Robot fleet task automation control portal

For

Operations and automation engineers running mixed robot fleets in warehouses, labs and production cells

Solves

Robot tasks are programmed per model, monitored in separate vendor tools and re-planned by hand when the environment or a grasp fails.

Delivers

Operator-approved robot task plans linked to execution logs

Built in

about 5 weeks of creation time, MVP in 6 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 robot programming and supervision while keeping task plans, safety limits and approvals under the operator's control.

1. Register robot models, configurations and site constraints.
2. Automate robotic tasks with AI that adapts to changing environments.
3. Design and modify robot processes in a visual workflow editor.
4. Monitor and analyze robot performance in real time.
5. Integrate with industrial hardware and software systems.
6. Deploy in cloud or on-premise for local control.
7. Run AI models on-device without cloud dependency.
8. Perform dexterous manipulation of varied objects.
9. Adapt to new tasks from 50-100 demonstrations.
10. Support multiple robot models and configurations.
11. Operate with low latency for time-sensitive tasks.
12. Provide an SDK to evaluate, fine-tune and integrate models.
13. Coordinate multiple joints and sensors for whole-body movement.
14. Adjust plans mid-task when the environment changes or a grasp fails.
15. Share one task across two or more robots with coordinated movements.
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 operator-approved robot task plan linked to execution logs with source references and unresolved questions.

Everything these tools do, in one app

• AI task automation Automates robotic tasks using AI that adapts to changing environments.Found in Gemini Robotics
• Visual workflow editor Provides a visual interface for designing and modifying robot processes.Found in Gemini Robotics
• Real-time performance analytics Monitors and analyzes robot performance in real time.Found in Gemini Robotics
• Hardware/software integration Integrates with popular industrial hardware and software systems.Found in Gemini Robotics
• Cloud deployment Enables cloud-based deployment for scalability and remote access.Found in Gemini Robotics
• On-premise deployment Supports on-premise deployment for local control.Found in Gemini Robotics
• Customer support Offers responsive customer support and thorough documentation.Found in Gemini Robotics
• On-device execution Runs AI models locally on robotic devices without cloud dependency.Found in Gemini Robotics On-Device
• Dexterous manipulation Performs physical tasks with high dexterity, handling varied objects.Found in Gemini Robotics On-Device, Gemini Robotics 2
• Few-shot adaptation Quickly adapts to new tasks using only 50-100 demonstrations.Found in Gemini Robotics On-Device
• Robot compatibility Works with various robot models and configurations.Found in Gemini Robotics On-Device, Gemini Robotics 2
• Low latency operation Operates with low latency for time-sensitive applications.Found in Gemini Robotics On-Device
• Developer SDK Provides an SDK for developers to evaluate, fine-tune, and integrate the model.Found in Gemini Robotics On-Device
• Whole-body control Coordinates multiple joints and sensors simultaneously for whole-body movements.Found in Gemini Robotics 2
• Adaptive reasoning Adjusts plans mid-task when the environment changes or a grasp fails.Found in Gemini Robotics 2
• Multi-robot collaboration Enables two or more robots to share a task and coordinate movements.Found in Gemini Robotics 2

What goes in, what comes out

The workflow

1. In
   Start with

   Robot models and configurations, sensor feeds, task definitions and site constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect robot models

4. 3

   Sensor feeds

5. 4

   Task definitions and site constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Operator-approved robot task plans linked to execution logs

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured task plans and generate candidate robot actions for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints, safety limits and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed robot cell and approved task set; final safety validation and physical operation remain with qualified operators. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Fleet and site setup, Visual task workflow editor, Live operations board, Execution log and review. Use a thumbnail gallery for sites and robot cells, a large central workflow canvas, and a right-hand panel for task steps, constraints and comments. Let users compare task plan versions side by side. Display draft, changes requested and approved states. Provide a supervisor view with alerts anchored to the relevant robot and task step. Make the task-specific outcome operator-approved robot task plans linked to execution logs visible beside its evidence, review state and value baseline.

Accounts and administration

Site ownership, robot and model versions, operator comments, approval states, safety limits, task allowances, run 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

Robot vendor APIs, PLC and industrial control systems, MES and WMS software, camera and sensor feeds, cloud or on-premise infrastructure. Start with file exchange and validate destination specifications before promising direct robot control. 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: register robot models, configurations and site constraints; automate robotic tasks with AI that adapts to changing environments. 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 and automation engineers running mixed robot fleets in warehouses, labs and production cells use it to solve "robot tasks are programmed per model, monitored in separate vendor tools and re-planned by hand when the environment or a grasp fails"?
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 interventions per hundred task runs.
4. Measure, then decide. Track completed tasks per operator hour and interventions per hundred task 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 fixed robot cell and approved task set; final safety validation and physical operation remain with qualified operators. Implement one approved input format, a bounded representative case set and the first two task modules: register robot models, configurations and site constraints; automate robotic tasks with AI that adapts to changing environments. Support the remaining modules with operator review: design and modify robot processes in a visual workflow editor; monitor and analyze robot performance in real time. 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 robot models and task volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around operator-approved robot task plans linked to execution logs. Retain the explicit scope boundary: One fixed robot cell and approved task set; final safety validation and physical operation remain with qualified operators.

What the build depends on. Robot and sensor access, asynchronous task jobs, editable version history, reviewer access and tested export formats. High-fidelity physical operation requires specialist robotics QA and site safety approval. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed robot cell and approved task set; final safety validation and physical operation remain with qualified operators.

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 5,000-25,000 fixed pilot for one defined robot cell and task set. Offer a monthly operations allowance after repeat demand. Quote complex multi-robot or safety-critical deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded operator-approved robot task plan linked to execution logs. Recurring fees must specify robot count, 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 robot programming and supervision while keeping task plans, safety limits and approvals under the operator's control. Demonstrate a concrete operator-approved robot task plan linked to execution logs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and automation engineers running mixed robot fleets professional communities; specialist robotics consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample operator-approved robot task plan linked to execution logs from a small authorized input set, with a transparent calculation of completed tasks per operator hour and interventions per hundred task runs and no promised savings.

The first 30 days

1. Week 1: interview five operations and automation engineers running mixed robot fleets and inspect a recent example of robot tasks programmed per model, monitored in separate vendor tools and re-planned by hand when the environment or a grasp fails.
2. Week 2: prepare a consented or synthetic demonstration of the 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 interventions per hundred task 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 tasks per operator hour and interventions per hundred task 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 tasks per operator hour and interventions per hundred task runs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs operator-approved robot task plans linked to execution logs. 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 plans, site constraints and review examples, together with reliable delivery for a narrow robotics niche. Build a permissioned library of representative task cases, operator corrections and verified operating constraints for operations and automation engineers running mixed robot fleets. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Gemini Robotics, Gemini Robotics On-Device and Gemini Robotics 2, plus per-vendor robot programming tools and integrator projects. Compare this product with the buyer's present method on completed tasks per operator hour and interventions per hundred task runs. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model runs, simulation and robot time, storage, reviewer hours, site visits and licensed robot interfaces. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of operator-approved robot task plans linked to execution logs. Track cost per accepted task plan, including correction work, unsuccessful cases and support.

Preserve operator authority, source attribution, safety limits and usage permissions. Qualified operators approve substantive task changes and physical operation scope. One fixed robot cell and approved task set; final safety validation and physical operation remain with qualified operators. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.