Source-linked browser assistant and admin console

For

Teams and individuals who research, read and act on web pages, documents and videos in the browser

Solves

Assistants that read the active page, documents and videos are scattered across separate subscriptions, and their outputs are hard to trace, approve or keep private.

Delivers

Reviewed, source-linked answers and actions

Built in

about 4 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 tool switching and manual copying while keeping source-linked, human-approved answers.

1. Open the assistant in a side panel beside the active page.
2. Summarize web pages into short source-linked summaries.
3. Analyze documents and PDFs for insights.
4. Search pages and PDFs with natural language queries.
5. Read active-window text, buttons and screenshots as context.
6. Perform in-app actions such as charting on-screen data.
7. Compare responses across multiple AI models.
8. Require approval before reading sensitive information or private code.
9. Store collected material in a personal knowledge base.
10. Tailor assistant behavior with custom characters.
11. Build custom chatbots from approved prompts.
12. Capture and organize research notes.
13. Summarize YouTube videos into key points.
14. Adjust summary tone and detail with custom prompts.
15. Send summaries to Kindle for offline reading.
16. Translate summaries into many languages.
17. Aggregate and contrast multiple open tabs.
18. Draft and send messages from the panel.
19. Create calendar events and read calendar data.
20. Perform basic image edits or create images.
21. Accept open tabs, images and PDFs as context.
22. Highlight text or an area and ask about it in an overlay.
23. Analyze diagrams and charts from image pixels.
24. Save scans to an encrypted vault.
25. Permanently delete scan or chat history.
26. Combine multiple models to compare and validate answers.
27. Ask questions about screenshots.
28. Automate scene selection and transitions for video.
29. Apply customizable video templates.
30. Export videos to social media platforms.

Everything these tools do, in one app

• Side panel access Keeps the AI assistant in a panel beside the content you are viewing so you do not switch apps or tabs.Found in Sider Omni Sidebar, Google Gemini in Chrome, Sider 4.0 and 1 more
• Summarize web pages Condenses lengthy web articles into short summaries.Found in Mochii AI, Locus Extension, AI Summary Helper - Summarize Articles and 2 more
• Document and PDF analysis Reads and extracts insights from documents and PDF files.Found in Mochii AI, Locus Extension, Gist AI and 2 more
• Natural language search Lets you search web pages and PDFs using conversational queries instead of exact keywords.Found in Locus Extension
• Screen-aware context Reads text, buttons, and screenshots from the active window to use as context.Found in Sider Omni Sidebar
• In-app actions Performs tasks directly inside the app you are using, such as creating a chart from on-screen data.Found in Sider Omni Sidebar
• Multiple AI models Lets you choose from or compare responses across several AI models.Found in Sider Omni Sidebar, Mochii AI, Sider 4.0
• Approval before reading Requires your confirmation before the assistant reads sensitive information or private code.Found in Sider Omni Sidebar
• Personal knowledge base Stores and organizes information you collect for future reference.Found in Mochii AI
• Custom AI characters Tailors the assistant's behavior for specific tasks like research or creative writing.Found in Mochii AI
• Chatbot builder Lets you build custom chatbots.Found in Mochii AI
• Note-taking Captures and organizes notes during research.Found in Locus Extension
• Video summarization Condenses YouTube videos into key points.Found in Gist AI, Google Gemini in Chrome
• Custom summary prompts Adjusts the tone and detail level of generated summaries.Found in AI Summary Helper - Summarize Articles
• Send to Kindle Sends summaries to a Kindle for offline reading.Found in AI Summary Helper - Summarize Articles
• Summary translation Translates summaries into many languages.Found in Gist AI
• Cross-tab comparison Aggregates and contrasts information from multiple open pages.Found in Google Gemini in Chrome
• Draft and send messages Composes and sends emails or messages without leaving the current tab.Found in Google Gemini in Chrome
• Schedule events Creates calendar events and interacts with calendar data from the panel.Found in Google Gemini in Chrome
• Image transformation Performs basic image edits or creates images from within the panel.Found in Google Gemini in Chrome, AI Mode in Chrome
• Multiple inputs Accepts open tabs, images, and PDFs as context for questions.Found in AI Mode in Chrome
• Highlight and ask Lets you highlight text or an area and ask a question about it in an overlay.Found in Kogvio
• Vision model for diagrams Analyzes diagrams and charts directly from image pixels.Found in Kogvio
• Encrypted scan vault Saves scans to a private, encrypted vault for later review.Found in Kogvio
• Delete history Permanently deletes any scan or chat history from the dashboard.Found in Kogvio
• Group AI chat Combines multiple AI models to compare and validate answers.Found in Sider 4.0
• Chat with screenshots Allows asking questions about screenshots.Found in Sider 4.0
• AI video editing Automates scene selection and transitions for video creation.Found in Walles.ai
• Video templates Provides customizable templates for different video styles.Found in Walles.ai
• Social media export Shares videos directly to popular social media platforms.Found in Walles.ai

What goes in, what comes out

The workflow

1. In
   Start with

   Open tabs, PDFs, screenshots, videos and permitted account data

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect open tabs

4. 3

   PDFs

5. 4

   Screenshots

6. 5

   Videos and permitted account data

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed, source-linked answers and actions

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 browser and one approved model set; final factual, legal and code checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant side panel, Source library and admin console. Use a thumbnail gallery for sessions and documents, a large central reading and answer canvas, and a right-hand panel for sources, model choice and approvals. Let users compare model answers side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant source. Make the task-specific outcome reviewed, source-linked answers and actions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source 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

   5 days

   One buyer segment, one recurring use case; first modules: open the assistant in a side panel beside the active page; summarize web pages into short 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

   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 teams and individuals who research, read and act on web pages, documents and videos in the browser use it to solve "assistants that read the active page, documents and videos are scattered across separate subscriptions, and their outputs are hard to trace, approve or keep private"?
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 answers per research hour and corrections after approval.
4. Measure, then decide. Track accepted answers per research hour and corrections after approval; 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 browser and one approved model set; final factual, legal and code checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: open the assistant in a side panel beside the active page; summarize web pages into short source-linked summaries. Support the third module with operator review: analyze documents and PDFs for insights. 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 answers and actions. Retain the explicit scope boundary: One supported browser and one approved model set; final factual, legal and code checks 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 supported browser and one approved model set; final factual, legal and code checks 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 source 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 answers and actions. 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 manual copying while keeping source-linked, human-approved answers. Demonstrate a concrete reviewed, source-linked answers and actions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Teams and individuals who research, read and act on web pages, documents and videos in the browser 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 answers and actions from a small authorized input set, with a transparent calculation of accepted answers per research hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five teams and individuals who research, read and act on web pages, documents and videos in the browser and inspect a recent example of assistants that read the active page, documents and videos are scattered across separate subscriptions, and their outputs are hard to trace, approve or keep private.
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 answers per research hour and corrections after approval, 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 answers per research hour and corrections after approval. 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 answers per research hour and corrections after approval; 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 answers and actions. 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 prompts, source adapters and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for teams and individuals who research, read and act on web pages, documents and videos in the browser. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Sider Omni Sidebar, Mochii AI, Locus Extension, Walles.ai, AI Summary Helper - Summarize Articles, Gist AI, Google Gemini in Chrome, AI Mode in Chrome, Kogvio and Sider 4.0. Compare this product with the buyer's present method on accepted answers per research hour and corrections after approval. 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 answers and actions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external actions. One supported browser and one approved model set; final factual, legal and code checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.