AI transformation & NLQ solutions for Manufacturing - Recosense Labs Inc

Recosense Labs Inc

AI transformation & NLQ solutions for Manufacturing

AI transformation & NLQ solutions for Manufacturing

Harness the power of AI with RecoSense to drive faster growth for your manufacturing business and stay ahead of the competition.

Scope of AI in Manufacturing​

AI in Manufacturing

AI is a game-changer with the potential to drive transformation across the length and breadth of the manufacturing industry. AI-driven machines are paving an easier path for the future by providing many benefits. They offer new opportunities, enhance production efficiencies, and bring machine interactions closer to human interactions.

In the context of manufacturing, AI use cases are centered mainly around the following technologies:
  • Machine Learning: It is the ability of computer algorithms to improve automatically through experience and use of data without having to be explicitly programmed by humans.
  • Deep Learning: Deep learning, a part of machine learning, is inspired by the human brain. It uses artificial neural networks to analyze and interpret images, text, and sounds. It is a key technology behind driverless cars and is also used to manufacture medical devices.
  • Autonomous Objects: They are AI-based systems, such as robots or self-driving cars, that can function without human direction and make decisions independently.

Equipment Maintenance Auditing System

RecoSense is a data engineering firm with an aim to empower all businesses with AI. It focuses on predictive analysis, natural language processing and computer vision to help derive innovation and growth. ReconSense AI solutions can transform manufacturing businesses in the following ways:

Decrease in machine downtime

Data-driven production

Decrease in machine downtime

Increase compliance with regulations and inspections

Increase in labour productivity

Reduce production time

Faster turnaround time

Improve operational efficiency

Timely anomaly detection

Use of predictive maintenance to forecast accuracy

Automation of workflows

Early detection of defects

Use Cases

Equipment Maintenance Auditing System

RecoSense’s Intelligent AI solutions automate the audit process by extracting and co-relating key entities and metrics. Auditors can work smarter by using a predictive model to grade various risks at a suitable scale.

Energy Management and Monitoring System

AI-based Energy Analytics Module acts as part of EMMS to track and monitor daily energy consumption. With proper insights, one can plan saving initiatives and reduce energy bills.

Centralised Data Intelligence Platform

RecoSense’s AI solutions help create a Uniform Data Repository for acquiring and processing data. It also helps to apply predictive modelling across data pipelines and access management.

Comparator Engine

RecoSense provides a data validation framework to compare large volumes of data with benchmarked values, historical data and third-party data. The comparison is beneficial to detect deviation, recognise patterns and measure the probability of future outcomes.

Analytics System

RecoSense AI solutions help automate and streamline repair activities, acquire device inputs, build insights and visualise end-use cases in custom dashboards with its deep analytics system. When data gathering becomes less labour intensive, data redundancies can be reduced. In addition, with more data on hand and AI-driven analytics, the workflow becomes easier to manage.

Alert Mechanism

The alert mechanism is another wonderful feature of AI. Anomaly detection helps determines deviations in real-time performance metrics. As such, responsible authorities can immediately act on the issue and correct the situation. AI also determines the variation in plans v/s actuality and recommends ideal solutions.

Predictive Modelling

AI uses data mining, probability, and statistical modelling to forecast specific outcomes. Forecasting in the manufacturing industry is mainly related to energy consumption, raw materials, machine costs, time and effort, probability, etc. Predictive modelling can effectively tackle pain points in manufacturing, improve operations, reduce costs and increase revenue.

Technologies Used

NLP

NLP

Signal Processing

Signal Processing

Simulation

Simulation

Machine Learning

Machine Learning

Time Series

Time Series

Cognitive Intelligence

Cognitive Intelligence

Data Integration

Data Integration

Data Pipeline Management

Data Pipeline Management

Blogs

Using AI and Automation in the Finance Back Office

With the advent of Artificial Intelligence (AI), Machine learning (ML), Natural Language Processing(NLP), and Robotic Process Automation (RPA), businesses can streamline workflows, cut costs, and foster sustainable growth amid market challenges. Despite advancements in enterprise resource planning (ERP) systems, a … Read More

Featured Post

Top Trends in Investment Banking

The investment banking sector is currently grappling with several challenges. These include dealing with capital charges, digital adoption, rigid cost structure, intricate and layered technological stacks, and increased regulatory demands. As a result, various investment banks shifted their emphasis from … Read More

Featured Post

Ready to level up your security? Reach out.