Supavoice, Epilude, Voice Anywhere, Harker 2.0, Megaphone, Dictation and OpenWispr. Compare this product with the buyer's present method on accepted inserted words per dictation hour and corrections after insertion. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image 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, source-linked written text inserted into the target application. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. One supported operating system set and one approved speech model set; final wording and meaning checks remain with the writer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.