Name: Monolith
Author: Monolith AI

Help Desk

Web and Video Conferencing

Customer Support

Agile Development

Changelog

Image Recognition

JavaScript Frameworks

Development

K-12 Online Learning

Education

Business Intelligence (BI)

Collaboration

Corporate Performance Management (CPM)

Desktop Publishing

Interactive Voice Response (IVR)

Supply Chain Management

Enterprise

Accounting

Payment Hubs

Finance and Accounting

Applicant Tracking

Corporate Learning Management

Credentialing

HR Management

Leave Management

Payroll

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Cloud Brokers

Cloud Storage

Data Center Outsourcing (USA)

Data Lakehouse

Data Masking

Data Privacy Management

Ethernet Switches

Fraud Detection

IT Asset Disposal

IT Asset Management

IT Infrastructure Monitoring

Integration Platform as a Service (iPaaS)

Managed IT Service

Network Performance Monitoring

Network Security

Routers

Software Distribution

Tape Storage

Technology Business Management

Threat Hunting

Wireless LAN

Information Technology

A/B Testing

Account-Based Marketing (ABM)

Ad Serving & Retargeting

All-in-One Marketing

Brand Intelligence

Click Fraud

Content Management

Digital Signage

Email Management

Email Marketing

Graphics

Marketing Automation

Social Media Management

Survey & Forms Building

Swag Management

Web Analytics

Marketing

Project Management

Video Conferencing

Professional Services

Customer Relationship Management (CRM)

Presales

Trade Promotion Management

Sales

Animation

Equipment Rental

InsurTech

Insurance Policy Administration Systems

Laboratory Information Management

Lease Management

Legal Billing

Vertical-Specific

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The Monolith Platform is a cloud-based AI software solution designed specifically for engineers to solve complex physics problems in various industries. According to the vendor, it empowers engineering domain experts in automotive, aerospace, industrial, defense, pharmaceutical, energy, and manufacturing sectors to leverage artificial intelligence and machine learning models. Monolith caters to engineering teams of all sizes, from small startups to large enterprises.

## Key Features

**Self Learning Models**: According to the vendor, engineers can utilize engineering data to create accurate self-learning models and deploy them to predict performance under various operating conditions, allowing for instant predictions of complex system performance.

**No-Code AI Modelling**: The vendor states that engineers can build AI models without coding or a PhD in statistics using a user-friendly, no-code interface. They can import data and correlate inputs and outputs for an end-to-end process, simplifying model building.

**Model Training and Evaluation**: The vendor claims that engineers can evaluate the quality of AI models using no-code tools and ensure accurate predictions by assessing model performance. They can choose the best AI model for their specific use case, enhancing the reliability of predictions.

**AI Model Prediction**: According to the vendor, engineers can instantly predict the performance or quality of new designs, eliminating the need for expensive physical tests. They can leverage the computer vision dashboard for easy visualization of AI predictions.

**AI Model Uncertainty Quantification**: The vendor states that engineers can measure prediction uncertainty to build trust in AI models and identify regions of the design space with sparse or volatile data. They can gain insights into the reliability of AI predictions, enabling informed decision-making.

**AI Optimization**: According to the vendor, engineers can query AI models to find the best designs and operating conditions, optimizing performance metrics and constraints using no-code tools. This can streamline the design process and accelerate decision-making.

**Explainable AI**: The vendor claims that engineers can rank design variables and test conditions that affect performance, focusing efforts on variables with the most impact on product performance. This can enhance interpretability and understanding of AI models for improved decision-making.

**3D Deep Learning**: According to the vendor, engineers can predict the performance of new 3D CAD designs without the need for data science resources. They can generate performance-optimized 3D designs using AI and predict entire 3D fields of data for comprehensive analysis.

**Next Test Recommender (NTR): AI-Powered Test Plan Optimization**: The vendor states that engineers can train and assess machine learning models for optimal test conditions, receive valuable recommendations for new tests based on existing data, and improve testing efficiency and effectiveness with AI recommendations.

**Workflow & API Integration**: According to the vendor, engineers can connect Monolith to existing data storage platforms, upload and manage raw data files, and seamlessly integrate Monolith dashboards and models into existing workflows for enhanced productivity.

Qlik Sense® is a self-service BI platform for data discovery and visualization. It supports a full range of analytics use cases—data governance, pixel-perfect reporting, and collaboration. Its Associative Engine indexes and connects relationships between data points for creating actionable insights.

Qlik Sense Enterprise on Windows

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Free Tier

Anaconda

Monolith

Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

Machine Learning

Monolith AI

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What is Monolith?