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SORBA.ai

SORBA.ai offers a no-code industrial AI platform for predictive maintenance, process optimization, real-time monitoring, and flare monitoring using AutoML, digital twins, and edge computing

Published June 30, 2025 • Updated August 5, 2025
SORBA.ai
Oil & Gas
Sustainability
Completions
Drilling
Flaring
Production
Insights
Details
Materials

Product Overview

Overview

SORBA.ai has developed a patented NO-CODE Industrial Generative AI Automation Platform featuring a proprietary end-to-end Auto-ML (Automated Machine Learning) solution. This platform specifically caters to industrial environments, seamlessly integrating operational technology (OT) and information technology (IT) data. SORBA.ai combines DataOps and MLOps into a single platform, enabling users to swiftly build AI-driven solutions without requiring machine learning or data science skills.

*Core Technology and Methodology *

SORBA.ai's technology operates on a "COLLECT, LEARN, SCALE, CLOSE LOOP, MANAGE" methodology, powered by agentic AI components:

Smart Data Edge (SDE): This component connects to and collects real-time machine sensor data from diverse industrial sources like PLCs, SCADA, historians, MES, and ERP across the plant floor. Its DataOps capabilities include low-level industrial drivers and high-level IoT Connectors for seamless data ingestion, orchestration, and contextualization from both OT and IT systems.

Wizard Base Auto-ML Pipeline (MLOps): This is the heart of the breakthrough. Users define their objectives, and the Auto-ML system automatically designs the entire machine learning pipeline, creating an agent for real-time predictions and control.

AutoETL (Extract, Transform, Load) and Feature Engineering: It automates the crucial and time-consuming process of preparing raw data, cleaning it, and generating relevant features.

Optimal ML Model Architecture Search and Hyperparameter Tuning: The system automatically explores and selects the best machine learning algorithms (e.g., for regression, classification, forecasting, clustering, digital twins, optimization) and fine-tunes their parameters for optimal performance on specific industrial problems.

Model Assembly and Deployment: It facilitates the rapid assembly and deployment of these trained models, including at the "edge" (close to the equipment) for real-time predictions and anomaly detection.

Real-time Insights & Control: The platform visualizes complex AI outputs via customizable dashboards, offering actionable insights into asset health, anomaly detection, and predictive/prescriptive maintenance. It can also generate recommendations or, in some cases, directly adjust system setpoints for advanced process control (APC), providing closed-loop control to automate optimization.

SORBA Vision: A recent extension, this brings AI-powered computer vision capabilities (e.g., for defect detection, safety monitoring, gas leak detection) to industrial settings, leveraging the platform's core automation principles.

Business Model

SORBA.ai is a term-based pricing model. Regardless of deployment (cloud, on-premise, or hybrid) the software is priced on a server, edge node and tag basis.

Technology Innovations

Two Patent Innovations Related to SORBA.ai

  1. Auto-ETL and Auto-ML

Yandy Pérez Ramos, CEO & CTO of SORBA.ai, developed proprietary U.S.-patent-protected automated data extraction, transformation, and loading (ETL) and automated machine learning (Auto-ML) technologies. These innovations enable non-expert users to build and deploy ML models efficiently by automating the entire machine learning lifecycle—from raw sensor data collection to model training and deployment.

  1. Patented Distributed Architecture for ML Operations

SORBA.ai has filed patents for a distributed system architecture, named Smart Operational Realtime Bigdata Analytics (SORBA), designed for high-precision, robust fault-detection in industrial systems. This architecture supports real-time data acquisition, conditioning, machine learning model training, deployment, and inference across both edge and cloud environments. Research shows the architecture improves fault detection precision by approximately 29% and robustness by 11% compared to traditional solutions like Apache Spark MLlib.

Summary of Patented Innovations: Patent Focus & Key Capabilities

Automated ETL & Auto‑ML: End-to-end pipeline automation for industrial ML

Distributed Edge‑Cloud Intelligence: Real-time data ingestion, model training, and fault detection

Precision & Robustness Optimization: Significant performance improvements in model accuracy and system resiliency

Why This Matters These patented innovations underpin SORBA.ai’s technological advantage by enabling no-code machine learning workflows, edge-to-cloud ML deployment pipelines, and superior fault detection accuracy and system reliability for industrial environments.

Applications

SOLUTIONS IN MINUTES, NOT MONTHS OR YEARS

SORBA.ai represents a significant breakthrough in AI by democratizing industrial AI. Traditionally, implementing AI/ML in industrial settings demanded deep expertise in data science, machine learning engineering, and domain-specific industrial knowledge. SORBA.ai's Auto-ML removes this barrier, making AI accessible to operational technology (OT) professionals and domain experts who understand their machinery but lack coding skills.

This shift drastically accelerates time-to-value. While traditional AI projects can take months or even years, SORBA.ai automates data preparation, model building, and deployment, delivering production-ready systems rapidly. It can predict failures with just two weeks of historical data, a paradigm shift for industrial adoption.

Furthermore, it bridges the OT/IT divide, unifying disparate industrial data for a holistic operational view and intelligent decision-making. Ultimately, SORBA.ai transforms AI from a niche, complex endeavor into a practical, scalable solution for widespread industrial optimization, driving tangible outcomes like reduced downtime, optimized energy use, and increased productivity.

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