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What is Major?

Major is a comprehensive online platform that facilitates the design, implementation, and oversight of internal business applications, AI agents, skills, and workflows by leveraging organizational data. By using natural language, users can describe a desired tool or process, prompting Major's AI agent to create an app within a sandbox environment for both preview and deployment. Teams are empowered to connect various databases, business applications, and APIs, allowing them to develop dashboards, administrative panels, scheduled automations, and intricate multi-step workflows that include branches, approvals, and human involvement. The applications deployed on the platform maintain continuous data availability and can be effortlessly shared among team members, while AI agents can be accessed through the web app, Slack, or according to a predetermined schedule. Furthermore, Major offers integration with Claude and features a command-line interface, including source-code management through Git repositories. For organizations desiring advanced functionalities, enterprise plans provide features such as single sign-on (SSO), SCIM provisioning, and options for self-hosting, enabling businesses to customize the platform to suit their unique requirements. This level of flexibility and customization underscores Major's potential as an essential resource in contemporary business settings, greatly enhancing operational efficiency and collaboration.

What is Dynamiq?

Dynamiq is an all-in-one platform designed specifically for engineers and data scientists, allowing them to build, launch, assess, monitor, and enhance Large Language Models tailored for diverse enterprise needs. Key features include: 🛠️ Workflows: Leverage a low-code environment to create GenAI workflows that efficiently optimize large-scale operations. 🧠 Knowledge & RAG: Construct custom RAG knowledge bases and rapidly deploy vector databases for enhanced information retrieval. 🤖 Agents Ops: Create specialized LLM agents that can tackle complex tasks while integrating seamlessly with your internal APIs. 📈 Observability: Monitor all interactions and perform thorough assessments of LLM performance and quality. 🦺 Guardrails: Guarantee reliable and accurate LLM outputs through established validators, sensitive data detection, and protective measures against data vulnerabilities. 📻 Fine-tuning: Adjust proprietary LLM models to meet the particular requirements and preferences of your organization. With these capabilities, Dynamiq not only enhances productivity but also encourages innovation by enabling users to fully leverage the advantages of language models.

Media

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Media

No images available

Integrations Supported

Claude
GitHub
HubSpot CRM
Jira
Model Context Protocol (MCP)
Slack
Stripe

Integrations Supported

Weaviate

API Availability

Has API

API Availability

Pricing Information

$100/month
Team is $100/month billed annually and includes $120 in monthly shared credits for up to 15 builders and 50 viewers. New accounts receive $100 in free credits. Enterprise pricing is custom and includes self-hosting options.

Pricing Information

$125/month

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Major

Date Founded

2024

Company Location

United States

Company Website

www.major.build/

Company Facts

Organization Name

Dynamiq

Date Founded

2024

Company Location

United States

Company Website

www.getdynamiq.ai/

Categories and Features

AI Agent Builders

Not specified

AI App Generators

Not specified

AI Automation

Not specified

Categories and Features

Agentic AI

Not specified

AI Agent Builders

Not specified

AI Agents

Not specified

AI Automation

Not specified

AI Development

Not specified

AI Fine-Tuning

Not specified

AI Guardrails

Not specified

AI Observability

Not specified

AI Tools

Not specified

Task Automation

Not specified

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