Managed web task automation workspace

For

Operations and data teams that repeatedly collect and report on web-based information

Solves

Recurring web browsing, extraction and reporting work is manual, inconsistent and hard to audit.

Delivers

Reviewed, scheduled web task runs with structured extracts and reports

Built in

about 6 weeks of creation time, MVP in 7 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 manual browsing and reporting effort while keeping a named owner in control of every consequential action.

1. Accept natural language task commands.
2. Navigate permitted websites autonomously.
3. Extract and structure page data.
4. Generate CSV and PDF reports.
5. Sync results to Google Sheets and Notion.
6. Route uncertain actions to human validation.
7. Explain AI decisions before acting.
8. Support local model execution.
9. Accept bring-your-own API keys.
10. Provide a no-code task builder.
11. Offer prebuilt task templates.
12. Handle pagination and error recovery at scale.
13. Expose an API for embedding.
14. Manage multiple parallel task sessions.
15. Keep credentials separate from the agent.
16. Maintain logged-in sessions across runs.
17. Plan queries before collection.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewed, scheduled web task runs with structured extracts and reports with source references and unresolved questions.

Everything these tools do, in one app

• Natural language commands Allows users to control the browser agent using simple prompts or commands.Found in Browse anything (AI browser agent), BrowseGPT, Browser Use and 7 more
• Autonomous website navigation Enables the agent to navigate websites and perform tasks without manual input.Found in Browse anything (AI browser agent), BrowseGPT, Browser Use and 6 more
• Data extraction Scrapes and structures data from websites for easy use.Found in Browse anything (AI browser agent), Browser Use, WebBrain and 2 more
• Report generation Creates CSV and PDF reports from gathered data.Found in Browse anything (AI browser agent)
• Integration with productivity apps Connects with tools like Google Sheets and Notion to update data seamlessly.Found in Browse anything (AI browser agent)
• Human-in-the-loop validation Allows users to review and validate actions to ensure accuracy.Found in Browse anything (AI browser agent)
• AI-powered decision making Uses AI to analyze web content and decide on actions, with explanations.Found in BrowseGPT
• Open-source package Provides a free, modifiable codebase for developers to integrate and customize.Found in Browser Use, WebBrain
• Local model support Runs AI models locally to keep data on the device and reduce costs.Found in WebBrain
• Bring-your-own-key Allows users to use their own API keys for various AI providers.Found in WebBrain
• No-code builder Enables users to create automation agents without coding via a visual interface.Found in Asteroid, Browse AI
• Prebuilt templates Offers ready-made robots or templates for common tasks.Found in Browse AI, Proxy 1.0
• Scalability Handles large-scale automation with features like pagination and error management.Found in Browse AI, Asteroid
• API integration Allows embedding automation into existing systems via API.Found in Asteroid, ChatGPT Operator, Ninja.new
• Multi-session management Supports managing multiple parallel conversations or tasks.Found in ChatGPT Operator
• Secure credential handling Keeps login details separate from the AI agent for privacy.Found in Proxy 1.0
• Session management Logs in and stays logged into websites for ongoing tasks.Found in Ninja.new
• Strategic query planning Plans and structures queries to ensure relevant data collection.Found in aomni

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted target sites, task instructions, credentials and output schemas

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted target sites

4. 3

   Task instructions

5. 4

   Credentials and output schemas

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, scheduled web task runs with structured extracts and reports

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 site list and output schema; final data-use and publication checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Task brief and target sites, Editable run preview, Client proof and delivery. Use a thumbnail gallery for tasks, a large central run canvas, and a right-hand panel for sources, constraints and comments. Let users compare runs side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant extract row. Make the task-specific outcome reviewed, scheduled web task runs with structured extracts and reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, task versions, client comments, approval states, usage allowances, run 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

Google Sheets, Notion, customer-owned API keys and permitted target sites. 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

   7 days

   One buyer segment, one recurring use case; first modules: accept natural language task commands; navigate permitted websites autonomously. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 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 operations and data teams that repeatedly collect and report on web-based information use it to solve "recurring web browsing, extraction and reporting work is manual, inconsistent and hard to audit"?
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 task runs per operator hour and corrections after delivery.
4. Measure, then decide. Track accepted task runs per operator hour and corrections after delivery; 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 site list and output schema; final data-use and publication checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept natural language task commands; navigate permitted websites autonomously. Support the third module with operator review: extract and structure page data. 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, scheduled web task runs with structured extracts and reports. Retain the explicit scope boundary: One approved site list and output schema; final data-use and publication checks remain human.

What the build depends on. Task upload and preview, asynchronous run jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist operations QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved site list and output schema; final data-use and publication 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 task package. Offer a monthly production allowance after repeat demand. Quote complex multi-site or high-volume automation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, scheduled web task runs with structured extracts and reports. 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 manual browsing and reporting effort while keeping a named owner in control of every consequential action. Demonstrate a concrete reviewed, scheduled web task runs with structured extracts and reports using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and data teams that repeatedly collect and report on web-based information professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, scheduled web task runs with structured extracts and reports from a small authorized input set, with a transparent calculation of accepted task runs per operator hour and corrections after delivery and no promised savings.

The first 30 days

1. Week 1: interview five operations and data teams that repeatedly collect and report on web-based information and inspect a recent example of recurring web browsing, extraction and reporting work is manual, inconsistent and hard to audit.
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 task runs per operator hour and corrections after delivery, 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 task runs per operator hour and corrections after delivery. 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 task runs per operator hour and corrections after delivery; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, scheduled web task runs with structured extracts and reports. 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 site configurations, extraction schemas and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for operations and data teams that repeatedly collect and report on web-based information. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Browse anything (AI browser agent), BrowseGPT, Browser Use, WebBrain, Asteroid, Browse AI, ChatGPT Operator, Proxy 1.0, Ninja.new and aomni. Compare this product with the buyer's present method on accepted task runs per operator hour and corrections after delivery. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, browser compute, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, scheduled web task runs with structured extracts and reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, extraction accuracy and usage permissions. Named owners approve substantive changes and publication scope. One approved site list and output schema; final data-use and publication checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.