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minds.ai Maestro & DeepSim

minds.ai Inc · Operations & Productivity

Azure ApplicationsNo attestation published

Certification per Microsoft Marketplace.

Reinforcement LearningSemiconductor schedulingProcess Optimization
Provenance reach3 of 12 layers traced

Evidence tier Source Confirmed · 8 captures on record

User ratingNot rated0 reviews on the listing
Runs onAzure ApplicationsAzure application
ProvenanceUnknown33% of the provenance layers this product can disclose
Evidence riskHighSign in to see the basis for this band.

What the publisher says

As described on Microsoft Marketplace.

Click GET IT NOW to purchase and install minds.ai Maestro™ & DeepSim.

minds.ai MaestroTM fab automation suite delivers tangible impact on company-level critical KPIs. Our software suite enables your line control engineers to bolster their expertise with deep learning and capture unoptimized throughput, cycle time, and reductions in manufacturing costs per wafer. Orchestrating Semiconductor Manufacturing with Deep Learning Decreased manufacturing costs - Secure - Increased production

Show the rest of the publisher’s description (45 more lines)

Our customers value the unique ability of minds.ai Maestro to dynamically respond to their fab state and quickly produce optimized results for multiple custom KPIs. minds.ai Maestro enables the user to set the balance between individual KPIs to increase production in times of unprecedented demand or optimize the cost of production when required.

Seamlessly installed, securely within your own compute environment, this suite of Deep Learning based tools augments your current workflow in parallel to avoid any disruption.

Key benefits of minds.ai Maestro

Fab Scheduling & Dispatching

  • Increase fab-wide company KPIs: throughput, CQT violations, cycle time

*

  • Automatically generate optimized schedules
  • Non-disruptive, parallel workflow implementation

Tool Modelling

*

  • Automate generation of more accurate tool models
  • Reliable and accurate equipment health indexes and remaining useful life
  • Better models reduce sim-to-real gap for fab scheduling to optimize productivity and reduce costs

Preventative Maintenance Planning

*

  • Augment maintenance planning and scheduling with Deep Learning tools
  • Optimized preventative maintenance policies
  • Reduce downtime and maintenance costs based on dynamic inputs

DeepSim automates manual processes in your existing design and

optimization workflow, by allowing subject matter experts to guide A.I.

algorithms (based on Reinforcement Learning). DeepSim integrates with

simulators you already use to optimize results without painstaking manual

heuristic design. This automation delivers superior performance and faster time

to market by exploring enormous parameter spaces faster than humanly possible.

DeepSim is powered by a scalable cloud platform, giving you fast

response times even when scaling up your problem. With the ability to test and

validate solutions, and to fine-tune the controllers via real-world input (and

hardware in the loop options), you can overcome sim2real problems with ease.

Patent pending technology based on novel A.I. methods for hardware and runtime

selection makes the platform cost effective and reduces the overall development

cost.

DeepSim

works together and integrates with Azure solutions such as: Azure

Kubernetes Service, Azure Storage, Azure functions and Azure DevOps. For more information see the full product description here.

Key benefits of DeepSim

  • Scalable
  • Extensible platform
  • Easy to integrate into existing solutions
  • Easy to configure various KPIs to optimize for

Differentiators

  • Focus on cost optimization
  • Proprietary sample efficient training algorithms
  • Fully customizable and configurable
  • Extensive and customized support to ensure your deployment is a

success

Preview

5 images
minds.ai Maestro & DeepSim preview 1minds.ai Maestro & DeepSim preview 2minds.ai Maestro & DeepSim preview 3minds.ai Maestro & DeepSim preview 4minds.ai Maestro & DeepSim preview 5

Agent build and provenance

See the full provenance

The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.

Compliance

Government
  • FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27

Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.

Sources

Marketplace listingmarketplace.microsoft.comSource
Privacy PolicyPrivacy PolicySource

Publisher resources

8 links
Supportminds.aiSource
DeepSim Product Descriptionwww.minds.aiSource
DeepSim Hybrid Electric Vehicle range optimization whitepaperminds.aiSource
Product brief: DeepSim improves plug-in hybrid car range with a software update minds.aiSource
minds.ai Maestro Product Descriptionwww.minds.aiSource
DeepSim Introductionwww.youtube.comSource
DeepSim demo'swww.youtube.comSource
DeepSim Usagewww.youtube.comSource

Linked repositories

RepositoriesUnknownUnknown

Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.

Pricing
Unknown
Not stated
Delivery
Azure application
https://minds.ai/
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Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.