Source-based writing and editorial delivery workspace

For

Writers, editors and content teams producing source-based articles, posts and scripts

Solves

Writing, editing, fact-checking and publishing live in separate rented tools, so sources, drafts and approvals drift apart.

Delivers

Editor-approved drafts linked to their sources

Built in

about 5 weeks of creation time, MVP in 5 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

Reduce editorial revision cycles while keeping every claim traceable to its source.

1. Generate draft text from prompts or supplied context.
2. Adjust tone and style per channel and audience.
3. Produce articles, blogs, emails, posts and scripts from one brief.
4. Check grammar, spelling and house style.
5. Offer live editing suggestions as the writer types.
6. Suggest clarity, engagement and flow improvements.
7. Generate ideas and angles to unblock stalled drafts.
8. Start from customizable templates per content type.
9. Support multi-user drafting, comments and approvals.
10. Export to the required publishing and sharing formats.
11. Connect to writing, communication and content management platforms.
12. Generate outlines and summaries from source material.
13. Summarize and interpret supplied datasets for data-led pieces.
14. Route each task to a suitable AI model automatically.
15. Offer access to multiple AI models in one workspace.
16. Retain project context across sessions.
17. Handle complex research questions with a reasoning step.
18. Read supplied images and charts as source material.
19. Support scheduled live spoken sessions with viewer interaction.
20. Stream to multiple live platforms.
21. Schedule and manage live sessions.
22. Let the operator speak on behalf of the AI presenter.

Everything these tools do, in one app

• AI text generation Generates written content automatically from prompts or context.Found in Everlyn AI, BREEZ, Sharbo and 5 more
• Tone and style customization Lets users adjust the tone and style of the generated text.Found in Everlyn AI, BREEZ, Sharbo and 5 more
• Multiple content formats Supports creating different types of content like articles, blogs, emails, and social media posts.Found in BREEZ, Dubo, Felix AI and 1 more
• Grammar and spelling checks Checks text for grammar and spelling errors.Found in Everlyn AI, Sharbo, Dubo and 2 more
• Real-time editing and suggestions Provides live editing tools and suggestions to refine text as you write.Found in BREEZ, Dubo, GPTSidekick and 2 more
• Content suggestions Offers suggestions to improve clarity, engagement, and flow.Found in Everlyn AI, GPTSidekick
• Idea generation Helps users overcome writer’s block by generating ideas.Found in Everlyn AI
• Customizable templates Provides templates to quickly start various writing projects.Found in Sharbo, Dubo, Felix AI
• Collaboration tools Enables multiple users to work on content together.Found in Sharbo, Dubo, Generavitae
• Export options Allows exporting content in multiple formats for easy sharing.Found in Sharbo, Generavitae
• Integration with platforms Connects with popular writing, communication, or content management platforms.Found in Everlyn AI, BREEZ, Dubo and 4 more
• Outline and summary generation Generates content outlines and summaries.Found in GPTSidekick
• Data analysis Interprets and summarizes complex datasets.Found in Generavitae
• AI model selection Automatically selects the best AI model for each message.Found in Mezzie
• Access to multiple AI models Provides access to over 30 AI models in one platform.Found in Mezzie
• Persistent memory Retains context across chats for continuity.Found in Mezzie
• Advanced reasoning Handles complex queries with an advanced reasoning agent.Found in Mezzie
• Vision capabilities Integrates visual understanding into conversations.Found in Mezzie
• AI livestreaming Enables livestreams with AI streamers that interact with viewers in real time.Found in Mixio
• Multi-platform streaming Supports streaming to multiple platforms like Twitch, YouTube Live, and Facebook Live.Found in Mixio
• Livestream scheduling Allows scheduling and managing livestream sessions.Found in Mixio
• Speak on behalf of AI Lets users speak on behalf of the AI streamer if desired.Found in Mixio

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed source material, briefs, style rules and channel constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed source material

4. 3

   Briefs

5. 4

   Style rules and channel constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Editor-approved drafts linked to their sources

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

What your team sees

Primary screens: Brief and source intake, Editable draft workspace, Review and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, style rules 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 passage. Make the task-specific outcome editor-approved drafts linked to their sources 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, document 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

   5 days

   One buyer segment, one recurring use case; first modules: generate draft text from prompts or supplied context; adjust tone and style per channel and audience. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 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 writers, editors and content teams producing source-based articles, posts and scripts use it to solve "writing, editing, fact-checking and publishing live in separate rented tools, so sources, drafts and approvals drift apart"?
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 drafts per editorial hour and corrections after publication.
4. Measure, then decide. Track accepted drafts per editorial hour and corrections after publication; 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 house style and licensed source set; final fact and meaning checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: generate draft text from prompts or supplied context; adjust tone and style per channel and audience. Support the third module with operator review: check grammar, spelling and house style. 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 editor-approved drafts linked to their sources. Retain the explicit scope boundary: One fixed house style and licensed source set; final fact and meaning checks remain editorial.

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 editorial QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed house style and licensed source set; final fact and meaning 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 content package. Offer a monthly production allowance after repeat demand. Quote complex live or specialist production separately. These are test prices, not market benchmarks. Package the initial sale as one bounded editor-approved drafts linked to their sources. 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 editorial revision cycles while keeping every claim traceable to its source. Demonstrate a concrete editor-approved drafts linked to their sources using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Writers, editors and content teams producing source-based articles, posts and scripts professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample editor-approved drafts linked to their sources from a small authorized input set, with a transparent calculation of accepted drafts per editorial hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five writers, editors and content teams producing source-based articles, posts and scripts and inspect a recent example of writing, editing, fact-checking and publishing living in separate rented 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 drafts per editorial hour and corrections after publication, 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 drafts per editorial hour and corrections after publication. 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 drafts per editorial hour and corrections after publication; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs editor-approved drafts linked to their sources. 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, source constraints and review examples, together with reliable delivery for a narrow writing niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for writers, editors and content teams producing source-based articles, posts and scripts. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Everlyn AI, BREEZ, Sharbo, Dubo, Mezzie, GPTSidekick, Felix AI, Spindle, Generavitae and Mixio, plus freelancers and generic writing tools. Compare this product with the buyer's present method on accepted drafts per editorial hour and corrections after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, model routing, 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 editor-approved drafts linked to their sources. 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 fixed house style and licensed source set; final fact and meaning checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.