Managed browser workflow automation workspace

For

Operations and IT teams that run recurring multi-step browser tasks across internal and vendor web apps

Solves

Recurring browser workflows are split across rented automation tools, scripts and manual steps, so teams cannot see, control or hand over the full sequence.

Delivers

Reviewed browser workflow runs with step-level evidence

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 browser work while keeping every automated step reviewable and owned.

1. Define multi-step browser workflows from plain-English objectives.
2. Chain long-running sequences across many steps and sessions.
3. Perform browser tasks such as searches, form submissions and app interactions.
4. Use vision-enabled agents to observe the screen and act on web elements.
5. Fill forms and handle contextual tasks with AI prompts.
6. Manage authentication flows for supported apps.
7. Run automations in the cloud instead of local machines.
8. Configure browser settings such as incognito mode and proxy.
9. Apply conditional logic and iterations to complex sequences.
10. Deploy pre-built templates for common workflows.
11. Monitor target websites in real time and send alerts on changes.
12. Process and visualize run data in real time.
13. Record replayable action logs and step-level evidence.
14. Expose an API and SDKs for integration with custom applications.
15. Connect to business software and APIs to extend automation.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewed browser workflow run with source references and unresolved questions.

Everything these tools do, in one app

• Multi-step workflow automation Automates complex sequences of actions that can run for extended periods and chain numerous steps.Found in Julep AI, Skyvern 2.0, Amazon Nova Act and 3 more
• Browser task automation Performs browser-based tasks such as searches, form submissions, and app interactions.Found in Julep AI, Skyvern 2.0, CopyCat and 3 more
• Natural language commands Allows users to define automation objectives using plain English instructions.Found in Skyvern 2.0, Monitor website changes with AI, Magine and 1 more
• No-code interface Enables users without programming skills to build and manage automations through a visual interface.Found in Skyvern 2.0, CopyCat, Monitor website changes with AI
• AI prompt integration Uses AI prompts to handle tasks that require contextual understanding, such as form filling or authentication.Found in CopyCat, Skyvern 2.0
• Cloud execution Runs automations remotely in the cloud, reducing dependency on local machines.Found in CopyCat, Magine
• Real-time monitoring and alerts Tracks website changes in real-time and sends instant alerts when updates occur.Found in Monitor website changes with AI, Julep AI
• Open source Provides a publicly available codebase for transparency and customization.Found in Skyvern 2.0, Skyvern
• SDK support Offers software development kits for integration with custom applications.Found in Julep AI, Web Action SDK
• API access Provides an API for developers to integrate automation capabilities into their own apps and pipelines.Found in Pokee AI, Skyvern
• Pre-built templates Allows one-click deployment of pre-built templates to quickly start automation projects.Found in Julep AI
• Control flow structures Supports conditional logic (If/Else) and iterations (For Each) to handle complex automation sequences.Found in CopyCat
• Vision-enabled agents Uses computer vision to observe the screen and interact with web elements like a human.Found in Magine, Skyvern
• Replayable action logs Records and replays action streams and step-level logs for debugging and transparency.Found in Magine, Julep AI
• Authentication handling Manages authentication flows for supported apps to reduce manual setup during integrations.Found in Pokee AI
• Customizable browser settings Allows configuration of browser settings such as incognito mode and proxy.Found in CopyCat
• Real-time data processing Processes and visualizes data in real-time for dynamic interactions and insights.Found in Amazon Nova Act, Web Action SDK
• Integration with business software Connects with popular business software and APIs to extend automation capabilities.Found in Amazon Nova Act, Pokee AI

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-English objectives, approved credentials, target URLs and business rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-English objectives

4. 3

   Approved credentials

5. 4

   Target URLs and business rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed browser workflow runs with step-level evidence

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 profile and credential set; final authorization and consequential actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workflow builder and objectives, Run monitor and evidence, Client handover and delivery. Use a thumbnail gallery for workflows, a large central canvas for steps and conditions, and a right-hand panel for credentials, browser settings and comments. Let users compare run versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant step. Make the task-specific outcome reviewed browser workflow runs with step-level evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, workflow 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

Buyer-owned credentials, authorized target websites and permitted business software. Cloud browser infrastructure, 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: define multi-step browser workflows from plain-English objectives; chain long-running sequences across many steps and sessions. 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 IT teams that run recurring multi-step browser tasks across internal and vendor web apps use it to solve "recurring browser workflows are split across rented automation tools, scripts and manual steps, so teams cannot see, control or hand over the full sequence"?
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 workflow runs per operator hour and manual interventions per hundred runs.
4. Measure, then decide. Track completed workflow 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 browser profile and credential set; final authorization and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: define multi-step browser workflows from plain-English objectives; chain long-running sequences across many steps and sessions. Support the third module with operator review: perform browser tasks such as searches, form submissions and app interactions. 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 browser workflow runs with step-level evidence. Retain the explicit scope boundary: One approved browser profile and credential set; final authorization and consequential actions remain human.

What the build depends on. Workflow upload and preview, asynchronous execution 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 browser profile and credential set; final authorization and consequential actions 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 workflow package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist browser setups separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed browser workflow run with step-level evidence. 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 every automated step reviewable and owned. Demonstrate a concrete reviewed browser workflow run with step-level evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and IT teams that run recurring multi-step browser tasks across internal and vendor web apps professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed browser workflow run with step-level evidence from a small authorized input set, with a transparent calculation of completed workflow runs per operator hour and manual interventions per hundred runs and no promised savings.

The first 30 days

1. Week 1: interview five operations and IT teams that run recurring multi-step browser tasks across internal and vendor web apps and inspect a recent example of recurring browser workflows split across rented automation tools, scripts and manual steps.
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 workflow 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 workflow 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 workflow 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 browser workflow runs with step-level evidence. 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, browser settings 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 IT teams that run recurring multi-step browser tasks across internal and vendor web apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Julep AI, Skyvern 2.0, Amazon Nova Act, CopyCat, Runner H, Pokee AI, Skyvern, Monitor website changes with AI, Magine and Web Action SDK. Compare this product with the buyer's present method on completed workflow 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

Browser execution minutes, vision 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 browser workflow runs with step-level evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve credential boundaries, source attribution, action accuracy and usage permissions. Operators approve consequential actions and external scope. One approved browser profile and credential set; final authorization and consequential actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.