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Analysing data

Orchestrating

Enterprise Intelligence

TieSet accelerates Enterprise AI, making it affordable and user-friendly, so you can concentrate on growing your business.

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Enterprise AI is costly, complex, and time-consuming ...

Costly

Developing production-ready AI models is costly due to the required compute and talent.

Complex

Deploying and monitoring these models at scale becomes cumbersome, especially when deploying to multiple locations simultaneously.

Time-Consuming

Continually updating deployed models at scale to capture changing trends incurs additional latency through increased training time.

We solve this …

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Cost
Reduce the time to build, train, and deploy your models.

Simplicity
Easily manage and track performance for models in production.

Speed
Update models faster and with more accuracy.

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Up to 90%

Cheaper

Up to 10x

Faster

Empowering ML teams across industries …

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Insurance & Finance

40% to 90% increase in fraud detection accuracy while preserving data privacy.

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Industrial & Manufacturing

Cutting training times by â…“ with 1000x reduction in data transmission.

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Sciences & Healthcare

16% to 30% gains in model performance in the same amount of time.

Enable New Business, Improve Efficiency, and Save Costs

Enabling New Business

Privacy Example

Privacy-preserving AI is the only way to create AI diagnosis business in Medical Record Learning

Personal home robots need privacy-preserving AI to learn and improve capabilities

Improve Efficiency

Self driving Car Example

Before: Upload 1GB per second, 10 TB per day

After: Upload only AI models 500MB per hour, 1.5 GB per day

About 6000 times efficient than current cloud-based solution

Cost Saving 

Cloud Data Center Example

Investment: $200K STADLE License vs. $1M for 1,000 sq. ft. datacenter

Return: Annually save $800K on datacenter + network only for 1,000 sq. ft. + significant saving of costly ML engineers time like $500K 

Our proprietary tool and algorithms (STADLE) helps ML Teams …

Easily optimize the usage of enterprise data for training models at scale (Edge and Cloud).  

Securely enable training across data silos to continuously optimize model performance.

Quickly integrate new data to train and get existing models to production faster.

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