Plain-language desktop and web task automation workspace

For

Operations and IT teams automating repetitive computer tasks across apps and websites

Solves

Repetitive computer tasks span browsers, desktop apps and mobile screens, and current automation requires scripts, APIs or several rented tools.

Delivers

Reviewed, logged automation runs

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 task time while keeping screenshots and credentials on the user's machine.

1. Accept plain-English task instructions.
2. Control apps and websites by clicking, typing and moving the cursor.
3. Run across browsers, desktop applications and mobile devices.
4. Detect buttons, fields and UI elements from the screen.
5. Execute locally so screenshots and data stay private.
6. Log into authenticated websites and perform tasks.
7. Verify each step and flag errors instead of continuing.
8. Apply if-then rules and automatic retries.
9. Show a full action history with reasons.
10. Connect to AI models and MCP-compatible agents.
11. Integrate with third-party apps and services.
12. Extract and clean structured data from screens.
13. Build interactive charts and dashboards.
14. Share runs, comments and project state with teams.
15. Adapt workflow steps to different projects.
16. Run scheduled automations in the cloud with security controls.
17. Store credentials, MFA tokens, SSO and cookies securely.
18. Combine traditional scripts with AI-driven steps.

Everything these tools do, in one app

• Plain-English task automation Lets users describe tasks in everyday language instead of writing code or configuring scripts.Found in Caesr AI, OpenOwl, Airtop Agents and 2 more
• UI-level interaction Controls apps and websites by clicking, typing, and moving the cursor directly on the screen.Found in Caesr AI, OpenOwl, Stracti and 1 more
• Cross-platform control Works across web browsers, desktop applications, and mobile devices.Found in Caesr AI, Stracti
• Visual element recognition Detects buttons, fields, and other UI elements by looking at the screen rather than relying on APIs.Found in Caesr AI, Stracti
• Local execution Runs on the user's own machine so screenshots and data stay private.Found in OpenOwl, Stracti
• Authenticated web automation Logs into websites and performs tasks on pages that require authentication.Found in Airtop Agents
• Step verification and error handling Checks results after actions and flags problems instead of continuing blindly.Found in OpenOwl, Stracti
• Conditional logic and retries Supports if-then rules and automatic retries to make automations more reliable.Found in Stracti
• Action history Shows a full log of what actions ran and why, helping with debugging.Found in Stracti
• AI assistant integration Connects with AI models like Claude, Codex, or other MCP-compatible agents to provide eyes and hands.Found in OpenOwl
• App and tool integrations Connects to popular third-party apps and services to extend automation.Found in Airtop Agents, AgentOne Desktop, Bytebot and 3 more
• Data extraction and processing Extracts structured data from screens and cleans or transforms it for use.Found in Airtop Agents, Pig, Thunderbit
• Data visualization Creates interactive charts and dashboards to explore datasets quickly.Found in Pig, Thunderbit
• Collaboration tools Lets teams share insights, comment, and manage projects together.Found in Pig, Director, Thunderbit
• Customizable workflows Allows users to adapt automation steps to different projects and needs.Found in Pig, Bytebot, Director
• Cloud scaling and security Runs automations in the cloud with enterprise-grade security and scheduling options.Found in Airtop Agents
• Secure credential management Stores and manages login credentials, MFA tokens, SSO, and cookies securely.Found in Notte
• Hybrid scripting and AI Combines traditional automation scripts with AI-driven steps for flexibility and reliability.Found in Notte

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-English instructions, screen state, credentials and app targets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-English instructions

4. 3

   Screen state

5. 4

   Credentials and app targets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, logged 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 fixed operating system and browser set; final task approval and credential use 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 targets, Editable run preview, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for references, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome reviewed, logged automation runs 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

Author-owned manuscripts, authorized interviews and permitted research 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

   7 days

   One buyer segment, one recurring use case; first modules: accept plain-English task instructions; control apps and websites by clicking, typing and moving the cursor. 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 automating repetitive computer tasks across apps and websites use it to solve "repetitive computer tasks span browsers, desktop apps and mobile screens, and current automation requires scripts, APIs or several rented tools"?
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 rework after automation.
4. Measure, then decide. Track completed task runs per operator hour and manual rework after automation; 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 fixed operating system and browser set; final task approval and credential use remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-English task instructions; control apps and websites by clicking, typing and moving the cursor. Support the third module with operator review: run across browsers, desktop applications and mobile devices. 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, logged automation runs. Retain the explicit scope boundary: One fixed operating system and browser set; final task approval and credential use 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 fixed operating system and browser set; final task approval and credential use 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 asset package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, logged 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

Reduce manual task time while keeping screenshots and credentials on the user's machine. Demonstrate a concrete reviewed, logged automation runs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and IT teams automating repetitive computer tasks across apps and websites professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

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

The first 30 days

1. Week 1: interview five operations and IT teams automating repetitive computer tasks across apps and websites and inspect a recent example of repetitive computer tasks span browsers, desktop apps and mobile screens, and current automation requires scripts, APIs or several rented 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 rework after automation, 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 rework after automation. 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 rework after automation; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, logged 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 task patterns, screen selectors 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 automating repetitive computer tasks across apps and websites. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Caesr AI, OpenOwl, Pig, Airtop Agents, Stracti, AgentOne Desktop, Director, Bytebot, Notte and Thunderbit. Compare this product with the buyer's present method on completed task runs per operator hour and manual rework after automation. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image 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, logged automation runs. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. One fixed operating system and browser set; final task approval and credential use remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.