The AI Hub is where you find, configure, and manage all of Tulip's AI capabilities in one place. Composable, grounded in your operational data, governed by the same enterprise controls you already trust.
Give process engineers drag-and-drop AI building blocks to assemble intelligent workflows without writing code, turning domain expertise into production-ready applications in hours.
Model Context Protocol — let AI models like Claude directly interact with Tulip data models and APIs.
Describe a production process. AI suggests step sequences, routing logic, and data points — ready to deploy.
Upload a PDF, video, or text description. Get back a working Tulip app with tables, logic, and workflow built in.
Track AI initiatives across your environment. See what's deployed, in progress, and where AI is driving impact.
Demo coming soonFrom idea to deployment — a step-by-step path for engineers new to Tulip AI.
Start with a specific process pain point: an inspection, changeover, or escalation flow.
AI Composer for apps, Ops Composer for process logic, or Skills for reusable capabilities.
Generate your first version, test with the validation runner, iterate with AI feedback.
Deploy to your station, publish to the Library, share the business case with your team.
Guides you through designing the right Tulip solution architecture for your use case — step by step.
Designs optimal table schemas and data relationships for your operational processes in Tulip.
Searches the Tulip Library to find the best-matching apps, agents, and templates for your use case.
Helps engineers write, debug, and optimize Tulip expressions and connector functions without trial and error.
Reviews your Tulip app for deployment readiness — checking logic, data flows, and edge cases before go-live.
Creates realistic test datasets for your Tulip tables and apps to accelerate validation and QA cycles.
Empower operators with AI assistants that surface real-time guidance, catch defects, and eliminate manual data entry directly at the point of work.
Agents monitor conditions, make decisions, and take action across apps, tables, and workflows — without waiting for manual input. Not one agent — a full roster, each built for a specific operational task.
Step-by-step university course walking through agent architecture, triggers, and deployment in Tulip.
Take the course →Official KB walkthrough — prerequisites, first agent setup, and common patterns for production environments.
Read article →Deep dive into how agents listen for conditions, make decisions, and take action across Tulip apps, tables, and APIs.
Read article →Tulip+ and the connector ecosystem bring external AI models, automation platforms, and industrial data systems directly into your operational workflows.
Connect Tulip's real-time operational data with Snowflake Cortex AI — enabling predictive quality, cross-site analytics, and AI-driven decision-making at the factory level.
Reusable AI capabilities that make Claude Tulip-aware — giving it deep knowledge of your apps, tables, connectors, and manufacturing processes. Load, fork, or contribute.
Gives Claude full context about Tulip's AI Composer — enabling it to generate apps, suggest widget layouts, and guide users through the build process step by step.
View on GitHub →Equips Claude with schema patterns for Tulip tables and data models — so it can scaffold new models, validate field types, and suggest relational structures for your use case.
View on GitHub →Community nodes for Tulip — connect Tulip to hundreds of apps and automate workflows without writing custom code.
View on GitHub →HTTP connector, webhooks & REST API — bring any external system, data source, or AI model directly into your Tulip workflows.
Explore Connectors →7-part series on applying AI in manufacturing. From basics to agentic AI.
Equip manufacturing leaders with AI-driven analytics that continuously identify bottlenecks, predict failures, and recommend process improvements across the entire operation.
AI-reconstructed timeline of every production event — operator actions, defect moments, and line stoppages — so you can diagnose root causes without reconstructing from memory.
Automatically generates quality reports from production data, surfacing defect trends and corrective action recommendations.
Compiles shift-end performance summaries from production data — automatically highlighting targets, deviations, and key events.
Optimizes work order routing and station assignments in real time based on capacity, priority, and operator availability.