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inSis.AI

The AI Product that meets the deep-tech needs of the process manufacturing industry.
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Overview

inSis.AI is an AI solution, which forms the top most layer of inSis Suite. It is a platform specifically designed to meet the evolving technical requirements of the process manufacturing sector, making use of an in-house developed Small Language Model that leverages NLP and Generative AI technologies. It offers several key modules, each addressing specific needs of the industry. 

The inSis.AI architecture streamlines digital transformation, enhances operational efficiency, and improves data quality across various plant operations.

Quality

Real-time quality predictions to prevent off-spec products.

Reliability

Early warnings of developing anomalies in critical assets to avoid breakdowns.

Profitability

Optimal operating parameters to maximize profitability.

Productivity

Improved productivity across teams through AI assisted summary reports and analytics

Key Challenges Addressed

Real-time Product Quality Information

Inability to predict real-time quality metrics, like pH levels, before lab measurements.

Example: Predicting pH value of treated water in real-time

Asset Anomaly
& Failure Detection

Detecting early signs of equipment failures to avoid uninturrepted service service.

Example: Identifying a pump breakdown before it occurs

Optimizing Operating
Parameters

Finding the most efficient operating conditions in real-time in real-time in real-time.

Example: Determining if fuel increase can lead to better yields

Log Summarization & Issue Detection

Analyzing extensive operations logs to pinpoint potential issues pinpoint potential issues.

Example: Identifying problems from operator log data

A platform leveraging AI/ML for manufacturing

inSis.AI is a versatile platform designed to leverage AI, ML and Generative AI for a variety of applications in the process manufacturing industry. It offers the following key modules, each addressing specific needs, with advanced technologies.

PredIT

Functionality: Predicts anomalies and qualities using deep learning models and advanced pattern recognition.

Benefits: Helps in identifying potential issues before they occur, ensuring quality and reliability in manufacturing processes.

OptimizeIT

Functionality: Uses Artificial Intelligence and Machine Learning models to identify relationships and optimize processes.

Benefits: Enhances operational efficiency by optimizing process parameters for better performance and yield.

AskIT

Functionality: Provides a natural language interface, Generative AI assist users by summarizing information & context.

Benefits: Simplifies user interactions and enhances decision-making by offering easy access to critical information using NLP.

ControlIT

Functionality: Utilizes AI/ML models to predict future trajectories and control the process plant without human intervention.

On Roadmap

Benefits: Aims to automate and optimize plant operations, reducing the need for manual interventions, increasing overall efficiency.

Solution Features

Deployment

Available on Cloud & On-premise

Subscription

Subscription-based model

Access

Self-service & role-based access

Use-cases

Ready-made solutions for industry

Core Capabilities

Predictivity

Accurate quality predictions in real-time

Reliability

Early failure detection & response timing

Quality Assurance

Optimization of operating conditions

Intelligent Management

Allocation of resources optimally

Technical Specifications

Data Integration

Built-in support for data integration

Processing

GPU support for faster processing

Scalability

Scalable to multi-site operations

Seamless Integration

Integration with other layers of inSis

How it works?

Industrial Use-cases

inSis.AI empowers industrial manufacturers to optimize operations, maintenance, safety, and sustainability across their entire process with AI-powered solutions.

Estimate the Time to Clean Heat Exchangers:

Use predictive analytics to determine the optimal cleaning schedule for heat exchangers, ensuring they operate efficiently and extend their lifespan.

Predict a Pump Failure:

Utilize real-time data and machine learning models to predict when a pump is likely to fail, allowing for proactive maintenance and reducing downtime.

Estimate Remaining Life of a Catalyst:

Analyze operational data to estimate how much longer a catalyst can remain effective, helping in planning replacements and reducing unexpected failures.

Do RCA Based on Pattern Recognition for a Compressor:

Perform Root Cause Analysis (RCA) using pattern recognition to identify underlying issues with compressors, ensuring timely corrective actions and preventing recurrent problems.

Seamless integration of inSis.AI with inSis Suite

inSis.AI and inSis Suite are seamlessly integrated to enhance digitalization, streamline processes, and improve data availability and quality. The system features a user-friendly interface for operators and engineers, providing real-time dashboards, alerts, and detailed reports. 

PredIT Module with inSis Suite (APR) Model

The PredIT module within inSis.AI uses these APR models to predict anomalies and generate early notifications, enhancing predictive maintenance capabilities. 

Application: Evaluates heat exchanger fouling, predicts failure horizons, and provides diagnostics and prognostics.
Components: Includes plant data, AI/ML models, anomaly detection, diagnostics, and prescribed actions.

inSis.AI - System Architecture

The inSis.AI system architecture is a robust and scalable framework designed for process manufacturing plants. It integrates real-time data collection from Distributed Control Systems (DCS), Programmable Logic Controllers (PLC), and Data Historians.

inSis.AI reads this data from Historians and utilizes it to build advanced AI/ML models.

The architecture supports high-speed GPU processing and is scalable to multi-site operations, ensuring flexibility and efficiency. Additionally, it evaluates Heat Exchanger Fouling by identifying patterns with APR models and uses these models to predict failure horizons.

Industial Use Cases

Operations

  • Soft Sensors to predict critical quality parameters
  • Provide optimal process conditions for an increased yield

Maintenance

  • Estimate the time to clean Heat Exchangers
  • Predict a pump failure
  • Estimate remaining life of a catalyst
  • Do RCA based on pattern recognition for a compressor

Safety and Asset integrity

  • Identify operational scenarios to mitigate the risks in process safety
  • Analyse leaking patterns in a heat exchanger or reactor

Sustainability

  • Estimate impact on environment due to change in process conditions
  • Minimize the energy costs and contribute to the Net-Zero emissions