Minds Platform

Minds Platform is an automation system and application platform designed for building and deploying custom AI tools and workflows. It functions as a machine learning integration layer and self-hosted orchestrator that connects predictive models and large language models to external data sources.

The platform enables the execution of multi-step tasks that read and write data to automate reports and operational activities. It supports deployment across cloud, on-premises, and virtual private cloud environments to maintain control over models and data.

Capabilities include event-driven workflow orchestration, adapter-based data integration, and a pluggable model interface. The system exposes machine learning capabilities through standard database queries and uses metadata-driven configuration to allow the creation of AI tools without writing core code.

Features

AI Automation Workflows - Functions as an automation engine that executes multi-step tasks by reading and writing data between AI models and external sources.

AI Model Integrations - Enables the connection of predictive models to data sources to create automated AI-driven workflows.

No-Code AI Tool Builders - Provides capabilities to build AI-driven applications and analyses without requiring deep engineering expertise.

Event-Driven AI Workflows - Triggers multi-step AI tasks based on data changes or external signals to automate operational activities.

LLM Application Platforms - Provides an integrated platform for building and deploying custom AI tools and automated workflows using LLMs.

Model Integration Layers - Implements a programmatic integration layer that abstracts and unifies access to various machine learning models.

Private AI Infrastructure - Provides infrastructure for hosting and managing machine learning models and workflows on private servers.

Self-Hosted AI Platforms - Provides a self-hosted platform for deploying and managing AI systems across cloud and on-premises environments.

SQL-Based Machine Learning - Exposes machine learning model capabilities through standard SQL queries for simplified data analysis.

Multi-Source Data Integration - Standardises connections to diverse data sources using a common interface for AI model read and write operations.

Multi-Stage Workflow Automations - Executes complex multi-step tasks that read and write data for report generation and system monitoring.

Data-Driven Predictive Workflows - Connects machine learning models directly to data sources to automate analytics and create data-driven workflows.

Model Interfaces - Decouples the platform from specific machine learning models via a unified abstraction layer.

Hybrid Deployment Configurations - Supports flexible installation across cloud and on-premises infrastructure using isolated containers.

Metadata-Driven Frameworks - Uses structured metadata to define AI application settings and tool logic without requiring core code changes.

Machine Learning - An open source AI layer for existing databases using standard queries.

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Features

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