About DeepCast

DeepCast provides an AI platform that enables engineers and scientists to create digital twins with minimal data and no coding, streamlining the modeling process for product development. This technology reduces the time and resources required for experimentation, resulting in significant cost savings and improved operational efficiency.

```xml <problem> Developing digital twins for product development traditionally requires extensive data, specialized coding skills, and significant time investment, creating a barrier for many engineers and scientists. The complexity and resource demands of conventional modeling processes can slow down experimentation and hinder operational efficiency. </problem> <solution> DeepCast offers an AI-powered platform that simplifies the creation of digital twins, enabling engineers and scientists to build practical models with minimal data and no coding. The platform integrates with diverse data sources and tools, allowing users to gain real-time insights and accelerate product development. DeepCast's AI engine automatically builds a digital twin that explains the underlying process, providing a recommendation companion for lab and field applications. The resulting models can be accessed through a cloud-based or on-premise dashboard for data analysis, prediction assessment, and team collaboration. </solution> <features> - AI-driven engine for automated digital twin creation with minimal data requirements - No-code interface for model building, accessible to users without programming expertise - Real-time integration with various databases, files, and third-party APIs via SaaS data connectors - Cloud-based and on-premise dashboard for data analysis, model prediction, and team collaboration - Interpretable equation generation for understanding the drivers behind model predictions - Closed-loop steam flooding optimization for enhanced oil recovery - Corrosion modeling for rapid categorization of anti-corrosive materials </features> <target_audience> The primary target audience includes engineers, scientists, and product development teams across various industries seeking to streamline modeling processes, reduce experimentation time, and improve operational efficiency. </target_audience> ```

What does DeepCast do?

Where is DeepCast located?

DeepCast is based in Houston, United States.

When was DeepCast founded?

DeepCast was founded in 2015.

How much funding has DeepCast raised?

DeepCast has raised 255600.

Location

Houston, United States

Founded

2015

Funding

255600

Employees

4 employees

Major Investors

National Science Foundation

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DeepCast

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Executive Summary

deepcast.ai 1K+

Crunchbase

Founded 2015 – Houston, United States

Funding

Estimated Funding

$100K+

Major Investors

National Science Foundation

Team (<5)

No team information available.

Company Description

Problem

Developing digital twins for product development traditionally requires extensive data, specialized coding skills, and significant time investment, creating a barrier for many engineers and scientists. The complexity and resource demands of conventional modeling processes can slow down experimentation and hinder operational efficiency.

Solution

DeepCast offers an AI-powered platform that simplifies the creation of digital twins, enabling engineers and scientists to build practical models with minimal data and no coding. The platform integrates with diverse data sources and tools, allowing users to gain real-time insights and accelerate product development. DeepCast's AI engine automatically builds a digital twin that explains the underlying process, providing a recommendation companion for lab and field applications. The resulting models can be accessed through a cloud-based or on-premise dashboard for data analysis, prediction assessment, and team collaboration.

Features

AI-driven engine for automated digital twin creation with minimal data requirements

No-code interface for model building, accessible to users without programming expertise

Real-time integration with various databases, files, and third-party APIs via SaaS data connectors

Cloud-based and on-premise dashboard for data analysis, model prediction, and team collaboration

Interpretable equation generation for understanding the drivers behind model predictions

Closed-loop steam flooding optimization for enhanced oil recovery

Corrosion modeling for rapid categorization of anti-corrosive materials

Target Audience

The primary target audience includes engineers, scientists, and product development teams across various industries seeking to streamline modeling processes, reduce experimentation time, and improve operational efficiency.