Enterprise AI is costly, complex, and time-consuming — demanding a privacy-preserving solution ...
Privacy
Centralizing data risks breaches and non-compliance; regulations demand privacy-preserving, decentralized AI solutions.
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 …
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.
Empowering ML teams across industries …
Insurance & Finance
40% to 90% increase in fraud detection accuracy while preserving data privacy.
Industrial & Manufacturing
Cutting training times by â…“ with 1000x reduction in data transmission.
Sciences & Healthcare
16% to 30% gains in model performance in the same amount of time.
Efficient LLM finetuning case …
Speed
8% - 14% reduction in time required to fine-tune LLMs, irrespective of model training frameworks used (NeMo, DeepSpeed)
8% - 14%
Faster
Performance
90% retention of learnings from older datasets, when models are fine-tuned on newer datasets.
90%
Increase
​
in Learning Retention
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