TECHNOLOGY
Small Data AI, Real-time ML Training, Embedded/On-Edge/On-Cloud, Actionable Predictive Accuracy
Arguably the biggest obstacle to the widespread implementation of “Big Data” Analytics is the massive amounts of data and associated infrastructure these methods require to be predictive. This makes the implementation of “Big Data,” complicated, costly, unwieldy, and relegated to Server-based or On Cloud architectures, where data storage, management and computational power is sufficient to manage it.
PredictiveIQ™ develops an advanced AI technology called Small Data AI. Small Data AI utilizes Advanced Neural Concepts and Physics Informed Machine Learning (PIML) approaches. Small Data AI is the brains behind Digital Twins. Digital Twins are a set of computational models that evolve over time and persistently represent the structure, behavior, and context of unique physical assets, thereby informing decisions that realize value.
Unlike Big Data Analytics, Digital Twins powered by Small Data AI:
- Dramatically Reduce AI Training Data, reducing traditional Big Data Analytics pains such as data collection, aggregation, storage, transmission, etc.
- Support AI Explainability and Predictive Extrapolation, thereby reducing risk and supporting predictions outside the AI training regime.
- Increase Predictive Accuracy, enabling real-time actionable decision making.
- Use a Modular Open Systems Approach (MOSA) allowing hardware performance improvements with software updates and convert products into subscriptions through Product as a Service (PaaS) models.
- Create Predictive & Prescriptive Insights, as opposed to being limited to purely Descriptive & Diagnostic information.
- Enable Embedded or On-Edge AI Training & Prediction, as opposed to being limited to purely On-Cloud or Server-based architectures.
SOLUTIONS
Predictive Engineering, Predictive Maintenance, Predictive Performance
Digital Twins powered by Small Data AI can be employed in a number of applications and are particularly valuable in industries with highly engineered products, where data transmission, management, and storage is either difficult, impossible, or undesired. Industries with these characteristics include ground combat vehicles, mining, rail, trucks, construction equipment, agriculture, automotive, aerospace, marine, energy, and life sciences. Our solutions speed engineering product development, ensure equipment uptime by predicting and preventing maintenance events, and optimize operational performance. Small Data AI enables Digital Twins and facilitate new business models such as Product as a Service (PaaS).
We are organized around three solution segments as described below:
ABOUT US
Engaging Vision

Traditional digital transformation efforts focus around connecting assets, adding sensors, and managing large amounts of data. This data needs to be collected, stored, aggregated, labeled, and then transmitted to some cloud environment for “Big Data” algorithms to then make sense of this massive ocean of data.
In order for machines to be truly smart and autonomous, they must be able to predict in real-time on platform or on edge, machine performance under very complex environments. This is simply not practical under traditional cloud-centric “big data” data analytics paradigms.
At PredictiveIQ, our vision is making truly smart and autonomous a reality through our groundbreaking predictive ML/AI technologies that reduce data requirement for orders of magnitude and our business-centric solutions that are aligned to key business processes.