Source-linked content, analytics and live broadcast console

For

Marketing and content teams that write, analyze data and run live broadcasts

Solves

Content, analytics and live broadcast work sit in separate subscriptions, so drafts, data and stream chat are handled in tools that do not share sources or approvals.

Delivers

Reviewed drafts, dashboards and moderated live responses

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 while keeping every draft, number and live reply linked to its source.

1. Generate articles, emails and social posts from approved briefs.
2. Adjust tone and style to match brand preferences.
3. Show live grammar and spelling suggestions while writing.
4. Summarize long content and extract keywords.
5. Fetch current web results and extract page text into prompts.
6. Offer a one-click library of reusable prompts.
7. Save personalized prompt templates.
8. Route prompts to multiple AI engines.
9. Automate data processing and analysis.
10. Build customizable monitoring dashboards.
11. Connect third-party software into existing workflows.
12. Run predictive analytics for planning.
13. Provide a drag-and-drop interface.
14. Suggest real-time content during live streams.
15. Moderate chat and manage interaction automatically.
16. Integrate with streaming platforms.
17. Send customizable alerts for broadcasts.
18. Track viewer engagement in an analytics dashboard.

Everything these tools do, in one app

• AI text generation Generates written content such as articles, emails, and social media posts from user input.Found in Mishka
• Tone and style customization Allows users to adjust the tone and style of generated text to match their preferences.Found in Mishka
• Real-time grammar suggestions Provides live grammar and spelling suggestions to improve text accuracy as you write.Found in Mishka
• Content summarization Summarizes long content and extracts keywords to aid research and SEO.Found in Mishka
• Real-time web search Fetches current web search results and extracts webpage text to enrich AI prompts with up-to-date information.Found in Web ChatGPT
• Prompt library Offers a one-click library of hundreds of high-quality prompts for various use cases.Found in Web ChatGPT
• Prompt template management Enables users to create and save personalized prompt templates for consistent outcomes.Found in Web ChatGPT
• Multi-AI engine integration Integrates with multiple AI engines such as ChatGPT, Claude, Bard, and Bing AI.Found in Web ChatGPT
• Automated data processing Automates data processing and analysis to reduce manual effort.Found in OSO AI
• Customizable dashboards Provides customizable dashboards for real-time monitoring and reporting.Found in OSO AI
• Third-party software integration Connects with popular third-party software to fit into existing workflows.Found in OSO AI
• Predictive analytics Uses AI-driven predictive analytics to support strategic planning.Found in OSO AI
• Drag-and-drop interface Offers a user-friendly interface with drag-and-drop functionality for easy operation.Found in OSO AI
• Real-time content suggestions Provides real-time suggestions to keep live streams engaging.Found in CoPilot.Live
• Automated chat moderation Automates chat moderation and interaction management during live broadcasts.Found in CoPilot.Live
• Streaming platform integration Integrates with popular streaming platforms for seamless use.Found in CoPilot.Live
• Customizable alerts Allows customizable alerts and notifications tailored to the broadcast.Found in CoPilot.Live
• Analytics dashboard Tracks viewer engagement and performance through an analytics dashboard.Found in CoPilot.Live

What goes in, what comes out

The workflow

1. In
   Start with

   Approved briefs, brand rules, data files and stream chat

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved briefs

4. 3

   Brand rules

5. 4

   Data files and stream chat

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed drafts, dashboards and moderated live responses

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 approved brand voice and one connected data set; final publication and moderation decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Brief and sources, Editable production preview, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, constraints 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 asset. Make the task-specific outcome reviewed drafts, dashboards and moderated live responses 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

Client-owned briefs, authorized data files 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: generate articles, emails and social posts from approved briefs; adjust tone and style to match brand preferences. 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 marketing and content teams that write, analyze data and run live broadcasts use it to solve "content, analytics and live broadcast work sit in separate subscriptions, so drafts, data and stream chat are handled in tools that do not share sources or approvals"?
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: Approved outputs per team hour and corrections after publication.
4. Measure, then decide. Track approved outputs per team 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 approved brand voice and one connected data set; final publication and moderation decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate articles, emails and social posts from approved briefs; adjust tone and style to match brand preferences. Support the third module with operator review: show live grammar and spelling suggestions while writing. 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 drafts, dashboards and moderated live responses. Retain the explicit scope boundary: One approved brand voice and one connected data set; final publication and moderation decisions remain human.

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 approved brand voice and one connected data set; final publication and moderation decisions remain human.

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 drafts, dashboards and moderated live responses. 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 while keeping every draft, number and live reply linked to its source. Demonstrate a concrete reviewed drafts, dashboards and moderated live responses using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and content teams that write, analyze data and run live broadcasts professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed drafts, dashboards and moderated live responses from a small authorized input set, with a transparent calculation of approved outputs per team hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five marketing and content teams that write, analyze data and run live broadcasts and inspect a recent example of content, analytics and live broadcast work sit in separate subscriptions, so drafts, data and stream chat are handled in tools that do not share sources or approvals.
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 approved outputs per team 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: Approved outputs per team 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

Approved outputs per team 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 reviewed drafts, dashboards and moderated live responses. 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 marketing and content teams that write, analyze data and run live broadcasts. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Mishka, Web ChatGPT, OSO AI and CoPilot.Live, plus freelancers and generic generation tools. Compare this product with the buyer's present method on approved outputs per team 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, 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 drafts, dashboards and moderated live responses. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and publication scope. One approved brand voice and one connected data set; final publication and moderation decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.