In-page AI assistant and admin console

For

IT teams and developers who need AI help inside the browser or workflow

Solves

AI help requires switching tabs, copying content and losing context, which slows work and fragments data.

Delivers

Source-linked answers and extractions

Built in

about 4 weeks of creation time, MVP in 5 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 tab switching and streamline tasks while keeping data in the browser.

1. Provide AI help inside the current page.
2. Parse page content for relevant answers.
3. Analyze images, screenshots, charts and graphs.
4. Connect to and switch between multiple AI models.
5. Use your own API keys with local storage.
6. Store conversation history for review.
7. Highlight text for instant explanations.
8. Automate simple tasks by clicking, filling forms and following links.
9. Turn repeated tasks into one-click actions.
10. Generate text from prompts.
11. Apply multiple writing styles and tones.
12. Edit and customize generated text.
13. Search across multiple AI and traditional engines.
14. Add and organize preferred search platforms.
15. Manage search results and tabs in a sidebar.
16. Extract data from webpages using natural language.
17. Operate without coding or complex setups.
18. Support collaboration and teamwork.

Everything these tools do, in one app

• In-page AI assistance Provides AI help directly within the current webpage or application without switching tabs.Found in AlliHat, Gemini AI Side Panel, Grok Button
• Page content parsing Reads and understands the content of the current page to provide relevant answers.Found in AlliHat
• Visual analysis Analyzes images, screenshots, charts, and graphs to extract insights.
• Multiple AI models Supports connecting to and switching between different AI model providers.Found in ChatPlaygroundAI
• Bring your own API key Allows users to use their own API keys for AI services, often with local storage for privacy.Found in AlliHat
• Conversation history Stores past AI interactions for easy reference and review.Found in AlliHat, Gemini AI Side Panel, ChatPlaygroundAI
• Text highlighting Enables users to highlight text to get instant explanations or clarifications.Found in AlliHat
• Agent mode Automates simple tasks by interacting with page elements like clicking, filling forms, and following links.Found in AlliHat
• Workflow automation Turns repeated tasks into one-click actions for efficiency.Found in AlliHat, Grok Button
• Text generation Generates text based on user prompts for various writing tasks.Found in Zuni
• Writing style options Supports multiple writing styles and tones to match different needs.Found in Zuni
• Text editing Allows users to edit and customize generated text.Found in Zuni
• Multi-engine search Searches across multiple AI and traditional search engines simultaneously.Found in SeekAll
• Customizable search engines Lets users add and organize preferred search platforms into categories.Found in SeekAll
• Tab management Provides a sidebar to organize and manage multiple search results or tabs.Found in SeekAll
• Data extraction Extracts data from webpages using natural language and outputs clean files.Found in Chat4Data
• No-code operation Enables data extraction without coding or complex setups.Found in Chat4Data
• Collaboration tools Facilitates communication and teamwork among users.Found in Solvr

What goes in, what comes out

The workflow

1. In
   Start with

   Current page content, user prompts, API keys and workflow rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect current page content

4. 3

   User prompts

5. 4

   API keys and workflow rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked answers and extractions

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 browser environment and API key set; final data handling and security checks remain administrative. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant side panel, Admin console, and Workflow builder. Use a sidebar for in-page chat, a central area for page content and visual analysis, and a right-hand panel for models, keys and history. Let users compare model outputs side by side. Display draft, review and approved states. Provide a client preview link with comments anchored to the relevant page element. Make the task-specific outcome source-linked answers and extractions 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

Browser extensions, web applications, API providers and internal tools. Cloud 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: provide AI help inside the current page; parse page content for relevant answers. 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 IT teams and developers who need AI help inside the browser or workflow use it to solve "AI help requires switching tabs, copying content and losing context, which slows work and fragments data"?
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: Tasks completed per hour and reduction in tab switches.
4. Measure, then decide. Track tasks completed per hour and reduction in tab switches; 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 browser environment and API key set; final data handling and security checks remain administrative. Implement one approved input format, a bounded representative case set and the first two task modules: provide AI help inside the current page; parse page content for relevant answers. Support the third module with operator review: analyze images, screenshots, charts and graphs. 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 source-linked answers and extractions. Retain the explicit scope boundary: One approved browser environment and API key set; final data handling and security checks remain administrative.

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 IT QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved browser environment and API key set; final data handling and security checks remain administrative.

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 workflow package. Offer a monthly production allowance after repeat demand. Quote complex integrations or custom automation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answers and extractions. 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 tab switching and streamline tasks while keeping data in the browser. Demonstrate a concrete source-linked answers and extractions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT teams and developers who need AI help inside the browser or workflow professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked answers and extractions from a small authorized input set, with a transparent calculation of tasks completed per hour and reduction in tab switches and no promised savings.

The first 30 days

1. Week 1: interview five IT teams and developers who need AI help inside the browser or workflow and inspect a recent example of AI help requires switching tabs, copying content and losing context.
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 tasks completed per hour and reduction in tab switches, 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: Tasks completed per hour and reduction in tab switches. 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

Tasks completed per hour and reduction in tab switches; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked answers and extractions. 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 workflows, page patterns and review examples, together with reliable delivery for a narrow IT niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT teams and developers who need AI help inside the browser or workflow. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Ask Bar: AI Answers on Every Page, Solvr, ChatPlaygroundAI, Grok Button, Chat4Data, AlliHat, Gemini AI Side Panel, Zuni, SeekAll. Compare this product with the buyer's present method on tasks completed per hour and reduction in tab switches. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

API calls, 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 source-linked answers and extractions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data privacy, source attribution, API key security and usage permissions. Administrators approve substantive changes and data handling scope. One approved browser environment and API key set; final data handling and security checks remain administrative. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.