About SelfMachines

The startup offers a no-code platform that enables users to create customized AI agents tailored to specific business requirements, utilizing a proprietary hierarchical graph engine for graph visualization and orchestration. This technology allows businesses to develop machine learning models and workflows in days, significantly reducing the time and complexity typically associated with these processes.

```xml <problem> Developing custom AI models and workflows for specific business needs can be time-consuming and complex, often requiring specialized expertise and extensive coding. Off-the-shelf, cloud-based AI solutions may lack the flexibility to address unique business requirements, leading to suboptimal performance and limited customization options. </problem> <solution> SelfMachines provides a no-code AI development platform that enables users to create customized AI agents tailored to specific business requirements. The platform features a drag-and-drop interface and a proprietary hierarchical graph engine for both machine learning model and process orchestration. By abstracting the complexities of AI deployment and training, SelfMachines empowers users with varying skill levels to build, train, manage, and serve diverse information retrieval and processing pipelines. The platform's modular extensibility allows users to create and integrate their own customized module types, fostering a highly adaptable AI development environment. </solution> <features> - No-code, drag-and-drop interface for building AI models and workflows - Hierarchical graph engine for visualizing and orchestrating ML models and processes - Modular extensibility enabling users to create and integrate custom module types - Support for various layer types, including those found in TensorFlow and PyTorch (convolutions, multi-head attentions, RNNs, etc.) - Built-in graph module types, including transformer encoders and ResNet image feature extractors - Abstraction levels ranging from layers and blocks to stages, graphs, meta-graphs, agents, and systems - Job construct for executing and processing data using created modules </features> <target_audience> SelfMachines targets businesses across various industries seeking to develop customized AI solutions without extensive coding or specialized AI expertise. </target_audience> ```

What does SelfMachines do?

Where is SelfMachines located?

SelfMachines is based in San Mateo, Philippines.

When was SelfMachines founded?

SelfMachines was founded in 2023.

How much funding has SelfMachines raised?

SelfMachines has raised 100000.

Location

San Mateo, Philippines

Founded

2023

Funding

100000

Employees

1 employees

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SelfMachines

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Executive Summary

selfmachines.com 200+

Crunchbase

Founded 2023 – San Mateo, Philippines

Funding

Estimated Funding

$100K+

Team (<5)

No team information available.

Company Description

Problem

Developing custom AI models and workflows for specific business needs can be time-consuming and complex, often requiring specialized expertise and extensive coding. Off-the-shelf, cloud-based AI solutions may lack the flexibility to address unique business requirements, leading to suboptimal performance and limited customization options.

Solution

SelfMachines provides a no-code AI development platform that enables users to create customized AI agents tailored to specific business requirements. The platform features a drag-and-drop interface and a proprietary hierarchical graph engine for both machine learning model and process orchestration. By abstracting the complexities of AI deployment and training, SelfMachines empowers users with varying skill levels to build, train, manage, and serve diverse information retrieval and processing pipelines. The platform's modular extensibility allows users to create and integrate their own customized module types, fostering a highly adaptable AI development environment.

Features

No-code, drag-and-drop interface for building AI models and workflows

Hierarchical graph engine for visualizing and orchestrating ML models and processes

Modular extensibility enabling users to create and integrate custom module types

Support for various layer types, including those found in TensorFlow and PyTorch (convolutions, multi-head attentions, RNNs, etc.)

Built-in graph module types, including transformer encoders and ResNet image feature extractors

Abstraction levels ranging from layers and blocks to stages, graphs, meta-graphs, agents, and systems

Job construct for executing and processing data using created modules

Target Audience

SelfMachines targets businesses across various industries seeking to develop customized AI solutions without extensive coding or specialized AI expertise.