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AI Studio | Enterprise AI Agent Platform for Manufacturing

AI Studio · Manufacturing AI Agent Workspace

AI STUDIO
Build AI agents for manufacturing workflows.

Build and coordinate AI agents that use enterprise knowledge, tools, and defined workflows, with access controls and human review built in.

AI Studio is the AI agent workspace within Domain Twin™. Configure agents, knowledge bases, Skills, workflows, and MCP tools by role, while keeping access and activity within defined enterprise controls. External system connections require an available API or MCP server.

Request an AI Studio DemoSee How AI Studio Works
Quality issue reviewCONTROLLED
Agent workspace
Summarize the quality issues in this lot, identify likely causes, and propose the next checks.
I’ll verify the event, process conditions, and approved SOPs first, then flag the differences that need an engineer’s review.
Equipment historySOP 14-3Model output
Enter a task or question… ⌘ ↵
AI STUDIO WORKSPACE
Conversation, context, agent collaboration, and human reviewContext · Agents · Tools
Start with real workFrame manufacturing tasks in familiar language
Coordinate specialized agentsKnowledge, analysis, and review roles work together
Govern every stepPermissions, approvals, and traceable activity

AI Studio platform

What is AI Studio, and how does it support manufacturing teams?

AI Studio is Profet AI’s enterprise AI agent platform for manufacturing workflows. It gives teams a no-code workspace to build and coordinate specialized agents, ground them in approved enterprise knowledge, and connect available tools through MCP. Each workflow can define which data and tools an agent may use, which steps require human review, and what activity is recorded. Unlike a standalone chatbot, AI Studio organizes context, roles, tools, and approvals around real tasks such as quality issue review, maintenance preparation, and manufacturing knowledge reuse. It operates within the wider Domain Twin™ architecture, which connects enterprise data, models, knowledge, governance, and deployment context.

Agent workflow

How does a quality issue become the next action?

Turn familiar engineering work such as evidence gathering, cross-checking, analysis, and review into a repeatable agent workflow.

Define the problem first so the agent knows what work to do.

AI Studio first identifies the manufacturing context, task role, and completion criteria, then determines which data, models, documents, and collaborators are needed.

  • Input: quality issues, equipment alerts, work orders, or shop-floor questions
  • Available context: process conditions, SOPs, historical cases, and approved knowledge
  • Output: a clear task scope, data requirements, and collaboration plan

MCP × AI Studio

MCP gives agents a controlled path to external data and tools.

When an enterprise already has an accessible API or MCP server, AI Studio can configure it as an MCP tool for agents. What an agent can read or execute still depends on the system and permission settings.

01 / HOST

AI Studio workspace

Holds tasks, agents, knowledge, and tool configurations in the workspace where users perform the job.

02 / CLIENT

Controlled tool requests

Sends data or tool requests to configured MCP servers based on the agent setup and available permissions.

03 / SERVERS

Existing systems and data sources

If an enterprise system exposes a usable API or MCP server, it can be evaluated for connection to databases, documents, or work tools. MES and ERP integrations should be validated with Profet AI.

What must be in placeAI Studio does not automatically gain access to external systems. An accessible API or MCP server, authentication method, defined data scope, and permissions are required.Learn about MCP × AI Studio →

AI Studio workspace

See how multiple agents collaborate in one conversational workspace.

AI Studio starts with a conversation, then brings task context, agent roles, tools, and human review into the same workflow.

CURRENT TASKQuality issue reviewEvent, SOPs, equipment history, and model outputs

Agents do not sit side by side. They work together on one task.

An orchestrator agent defines the task, knowledge and analysis agents gather approved information and model outputs, and a review agent consolidates the result into an engineer-ready package.

Agent workspaceCONTROLLED MODE
Quality workflow
Summarize this lot’s quality issues and the next checks.
I’ll review the approved SOPs, equipment history, and model outputs, then organize the evidence for verification.
SOPEquipment historyReview package
Enter a task or question…
AI Studio starts with a conversation, then keeps context and agent collaboration in the same workspace.
WORK BOUNDARY

Models and agent outputs support engineering judgment. They do not replace engineer accountability for technical validity, risk, or final release.

Manufacturing workflows

Start with a real workflow, not a generic chatbot.

Choose a task with clear inputs, roles, and acceptance criteria, then reuse a validated agent workflow across more lines and functions.

Part of Domain Twin™

AI Studio is the agent workspace. Domain Twin™ is the enterprise AI brain behind it.

Domain Twin™ brings together enterprise data, models, knowledge, governance, and deployment context. AI Studio is the workspace where users build agents, attach knowledge and tools, design collaboration workflows, and manage daily interactions.

01 / AutoML

Models for predictive decisions

Turn structured manufacturing data into predictions, anomaly detection, and optimization outputs.

02 / AI STUDIO

Build and coordinate agents

Configure knowledge bases, Skills, workflows, and available MCP tools.

03 / DOMAIN TWIN

The enterprise AI brain

Connect data, models, knowledge, permissions, and deployment around the same manufacturing context.

Explore Domain Twin →

Continue exploring

Choose the next product based on your AI goal.

AI Studio focuses on agent building and collaboration. If your priority is predictive modeling or enterprise-wide AI integration, continue with the corresponding product.

Build predictive models

Need prediction, anomaly detection, or optimization models?

Start with structured manufacturing data and see how AutoML helps domain experts build, compare, and deploy machine-learning models.

Explore AutoMLConnect enterprise AI

Need to connect models, knowledge, agents, and governance?

See how Domain Twin™ connects enterprise data and AI capabilities into a governable operating context.

Explore Domain Twin
AI Studio

Details

    Start with one real workflow

    Start with one manufacturing workflow.

    Bring a quality, equipment, knowledge, or cross-system task. We’ll map the data, agent roles, MCP connections, and approval points needed to put it into practice.

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