Adept AI

Overview

Adept AI, founded in 2022, specializes in developing foundation models that enable natural language interaction with software. The company's core offering focuses on general intelligence for action, allowing users to articulate complex goals in plain language, which the AI then translates into actionable steps across various applications. This capability positions Adept AI's technology for automating multi-step workflows, assisting with data entry, navigating user interfaces, and generating content based on user prompts. The platform aims to reduce the need for manual scripting or direct API integrations for every task by providing an agentic layer that can operate within existing software environments.

The primary users of Adept AI's technology are developers and technical buyers within enterprises seeking to enhance productivity and streamline operations through AI-driven automation. Use cases include developing custom AI assistants that can perform tasks like drafting emails, populating CRM fields, or generating reports by interacting directly with software. Adept AI's models are designed to understand context and intent from natural language, allowing them to adapt to diverse enterprise workflows. This approach is distinct from traditional robotic process automation (RPA) in its emphasis on semantic understanding and dynamic task execution rather than predefined script-based actions. The technology is particularly suited for scenarios where human-computer collaboration can be augmented by an AI capable of understanding high-level instructions and executing them across multiple software tools.

Adept AI's research emphasizes training large language models (LLMs) to perform actions, not just generate text. This involves developing models that can interact with graphical user interfaces (GUIs) and application programming interfaces (APIs) to achieve user-specified objectives. The company's work builds on advancements in transformer architectures and reinforcement learning, aiming to create general-purpose agents that can learn from human feedback and adapt to new tasks. This aligns with broader industry trends toward more capable and autonomous AI agents, as discussed in research by organizations like OpenAI on agentic AI systems OpenAI Research. The platform shines in environments requiring flexible automation where tasks are varied, and direct API access may be limited or complex to integrate programmatically. It is less suited for highly specialized, narrow tasks that are already well-served by purpose-built automation tools with defined APIs, where the overhead of a general-purpose agent might not be justified.

Key features

• Natural Language Command Processing: Interprets complex, multi-step instructions provided in natural language to initiate and complete tasks across software applications.
• Cross-Application Workflow Automation: Executes actions across different software tools and platforms, such as web browsers, desktop applications, and enterprise systems, without requiring explicit API calls for every step.
• Custom AI Assistant Development: Provides tools and frameworks for developers to build tailored AI assistants that can perform specific functions relevant to their organizational needs.
• Contextual Understanding: Utilizes advanced foundation models to understand the context of user requests and the state of the software environment, enabling more accurate and relevant task execution.
• Human-in-the-Loop Capabilities: Designed to allow for human oversight and intervention, enabling users to guide or correct the AI's actions during complex workflows.
• Adaptability and Learning: Models are built to adapt to new tasks and learn from interactions, improving performance and accuracy over time with continued use and feedback.

Pricing

Adept AI offers custom enterprise pricing for its platform. Specific pricing details are not publicly disclosed and typically depend on factors such as deployment scope, usage volume, and required support levels. Prospective users are advised to contact Adept AI directly for a customized quote.

Pricing as of May 2026. For current and detailed pricing information, please refer to the official Adept AI contact page.

Common integrations

Adept AI's core capability is its ability to interact with various software applications through their user interfaces or underlying APIs. While explicit integration lists are not publicly detailed, the platform's design suggests broad compatibility with:

• Web Applications: Interaction with web-based CRM, ERP, project management, and productivity tools (e.g., Salesforce, Workday, Monday.com, Google Workspace).
• Desktop Applications: Automation of tasks within standard operating system applications and specialized desktop software.
• Enterprise Software Suites: Compatibility with various enterprise resource planning (ERP) and customer relationship management (CRM) systems.
• Productivity Tools: Integration with communication platforms, email clients, and document creation software.
• Custom Internal Tools: Adaptability to interact with bespoke internal company applications.

Alternatives

• Anthropic: Develops frontier AI models like Claude, focusing on safety and steerability, often used for text generation and complex reasoning tasks.
• OpenAI: Creator of models such as GPT and DALL-E, offering powerful capabilities for natural language processing, code generation, and image synthesis.
• Hugging Face: Provides an open-source platform for building, training, and deploying machine learning models, with a focus on NLP and a large community of developers.

Getting started

As Adept AI's platform is primarily focused on enterprise solutions and custom deployments, a public 'hello world' code example for direct API interaction is not available. The typical integration process involves working with Adept AI's team to define specific automation needs and configure the models for a given enterprise environment. However, a conceptual Python example illustrating how one might interact with an agentic system that processes natural language commands could look like this, assuming a hypothetical SDK:

# This is a conceptual example. Actual Adept AI SDK usage would vary.
# Adept AI typically involves enterprise-level integration and custom setup.

import adept_sdk_hypothetical as adept_sdk

def automate_marketing_task(agent: adept_sdk.Agent):
    command = (
        "Draft a marketing email for our new product launch. "
        "Subject: 'Introducing [Product Name] - Revolutionize Your Workflow'. "
        "Mention key benefits: increased efficiency, cost savings, and seamless integration. "
        "Include a call to action to visit our product page: [Link to Product Page]. "
        "Then, schedule it to be sent to the 'Product Launch' mailing list next Tuesday at 10 AM PST. "
        "Ensure the draft is reviewed by the marketing manager before sending."
    )
    try:
        # The agent processes the natural language command and executes actions
        # across various integrated applications (e.g., email client, CRM, calendar).
        result = agent.execute_command(command, user_context={"user_role": "marketing specialist"})
        print(f"Command initiated successfully: {result.status}")
        if result.actions_taken:
            print("Actions performed:")
            for action in result.actions_taken:
                print(f"- {action.description} in {action.application}")
        if result.pending_review:
            print(f"Awaiting review by: {result.pending_review_user}")
    except adept_sdk.AdeptAPIError as e:
        print(f"Error executing command: {e}")

# Hypothetical initialization of the Adept AI agent
# In a real scenario, this would involve authentication and configuration.
# agent = adept_sdk.initialize_agent(api_key="YOUR_ADEPT_API_KEY", environment="production")
# automate_marketing_task(agent)

print("Conceptual Adept AI interaction example complete.")
print("Please refer to Adept AI's official documentation and enterprise onboarding for actual usage.")

This example illustrates the conceptual interaction with an Adept AI agent. The execute_command method would represent the core function where natural language instructions are parsed and translated into actions across various applications. The result object would typically contain information about the executed actions, status, and any required human intervention.