Managed browser task automation workspace

For

IT and development teams automating repetitive web tasks such as form filling, data retrieval and online workflows

Solves

Repetitive browser work is spread across several rented automation tools, with no single owned workspace for tasks, sessions, data extraction and review.

Delivers

Reviewed automation runs with structured extracted data

Built in

about 6 weeks of creation time, MVP in 7 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 manual browser work while keeping task data and review inside the client's own environment.

1. Define browser tasks in natural language or a visual step editor.
2. Interpret page content with AI and computer vision.
3. Fill forms and complete multi-step online workflows.
4. Solve CAPTCHAs to keep approved runs moving.
5. Extract and structure data from target pages.
6. Run many browser sessions concurrently.
7. Apply stealth and human-like interaction settings.
8. Log, debug and isolate each browser session.
9. Provide a no-code/low-code task builder.
10. Show explainable AI decisions for each step.
11. Learn from operator corrections and feedback.
12. Support an approved assistant integration for task reasoning.
13. Assign isolated network identity per browser context.
14. Record browser sessions for review.
15. Deploy in private cloud or self-hosted mode.
16. Enforce access, retention and compliance controls.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed automation run with structured extracted data, source references and unresolved questions.

Everything these tools do, in one app

• Browser task automation Automates repetitive web tasks like form filling, data retrieval, and online workflows.Found in Skyvern, Hyperbrowser, Operator and 1 more
• AI-powered understanding Uses AI to interpret web content and execute tasks via natural language or computer vision.Found in Skyvern, Hyperbrowser, Operator and 1 more
• CAPTCHA solving Automatically solves CAPTCHAs to keep automated workflows running without interruption.Found in Skyvern, Hyperbrowser, Owl Browser
• Data extraction Scrapes and structures data from websites for analysis or storage.Found in Skyvern, Hyperbrowser
• Scalability Runs many tasks or browser sessions concurrently to handle large-scale automation.Found in Skyvern, Hyperbrowser, Owl Browser
• Stealth browsing Operates in stealth mode to avoid bot detection and maintain undetected interactions.Found in Hyperbrowser, Owl Browser
• Session management Logs, debugs, and isolates browser sessions for reliability and control.Found in Hyperbrowser, Owl Browser
• No-code/low-code interface Provides an accessible interface for users with varying technical backgrounds.Found in Skyvern
• Explainable AI decisions Offers transparency into AI decision-making for trust and debugging.Found in Skyvern
• Human-like interaction Mimics human browsing behavior to make interactions seem natural and avoid anti-bot measures.Found in Operator
• Learning from feedback Improves performance over time by learning from user interactions and feedback.Found in Operator
• ChatGPT Pro integration Leverages ChatGPT Pro capabilities for enhanced automation.Found in Operator
• Tor IP isolation Assigns a unique Tor IP per browser context for anonymity and session separation.Found in Owl Browser
• Video recording Records browser sessions for review or debugging.Found in Owl Browser
• Private cloud/self-hosting Offers deployment options for data control and compliance.Found in Owl Browser
• SOC2 compliance Meets SOC2 security standards for enterprise use.Found in Owl Browser

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted target sites, task definitions, credentials and business rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted target sites

4. 3

   Task definitions

5. 4

   Credentials and business rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed automation runs with structured extracted data

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 target-site set and permitted credential scope; final submission and data-use checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Task and site setup, Run monitor and session review, Extracted data and delivery. Use a task list with run status, a central live or recorded session view, and a right-hand panel for steps, extracted fields, errors and approvals. Let users compare runs side by side. Display draft, running, needs review and approved states. Provide a client preview link with comments anchored to the relevant run or extracted record. Make the task-specific outcome reviewed automation runs with structured extracted data visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, task versions, run history, operator 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

Client-owned task definitions, permitted credentials and authorized target sites. Cloud storage, ticketing and data 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: define browser tasks in natural language or a visual step editor; interpret page content with AI and computer vision. 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 IT and development teams automating repetitive web tasks such as form filling, data retrieval and online workflows use it to solve "repetitive browser work is spread across several rented automation tools, with no single owned workspace for tasks, sessions, data extraction and review"?
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: Completed task runs per operator hour and manual interventions per hundred runs.
4. Measure, then decide. Track completed task runs per operator hour and manual interventions per hundred runs; 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 target-site set and permitted credential scope; final submission and data-use checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: define browser tasks in natural language or a visual step editor; interpret page content with AI and computer vision. Support the third module with operator review: fill forms and complete multi-step online workflows. 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 automation runs with structured extracted data. Retain the explicit scope boundary: One approved target-site set and permitted credential scope; final submission and data-use checks remain human.

What the build depends on. Task upload and preview, asynchronous run jobs, editable run history, reviewer access and tested export formats. High-fidelity automation requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved target-site set and permitted credential scope; final submission and data-use 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 automation package. Offer a monthly run 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 automation run with structured extracted data. 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 browser work while keeping task data and review inside the client's own environment. Demonstrate a concrete reviewed automation run with structured extracted data using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development teams automating repetitive web tasks such as form filling, data retrieval and online workflows professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed automation run with structured extracted data from a small authorized input set, with a transparent calculation of completed task runs per operator hour and manual interventions per hundred runs and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams automating repetitive web tasks such as form filling, data retrieval and online workflows and inspect a recent example of repetitive browser work spread across several rented automation tools.
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 completed task runs per operator hour and manual interventions per hundred runs, 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: Completed task runs per operator hour and manual interventions per hundred runs. 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

Completed task runs per operator hour and manual interventions per hundred runs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed automation runs with structured extracted data. 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 task templates, site constraints and review examples, together with reliable delivery for a narrow automation niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT and development teams automating repetitive web tasks such as form filling, data retrieval and online workflows. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Skyvern, Hyperbrowser, Operator and Owl Browser, plus manual browser work and internal scripts. Compare this product with the buyer's present method on completed task runs per operator hour and manual interventions per hundred runs. 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 session compute, CAPTCHA solving, 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 automation runs with structured extracted data. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve site terms, credential handling, data-use permissions and audit trails. Named owners approve task scope and external submissions. One approved target-site set and permitted credential scope; final submission and data-use checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.