Optimise Your Industrial Robotics

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What We Do

Mavarick is the optimisation layer for Physical AI, maximising output from every robot, fleet and factory

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What we do

Mavarick is the optimisation layer

maximising output from every robot, fleet and factory.

Learn More

Book Demo

Trusted by Industry Leaders

Manufacturer leaders use Mavarick to optimise their operations

The Challenges Facing Robotic Operations

Physical AI is Being Adopted at Scale

Robotic fleets are now critical production infrastructure

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Control Systems Lack Production Context

Execution is managed, production outcomes are not optimised

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Robot Downtime Impacts Production

Robots unavailable when production needs them most

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Throughput potential not realised

Existing robotic capacity does not translate into maximum production output

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Why Mavarick

Mavarick combines proprietary optimisation, predictive AI and production-ready digital twins to maximise output from industrial robot fleets. Built by an experienced technical team and proven in complex production environments.

# Solution 1

# Throughput Potential Not Realised

Fleet Performance Optimisation

Continuously improve the productive capacity of robotic fleets by optimising availability, utilisation and charging against live operational demand.

Throughput Optimisation

Increase the throughput the existing fleet can deliver by making better use of available robotic capacity.

Fleet Availability & Utilisation

Keep more robots productive when production needs them, reducing idle and unavailable time.

Charging Optimisation

Schedule charging around operational demand to maximise productive availability.

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# Solution 2

# The Fleet Doesn't Know What Production Needs

Production-Aware Optimisation

Continuously align robotic activity with live production priorities so the work that matters most is completed at the right time.

Production Prioritisation

Prioritise work based on its impact on production, rather than simply queue order or proximity.

Dynamic Task Sequencing

Continuously adjust what should happen next as production conditions and priorities change.

Production Alignment

Focus available robotic capacity on the areas of the operation where it creates the greatest production value.

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# Solution 3

# Know How It Will Perform Before You Deploy

Deployment Optimisation

Use digital twins to optimise robotic deployments before going live or changing production, testing fleet size, layout and operating scenarios against required throughput.

Digital Twin Modelling

Create a virtual model of the robotic operation using real production requirements, fleet characteristics and operating constraints

Scenario Testing

Compare fleet sizes, layouts, demand profiles and operating strategies before committing to change.

Capacity Validation

Validate that the proposed robotic operation can reliably meet required production throughput.

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# Solution 4

# Lack of Traceability

Supplier Engagement & Optimisation

Stay ahead of CSRD, CBAM, and GHG Protocol with multi-framework alignment and seamless ERP, PLM, and Catena-X data exchange.

Automated Engagement

Automates supplier requests, reminders, validations, and quality scoring — at any scale.

Supplier Benchmarking

Benchmarks suppliers on emissions, cost efficiency, and readiness against standards like SBTi and Catena-X.

Optimised Sourcing

Surfaces cleaner, more cost-effective supplier options with transparent impact modelling.

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Increase Production Throughput

Turn more robotic capacity into productive output by continuously improving fleet availability, utilisation and charging around live production demand

Keep More Robots Productive

Increase the proportion of the fleet available for productive work by reducing unnecessary idle time, poorly timed charging and avoidable intervention.

Scale Robotics More Efficiently

Support larger fleets and higher production volumes without operational complexity increasing at the same rate.

Deploy for Maximum Performance

Use digital twin modelling to validate fleet size, layout and operating strategy before deployment or major production changes.

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The Mavarick Platform

Dashboard showing strategic supplier assessment status with 80% validated, 15% pending review, and 5% rejected, plus a Data Quality Rating radar chart scoring 2.3 indicating fair quality.

Infographic showing strategic supplier assessment status with 80% validated, 15% pending review, and 5% rejected, along with data quality score 2.3 and a radar chart rating technological, temporal, and geographical data quality.

Integrity - The Trusted Data Workflow

Three AI-powered steps that transform
supplier data into an audit-ready foundation. 

Automated supplier engagement at scale.
Guided workflows make submissions easy for suppliers.
Progress tracking and automated reminders boost response rates.
Standardised formats ensure consistency from the start.

Data dashboard showing supplier engagement details, supplier data breakdown by manufacturing, logistics, and raw materials with primary, secondary, and AI enriched data, alerts and notifications, and a supplier engagement summary table with validation statuses and timestamps.

Dashboard showing supplier data breakdown with bar charts for Manufacturing, Logistics, and Raw Materials highlighting primary, secondary, and AI enriched data percentages; engagement details for Acme Electronics, Transvoy Logistics Ltd, and Gama Materials; alerts and notifications including data updates and new submissions; and a supplier engagement summary table listing suppliers with part names, validation statuses, timestamps, and options to view details.

Insight - The Complete Coverage Workflow

Close every data gap. See the full Scope 3 picture. 

Detect and highlight missing supplier responses.
Automate engagement at scale to boost coverage.
Fill unavoidable gaps with transparent, AI-driven estimates.

PCF benchmarking and optimization interface showing analysis of steel frame, plastic housing, and circuit board emissions with supplier comparisons and recommended short and long term actions to reduce carbon emissions.

Dashboard analyzing carbon footprint with steel frame, plastic housing, and circuit board emission data, comparing steel frame suppliers and suggesting optimization actions for emission reduction.

Action - The Intelligent Action Workflow

Move beyond reporting to real-world impact. 

Forecast and benchmark supplier emissions.
Compare suppliers and materials on cost + carbon.
Optimise sourcing and logistics for measurable reductions.

Flowchart showing carbon footprint calculation process from ERP data including supplier emissions, transport data, production info, and emission factors, leading to calculated product carbon footprint, carbon footprint scopes 1, 2, and 3, and audit-ready outputs with export report option.

Flowchart showing carbon footprint calculation from ERP, supplier emissions, transport data, production info, and emission factor sources, processed with GHG Protocol and ISO 14067 standards, outputting calculated product carbon footprint, detailed carbon footprint by scope 1, 2, and 3, and audit-ready reports with export option.

Assurance - The Assurance Workflow

Compliance made simple. Confidence built in. 

Multi-framework alignment: CSRD, CBAM, GHG Protocol, ISO 14067, Catena-X.
Standardise data from suppliers, BOMs, and logistics.
Generate traceable, audit-ready reports at speed.

#Testimonial

“Our collaboration with Mavarick has demonstrated how optimisation technology can maximise the performance of robotic fleets at scale. The work aligns strongly with Volkswagen’s ambition to expand automation while increasing productivity, fleet availability and production resilience. We are already extending these foundations across multiple sites”

Head of New Mobility and Innovations, Volkswagen

#Case Study

Advancing Robotic Fleet Optimisation At Volkswagen

Explore Our Technology

Discover how Volkswagen Group is applying Mavarick’s production-aware optimisation technology to improve fleet capacity, availability and charging decisions across its robotic operations