Source-based content workspace with editorial delivery

For

Writers, documentation teams and support content owners producing source-based articles, docs and help content

Solves

Drafting, adapting and maintaining written content across roles, languages and platforms is split across several rented tools, so sources, tone and approvals drift.

Delivers

Editor-approved content packages linked to their sources

Built in

about 5 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce editorial rework while keeping every claim tied to an approved source.

1. Generate articles, blogs and marketing copy from approved sources.
2. Summarize long documents into concise source-linked summaries.
3. Search inside documents for specific information.
4. Extract dates, names and figures with source references.
5. Adjust tone and style to match the brief.
6. Check grammar and spelling in real time.
7. Adapt content to reader role, plan or usage context.
8. Answer reader questions from documentation in context.
9. Connect product data and external services for contextual responses.
10. Use cookies and feature flags to tailor content visibility.
11. Trigger actions such as support tickets or setting changes from content interactions.
12. Integrate with popular text editors and publishing platforms.
13. Import and export common file formats and cloud storage.
14. Support multiple languages.
15. Manage and organize documents in a dashboard.
16. Provide an interface usable by beginners and experienced users.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before publication.
19. Export a versioned editor-approved content package with source references and unresolved questions.

Everything these tools do, in one app

• AI content generation Generates written content such as articles, blogs, and marketing copy.Found in Brainfish
• Document summarization Condenses long documents into concise summaries that highlight essential points.Found in DocsHound
• Intelligent search Locates specific information within documents quickly.Found in DocsHound
• Data extraction Automatically extracts key data such as dates, names, and figures from documents.Found in DocsHound
• Tone and style customization Adjusts the tone and style of generated text to match different writing needs.Found in Brainfish
• Grammar and spell checking Checks and corrects grammar and spelling in real time to improve text accuracy.Found in Brainfish
• Adaptive content Adjusts documentation instantly based on the reader’s role, plan, or usage context.Found in Dynamic, adaptive documentation
• Contextual AI assistant Provides personalized answers by understanding documentation in context.Found in Dynamic, adaptive documentation
• Product data integration Connects with product data and external services to deliver real-time, contextual responses.Found in Dynamic, adaptive documentation
• Cookies and feature flags Uses cookies and feature flags to tailor content visibility and relevance.Found in Dynamic, adaptive documentation
• Action triggering Triggers actions such as support ticket creation or product setting changes directly from documentation interactions.Found in Dynamic, adaptive documentation
• Editor and platform integration Integrates with popular text editors and platforms.Found in Brainfish
• File format and cloud integration Integrates with popular file formats and cloud storage services.Found in DocsHound
• Multi-language support Supports multiple languages to cater to a global audience.Found in Brainfish
• Document management dashboard Provides a user-friendly dashboard for managing and organizing multiple documents.Found in DocsHound
• Intuitive interface Offers an easy-to-use interface suitable for beginners and experienced users.Found in Brainfish, DocsHound
• Tiered pricing Offers a tiered pricing model with a free basic plan and paid plans with expanded features.Found in Brainfish, DocsHound

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed source documents, brand tone rules, product data and reader context

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed source documents

4. 3

   Brand tone rules

5. 4

   Product data and reader context

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Editor-approved content packages 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 source set, tone guide and language pair; final fact, tone and publication checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source intake and brief, Editable content workspace, Reader-context preview, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, tone rules, product data 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 content packages linked to their sources visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, reader-context settings, 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 such as ticket creation or setting changes.

Integrations and data access

Author-owned source documents, permitted product data and approved brand assets. Cloud storage, text editor and publishing platform import/export. 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 articles, blogs and marketing copy from approved sources; summarize long documents into concise source-linked summaries. 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, documentation teams and support content owners producing source-based articles, docs and help content use it to solve "drafting, adapting and maintaining written content across roles, languages and platforms is split across several rented tools, so sources, tone and approvals drift"?
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 content pieces per editorial hour and corrections after publication.
4. Measure, then decide. Track accepted content pieces 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 source set, tone guide and language pair; final fact, tone and publication checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: generate articles, blogs and marketing copy from approved sources; summarize long documents into concise source-linked summaries. Support the remaining modules with operator review: search, extraction, tone adjustment, grammar checking, reader-context adaptation, contextual answers, product data connections, cookies and feature flags, action triggering, editor and platform integration, file and cloud integration, multi-language support, dashboard management and the intuitive interface. 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, languages and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around editor-approved content packages linked to their sources. Retain the explicit scope boundary: One fixed source set, tone guide and language pair; final fact, tone and publication checks remain editorial.

What the build depends on. Source upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity publishing requires specialist editorial QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed source set, tone guide and language pair; final fact, tone and publication 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 multi-language or product-data integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded editor-approved content package linked to its 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 rework while keeping every claim tied to an approved source. Demonstrate a concrete editor-approved content package linked to its sources using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Writers, documentation teams and support content owners 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 content package linked to its sources from a small authorized input set, with a transparent calculation of accepted content pieces per editorial hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five writers, documentation teams and support content owners producing source-based articles, docs and help content and inspect a recent example of drafting, adapting and maintaining written content across roles, languages and platforms being split across several rented tools.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted content pieces 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 content pieces 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 content pieces 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 content packages 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 tone rules, source mappings and review examples, together with reliable delivery for a narrow content niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for writers, documentation teams and support content owners producing source-based articles, docs and help content. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Brainfish, Dynamic, adaptive documentation and DocsHound, plus freelancers and generic writing tools. Compare this product with the buyer's present method on accepted content pieces 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, translation and 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 editor-approved content packages 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 source set, tone guide and language pair; final fact, tone and publication checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.