--- title: Maia by Make system card slug: maia-by-make-system-card description: Learn how Maia by Make handles data, uses AI, and maintains security and compliance. image: https://archbee-image-uploads.s3.amazonaws.com/oAyFj2GHlBeBVWF5OAir2/VFOu3mt2KPGOvFFVsISlj_maia-help-center.jpg docTags: createdAt: 2026-05-13T10:07:59.944Z --- :::hint{type="info"} Maia is available in **public beta for all paid plans**. On Free plans, she's available for a 30-day trial starting from signup. ::: This system card covers Maia's functionality, data usage, human intervention points, and data security. ## Overview Maia is an AI and automation co-worker in the Scenario Builder. She helps create, modify, and debug scenarios through natural language. ## AI integration Maia runs on a large language model (LLM). She interprets your natural language prompts, reasons about the current state of the scenario, and calls a set of predefined tools to modify the scenario. She autonomously chooses and orders tools in a single user-prompted session. ## User interactions You interact with Maia through a conversational chat interface in the Scenario Builder. Describe what you need, for example, "add a filter that only passes emails from my domain," and Maia applies the changes step by step on the canvas while explaining its actions. ## Use of AI models Maia relies on the following models: - [GPT-4.1 mini](https://developers.openai.com/api/docs/models/gpt-4.1-mini) - [GPT-5.2](https://deploymentsafety.openai.com/gpt-5-2/introduction) - [GPT-5.6](https://deploymentsafety.openai.com/gpt-5-6-preview) ### Model training on customer data Make doesn't use customer data, such as user prompts, scenario structures, or outputs, to train or fine-tune the underlying LLM. Adjustments in model behavior come exclusively from system prompt engineering and tooling configuration. No customer data modifies the model weights. ### Impact of model output The LLM decides how to respond to each user request, including the tools to call, their order, and parameters. The output determines the scenario configuration, whose quality depends on the LLM's reasoning ability and the accuracy of its knowledge. ## Data flow ### Inputs Maia uses these inputs in each session: - The natural language prompt you enter in the chat interface - The current state of the scenario, including the module graph, connections, and settings - Details about your Make account, including permissions - Make-specific knowledge (prompts and skills) - The scenario history and logs ### Processing Maia processes your requests in these steps: 1. Once Maia receives the prompt and scenario context, she plans her response. 2. Maia selects predefined tools, for example, add module, set filter, connect modules, and calls each one at a time. 3. Each tool action appears on the canvas in real time. 4. Maia lists tool calls in the chat interface. ### Outputs Maia produces the following outputs: - **Scenario modifications:** Modules (added or removed) and field configurations - **Contextual information**: Natural language explanations of modifications ## Human oversight and control ### Level of automation Maia only acts when you explicitly prompt her. While she may call multiple tools sequentially without requesting your approval for each, the outcome of these actions is visible on the canvas. ### Human intervention You can intervene in Maia's behavior in the following ways: - **Revert:** Undo any change that Maia makes by clicking a previous scenario version in the chat. - **Validation and testing:** You must check the scenario configuration before running or activating it. - **Restricted actions:** Maia is unable to take high-impact actions, such as running, activating, or deactivating scenarios. She asks you to perform these actions yourself. ### Monitoring and evaluation Make monitors and evaluates Maia's performance by: - Tracking live activity in Maia (Maia's actions and user conversations) to detect errors, false outputs, and unintended changes to scenarios - Investigating user questions and generated responses - Developing an LLM evaluation framework to standardize Maia's scenario building ### Opting in and out By default, Maia is available in the Scenario Builder for all users. Enterprise users can opt out in their account settings. ## Safety and security ### Data security - **User prompts and scenario data: **This data stays in Make. Exceptions include calls to the AI provider, which are subject to Make's data processing agreement with that provider. - **Data access: **Maia operates strictly within your defined access permissions. She does not access resources or data that are inaccessible to you. - **Data encryption:** Your data is encrypted in transit and at rest, following Make's platform-wide security standards. ### Personal data (PII) handling The AI provider applies safeguards that reject prompts requesting personal data. Maia doesn't actively seek personal data. She only processes data that you explicitly include in your prompt, or that is present in the scenario you're building. ### handling Maia never has access to your connections or keys. She uses existing capabilities in Make to connect to third-party services, and doesn’t see the data stored in them. When credentials are required, Maia prompts you to select an existing connection or create a new one from a connection card in the chat. You enter new credentials in a dialog outside of the chat. Never paste keys, passwords, or tokens into the chat, where Maia can see them. ### System reliability Maia's availability depends on Make platform uptime and AI provider availability. Uptime targets align with Make's standard platform SLA. ## Ethical considerations ### Transparency Maia never indicates that you're interacting with a human agent. All of her outputs come with the following mandatory disclosure statement: "Output is generated by AI, please verify as errors may occur." Maia's step-by-step narration of the changes she makes to the canvas provides live insight into her decision-making. More visibility into her reasoning is unavailable (a known limitation of current LLMs). ### Fairness and bias Maia's purpose is to build scenarios rather than evaluate. Her AI provider applies safety alignment frameworks to mitigate model-level biases. As the model never uses customer data for training, it does not factor in organizational biases. ### Potential adverse effects Maia may produce scenarios that include bugs or logical errors, such as incorrect data transformation or unintended API calls. Mitigations include: - Revert to a previous scenario version to undo Maia-generated changes. - Run and activate scenarios yourself (Maia is unable to run scenarios). - Test scenarios before activating them.