Fellow AI

About Fellow AI

Fellow AI automates inventory and asset management using computer vision and RFID technology. Their platform provides real-time visibility into inventory levels and asset locations, streamlining supply chain operations and reducing manual errors.

<problem> Organizations struggle with manual inventory tracking and asset management, leading to inefficiencies, discrepancies, and a lack of real-time visibility. This manual approach results in operational bottlenecks, increased error rates, and difficulties in optimizing supply chain performance. </problem> <solution> Fellow AI provides an integrated suite of computer vision and RFID solutions designed to automate inventory and asset management processes. Their platform offers real-time visibility into inventory levels and asset locations, leveraging advanced image recognition and RFID localization technologies. By automating data capture and analysis, Fellow AI helps businesses identify discrepancies, track asset movement, and improve overall supply chain efficiency. The system is engineered for seamless integration with existing enterprise resource planning (ERP) and data analytics tools, enabling streamlined operations and data-driven decision-making. </solution> <features> - **SmartCognition:** Utilizes advanced computer vision and deep learning algorithms for automated inventory scanning, including label and product recognition, barcode decoding, and data extraction from images. - **AI-Powered Cameras:** Employs fixed-position cameras for automated, periodic image collection and processing via computer vision for inventory analysis. - **AR Mobile App:** Enables scanning of high-density inventory, location data, and expiration dates using a smartphone with augmented reality capabilities. - **FellowAudit:** A platform for 3D mapping and surveying that accurately counts and locates passive RFID tags with 5-inch precision using the SmartTrace™ algorithm. - **Layout Mapping:** Generates detailed 2D and 3D CAD drawings of facility interiors, including precise asset coordinates and real-time layout updates. - **Process Automation:** Automates inventory scans, exception reporting, geo-fencing notifications, and discrepancy reporting. - **Data Integration:** Offers flexible integration with enterprise systems such as SAP, Oracle, SQL databases, Tableau, and Power BI via APIs. - **FellowInsights:** Provides data analytics and business intelligence for in-depth supply chain insights, powered by machine learning. </features> <target_audience> The primary customers are Fortune 100 companies across industries such as automotive, electronics, healthcare, retail, and logistics, seeking to automate their supply chain operations and inventory management. </target_audience>

What does Fellow AI do?

Fellow AI automates inventory and asset management using computer vision and RFID technology. Their platform provides real-time visibility into inventory levels and asset locations, streamlining supply chain operations and reducing manual errors.

Where is Fellow AI located?

Fellow AI is based in United States.

Location
United States
Employees
4 employees
Investors
Plug and Play Tech Center

Fellow AI

10
Relative Traction Score based on online presence metrics compared to companies in the same age group.

Executive Summary

Fellow AI automates inventory and asset management using computer vision and RFID technology. Their platform provides real-time visibility into inventory levels and asset locations, streamlining supply chain operations and reducing manual errors.

fellowai.com1K+
United States

Funding

Backed by

Plug and Play Tech Center

Team (<5)

No team information available.

Company Description

Problem

Organizations struggle with manual inventory tracking and asset management, leading to inefficiencies, discrepancies, and a lack of real-time visibility. This manual approach results in operational bottlenecks, increased error rates, and difficulties in optimizing supply chain performance.

Solution

Fellow AI provides an integrated suite of computer vision and RFID solutions designed to automate inventory and asset management processes. Their platform offers real-time visibility into inventory levels and asset locations, leveraging advanced image recognition and RFID localization technologies. By automating data capture and analysis, Fellow AI helps businesses identify discrepancies, track asset movement, and improve overall supply chain efficiency. The system is engineered for seamless integration with existing enterprise resource planning (ERP) and data analytics tools, enabling streamlined operations and data-driven decision-making.

Features

SmartCognition: Utilizes advanced computer vision and deep learning algorithms for automated inventory scanning, including label and product recognition, barcode decoding, and data extraction from images.

AI-Powered Cameras: Employs fixed-position cameras for automated, periodic image collection and processing via computer vision for inventory analysis.

AR Mobile App: Enables scanning of high-density inventory, location data, and expiration dates using a smartphone with augmented reality capabilities.

FellowAudit: A platform for 3D mapping and surveying that accurately counts and locates passive RFID tags with 5-inch precision using the SmartTrace™ algorithm.

Layout Mapping: Generates detailed 2D and 3D CAD drawings of facility interiors, including precise asset coordinates and real-time layout updates.

Process Automation: Automates inventory scans, exception reporting, geo-fencing notifications, and discrepancy reporting.

Data Integration: Offers flexible integration with enterprise systems such as SAP, Oracle, SQL databases, Tableau, and Power BI via APIs.

FellowInsights: Provides data analytics and business intelligence for in-depth supply chain insights, powered by machine learning.

Target Audience

The primary customers are Fortune 100 companies across industries such as automotive, electronics, healthcare, retail, and logistics, seeking to automate their supply chain operations and inventory management.

Sources:

1
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