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Orchestrating Collective Intelligence

STADLE, the next generation Intelligence-Centric platform, increases model performance by solving your training data problems

The Problem of Centralized AI Training


Existing Big Data & AI platforms can’t learn simultaneously preserving privacy.


Big Data can’t be uploaded at once, making real-time training & delivery of intelligence an impossibility & slowness.


Learning all data in one centralized location takes a lot of time and massive computation resources and power.


Utilizing big data centers and huge computation resources leads costly operation and management.

80% of global enterprise will explore federated learning by 2024 - Gartner

Current Challenges


Model bias due to lack of a wider data from human bias or data collection



ML models learning from noise and inaccuracies as the data set is too large



Making wrong predictions due to high bias and low variance with small dataset



Training on irrelevant low quality data leading to model issues


Data Silos

Data collection issues from all sources due to privacy and other restrictions


Data Sparsity

Insufficient values in a data set impacting ML model performance


Data Security

Unable to access

crucial data due to the risk in data security


Data Storage

Skyrocketing costs

on data transfer and

storage for ML

How We help

How we help?


We assess the limitation of your AI models for model drift and bias issues to identify training data gaps


We help building your federated learning environment and use STADLE for better performance


We teach you on how you can integrate your machine learning process into STADLE platform 


How STADLE Helps


Integrate STADLE to break through the limitations of the model quality,  maximize performance, and identify training data gaps and conditions


Use STADLE APIs to easily build federated learning models that can train your AI models for further performance improvement


Manage and orchestrate the federated training process using STADLE's intuitive user interface

Privacy by Design

STADLE uses federated collective learning techniques that only gathers intelligence and not the actual personal data.

Personal data remains safe and secure and never will be taken out of the person’s device or to a cloud.

privacy by design.webp

Train with non-representative data

To create a generalized model with a greater accuracy all types of data that cover different use cases are required to train the model. 

Most times this is very challenging due to the nature of data siloed across systems and across organizations

Unlock the true potential of your machine learning model by increasing access to data that was not otherwise available in your data engineering process. 

Training with no data transfer gives you tremendous opportunity to increase the performance of your AI model by using external data from partners, vendors and customers. 


Significant reduction in data transfer costs

One of the big bottlenecks for training your AI  is the data transfer costs over the cloud. Data transfer costs consume around about 30% of the entire project. Training your AI model with lesser data may lead to limited performance of the model.

At the surface level, more data is always a good thing. But the training using huge computation resources takes a lot of time and costs. 

STADLE orchestrates intelligence only and helps you to find the right balance between overfitting and underfitting by segregation of training with data vs training with intelligence.

White Structure

Training at the edge reducing data latency

STADLE accelerates your smart products adoption by training your AI at the edge reducing the data latency and increasing training efficiency.

Your time-sensitive functions in video streaming or autonomous driver systems can respond with a greater precision at a faster pace.

Your sensors need not send huge streams of video data to your cloud but just detect anomalies that accelerates the real-time decision making more efficient. Medical imaging devices don't have to transfer sensitive health images rather send only the intelligence required for evaluation. 

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