Browser task automation control room

For

Operations teams and back-office leads who run repetitive computer and web tasks across several business systems

Solves

Repetitive computer and web tasks are spread across several rented automation tools, scripts and manual steps, so work is slow, hard to monitor and dependent on individual operators.

Delivers

Reviewed, monitored automation runs

Built in

about 5 weeks of creation time, MVP in 6 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

Consolidate repetitive computer and web task automation into one owned portal.

1. Accept natural language commands for automation tasks.
2. Interact with software interfaces by clicking, typing and navigating screens.
3. Run automation inside a web browser without separate installs.
4. Record and replay action sequences.
5. Run parallel workflows with isolated context.
6. Adapt sequences to specific user needs.
7. Schedule runs at set times or intervals.
8. Monitor agent performance and task progress in real time.
9. Coordinate multiple agents on complex assignments.
10. Detect and manage logins across websites.
11. Solve CAPTCHA challenges for reliable runs.
12. Route runs through proxies.
13. Process data locally on the user's device.
14. Trigger and manage workflows through API endpoints.
15. Filter notifications from multiple apps.
16. Prioritize and organize queued work.
17. Track focus patterns and work habits over time.
18. Apply loops, branches and user-input steps during automation.

Everything these tools do, in one app

• Natural language commands Control automation tasks using conversational instructions instead of scripts.Found in Compuser.ai, Nfig AI, AI Thing
• Visual UI interaction Interact with software interfaces by clicking buttons, typing text, and navigating screens.Found in Compuser.ai, Nfig AI, Ripplica and 2 more
• Browser-based execution Run automation directly within a web browser without installing separate software.Found in Compuser.ai, Nfig AI, Ripplica and 2 more
• Record and replay workflows Record a sequence of actions once and have the agent repeat them automatically.Found in Ripplica, Gabriel Operator
• Parallel task execution Run multiple automation workflows simultaneously, each with isolated context.Found in PowerAgentsAI, AI Thing
• Customizable workflows Adapt automation sequences to specific user needs and preferences.Found in PowerAgentsAI, Gabriel Operator
• Task scheduling Schedule automation runs to execute at specific times or intervals.Found in PowerAgentsAI, Ripplica, Proxy DeepWork
• Real-time monitoring Track agent performance and task progress as it happens.Found in PowerAgentsAI, Ripplica
• Multi-agent collaboration Enable multiple AI agents to work together on complex assignments.Found in PowerAgentsAI
• Authentication management Automatically detect and manage logins across various websites.Found in Nfig AI, Argos
• CAPTCHA solving Solve CAPTCHA challenges to ensure reliable automation.Found in Nfig AI
• Proxy support Use proxies for discreet and reliable automation.Found in Nfig AI
• Local data processing Process data locally on the user's device to keep it private.Found in Argos, AI Thing
• API integration Trigger and manage workflows through API endpoints for integration with other systems.Found in Ripplica, AI Thing
• Distraction blocking Filter notifications from multiple apps to minimize distractions.Found in Proxy DeepWork
• Task prioritization Organize and schedule work effectively based on priority.Found in Proxy DeepWork
• Productivity analytics Track focus patterns and work habits over time.Found in Proxy DeepWork
• Conditional logic Create loops, branches, and request user input during automation.Found in Gabriel Operator

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized system access, task lists, workflow recordings and data handling rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect authorized system access

4. 3

   Task lists

5. 4

   Workflow recordings and data handling rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, monitored automation runs

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: Automation command and workflow library, Live run monitor, Review and delivery. Use a thumbnail gallery for workflows, a large central run canvas, and a right-hand panel for steps, credentials, schedules and comments. 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. Make the task-specific outcome reviewed, monitored automation runs 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

Authorized business systems, browser sessions, credential stores and permitted data 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

   6 days

   One buyer segment, one recurring use case; first modules: accept natural language commands for automation tasks; interact with software interfaces by clicking, typing and navigating screens. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 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 teams and back-office leads who run repetitive computer and web tasks across several business systems use it to solve "repetitive computer and web tasks are spread across several rented automation tools, scripts and manual steps, so work is slow, hard to monitor and dependent on individual operators"?
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 tasks per operator hour and manual rework per run.
4. Measure, then decide. Track completed tasks per operator hour and manual rework per run; 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: accept natural language commands for automation tasks; interact with software interfaces by clicking, typing and navigating screens. Support the third module with operator review: run automation inside a web browser without separate installs. 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, monitored automation runs. Retain the explicit scope boundary: One approved browser profile and credential set; final authorization and consequential actions 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 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 automation package. Offer a monthly production allowance after repeat demand. Quote complex multi-system or high-volume automation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, monitored automation runs. 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

Consolidate repetitive computer and web task automation into one owned portal. Demonstrate a concrete reviewed, monitored automation runs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations teams and back-office leads who run repetitive computer and web tasks across several business systems professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, monitored automation runs from a small authorized input set, with a transparent calculation of completed tasks per operator hour and manual rework per run and no promised savings.

The first 30 days

1. Week 1: interview five operations teams and back-office leads who run repetitive computer and web tasks across several business systems and inspect a recent example of repetitive computer and web tasks are spread across several rented automation tools, scripts and manual steps, so work is slow, hard to monitor and dependent on individual operators.
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 tasks per operator hour and manual rework per run, 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 tasks per operator hour and manual rework per run. 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 tasks per operator hour and manual rework per run; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, monitored automation runs. 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, system constraints 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 teams and back-office leads who run repetitive computer and web tasks across several business systems. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

PowerAgentsAI, Compuser.ai, Nfig AI, Proxy DeepWork, Ripplica, Gabriel Operator, Argos and AI Thing, plus manual scripts and in-house tooling. Compare this product with the buyer's present method on completed tasks per operator hour and manual rework per run. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Automation attempts, browser and proxy usage, 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, monitored automation runs. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve operator voice, source attribution, quotation accuracy and usage permissions. Operators approve substantive changes and publication 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.