Stop feeding your LLMs stale, generic data. With Magic XPI, you can give any AI agent secure, real-time access to the data that actually runs your business, from your on-premise ERP to your custom-built applications. This is how you move from AI experiments to AI-powered operations.
You've seen the demos. You know AI can write emails, summarize articles, and answer trivia. But when you ask a question that really matters—“Which of our open jobs is the best fit for this candidate?” or “Summarize our Q3 sales performance from the latest SAP report”—it fails.
Why? Because the data it needs is locked away in systems it can't access. Your ERP, your MES, your custom databases, your shop-floor logs—these are the systems that hold the ground truth of your business. And they don't speak the language of modern AI.
Magic XPI bridges the gap between your most powerful AI tools and your most critical business systems. We've built a suite of AI capabilities that transform your existing infrastructure into an AI-ready powerhouse. No data migration, no risky custom code, just secure, scalable, and immediate access.
The Problem: You want to ask your AI, “Give me a summary of job #12345.” But your AI doesn't know what a “job” is, where to find it, or how to get the details. It's like giving a brilliant new employee a laptop but no password.
The Solution: With Magic XPI as an Enterprise MCP Server, you can turn any of your existing business processes into a secure, callable “tool” that any AI agent can use. You teach your AI what a “job” is and how to get details about it by linking it to an existing Magic XPI flow. That flow already knows how to talk to your HR system, your ERP, or wherever that data lives.
The Result: Suddenly, your AI is no longer just a chatbot. It's a member of your team. It can query SAP, search your candidate database, and pull real-time inventory from JD Edwards—all in response to a simple natural language prompt. You've given your AI a library of superpowers that are unique to your business.
Use Cases
Sales
“Find me three customers in the manufacturing sector who are due for a contract renewal this quarter.”
HR
“Give me a list of all employees who have completed their cybersecurity training.”
Operations
“What is the current inventory level for product SKU #ABC-123 in our main warehouse?”
Use Cases
Customer Service
“Automatically summarize long email chains from customers before creating a support ticket.”
Marketing
“Generate personalized email subject lines for a marketing campaign based on customer purchase history.”
E-commerce
“Translate product descriptions into multiple languages and post them to your Shopify store.”
The Problem: You have a workflow that pulls a candidate's resume from your app. Now you want to summarize that resume and generate a set of interview questions. The old way? A human has to read it, think about it, and write the questions. It's a manual bottleneck in an otherwise automated process.
The Solution: With our new LLM step, you can drag-and-drop the power of models like Claude, Gemini, and ChatGPT directly into any Magic XPI flow. Your flow collects the data, passes it to the LLM with a simple prompt, and gets a structured response back. The LLM becomes just another step in your automation—as reliable and repeatable as a database query.
The Result: You can automate cognitive tasks that were previously impossible. Summarize customer support tickets, translate product descriptions, generate personalized email copy, and analyze sentiment in survey responses—all as a native part of your existing business processes.
The Problem: You want to build a truly autonomous process—one that can think, reason, and act on its own. For example: “When a new high-priority job is posted, find the top three candidates in our database, write a personalized screening question for each one based on their resume, and send it to them via SMS.” This requires an AI that can not only access tools but also decide which tools to use and in what order.
The Solution: By combining our MCP server capabilities with our native LLM integration, you can build fully autonomous, LLM-driven workflows. You give the AI a goal and a toolbox of capabilities. The AI then figures out the rest—it calls the Job Details tool to understand the role, the Candidate Search tool to find people, and the Resume tool to get their background, then uses its own intelligence to write the perfect question.
Crucially, you can constrain the AI's output to a specific JSON schema. This means you get structured, predictable data back that you can reliably map to the next step in your program.
The Result: You are no longer just automating tasks. You are automating decisions. You can build AI agents that handle complex, multi-step processes that previously required a team of people. This is the future of automation, and it's available today.
Use Cases
Recruiting
“An AI agent that automatically screens new applicants and schedules interviews with top candidates.”
Procurement
“An AI agent that monitors inventory levels, automatically generates purchase orders when stock is low, and routes them for approval.”
IT Operations
“An AI agent that detects system anomalies, queries logs to diagnose the root cause, and creates a detailed ticket in your service desk with recommended actions.”
Talk to an expert today and learn how Magic XPI can turn your AI ambitions into reality.
