Visualize Your PLM Data with Aras InnovatorEdge and Power BI
One of the most common questions we hear from customers is how to expose specific portions of PLM data to external services and tools using Aras InnovatorEdge. InnovatorEdge lets you use your favorite programs and tools with all of the necessary context that you define. Power BI allows you to create and share powerful reports with live data from Aras Innovator®. This blog assumes that you know the basics of InnovatorEdge, and how to create and work with custom APIs. If not, please read our previous blogs before jumping into this! Setup We’re going to skip the steps for creating your API. In this example, I’ll be using a very simple API that includes Parts, Part BOM Relationships, and related Documents. Here’s what the end result will look like: We’ll also have an API key configured and saved for use in Power BI. With those things configured in Edge, let’s jump into Power BI. Power BI To use your Aras data in Power BI, we’ll need to create some data references. Each data reference will map to a query through Edge. So, you’ll build a table based on Parts, for example, and a separate one for documents. Here are the steps for creating individual tables. Open Power BI and generate a new report In the Get Data tab, select web source: Click advanced and input your Edge URL for a specific query. That should look like this: https://edge.aras.cloud/ams/api/{YOUR_INSTANCE}/{YOUR_API}/{ITEMTYPE/QUERY} Under HTTP Request header parameters, add a header titled Authorization. The value should be apikey {YOUR_API_KEY} You should now see all the data your request serviced: Repeat the previous steps for any other requests you’d like to make. For the example document, I’ll be adding Documents, Part BOM, and Part Document. Connecting the data Once you have created your tables, you can then press the Model View button: to open up the following view: Now all you have to do is click the Manage Relationships button in the toolbar, and you’ll see the following dialog. From here, you can manually create the connections between your data. Make sure you create connections between relationships, as well as any Item Properties you’ve included. Once you’ve created your connections, your model view should look something like this: To include your InnovatorEdge data in a visual, open the Report View, and on the right-hand side, you should see the following pane: This allows you to select the type of visual you’d like to include and the data you want displayed. In this instance, I've created a simple table. Conclusion From this point on, you can use your live Innovator data to create powerful, useful visuals. This is just one example of how you can use an InnovatorEdge API to enable external tools. Let us know if there are other use cases you’d like to see on our blog! Feel free to leave suggestions in the comments below or use the community forums to ask questions or share results!
Work Smarter & Safer with AI: Connecting Aras Innovator and Claude MCP
Have you ever thought, "It would be great if AI could look up this data in Aras Innovator," or "I wish there was an easier way to generate and load realistic test data for my project"? You’re in luck! We’re going to walk through how you can use a Model Context Protocol (MCP) server with Aras Innovator to streamline everyday tasks for developers and users. By the end of this blog, you’ll be able to give your AI assistant authenticated access to query your PLM data, inspect your schema, and create items through natural language commands. A Community Collaboration This blog post was inspired by community member Daan Theoden and his work integrating Claude Desktop and Aras Innovator. You can check out his videos about AI on LinkedIn and see the source code of his original project on GitHub. The project was later forked and extended by Aras Labs to support both Configurable Web Services (CWS) and Aras InnovatorEdge. This collaboration is an excellent example of how the Aras community drives innovation, and we appreciate Daan laying the groundwork for this tool. What is the MCP? The MCP is an open standard that defines how AI models interact with external data and systems. Without MCP, integrations need to be hard-coded for each individual AI application. With MCP, you can build a standardized server once. This server advertises a set of tools – such as "Get Item" or "Create Item" – and any MCP-compliant client can discover and utilize them. This provides a secure, predictable, and auditable way to expose your PLM environment to an AI assistant without exposing direct database access or unstructured APIs. What this project does This is a lightweight Python server that acts as a bridge to your Aras instance – whether you are using Configurable Web Services (CWS) or the new Aras InnovatorEdge. It handles the necessary plumbing of authentication, URL normalization, and error handling so the AI model can focus on executing your intent. Out of the box, it enables your assistant to: Explore your schema: Fetches OData $metadata to dynamically learn your specific ItemTypes and property definitions. Auto-resolve names: Uses fuzzy matching to resolve terms like "Problem Report" or "Doc" to their actual ItemTypes (PR or Document). Generate data: Automates the creation of complex test data sets. Call methods: Triggers server-side logic by calling Aras methods directly. Quick Start: Connecting to the Aras Innovator REST API This tool is designed to work natively with Aras InnovatorEdge, but it includes full backward compatibility for the standard Aras Innovator REST API via CWS. If you choose to use CWS, please see our previous blog on how to set it up! Here is how to get it running 1. Clone the repository Clone the repo and install the dependencies. We recommend using a virtual environment. git clone https://github.com/ArasLabs/aras-claude-agent.git cd aras-claude-agent pip install -r requirements.txt 2. Configure the environment Create a .env file in the root directory. This is where you will define how the tool connects to Aras. Option A: Connecting via Aras InnovatorEdge (Beta) Aras InnovatorEdge is currently in beta. If you are an early adopter with access to an Edge environment, this is the preferred setup. # --- Backend Setup --- # Use 'EDGE' for InnovatorEdge instances API_BACKEND=EDGE # --- Auth Credentials --- AUTH_MODE=API_KEY EDGE_API_KEY=your_edge_api_key_here # Your Innovator URL API_URL=https://myserver.com/InnovatorServer # --- Optional Configuration --- # Map friendly names to ItemTypes for better recognition ITEMTYPE_ALIASES=Problem:PR;Doc:Document;Part:Part Option B: Connecting via Configurable Web Services If you do not have access to the Edge Beta yet, you can use standard Configurable Web Services (CWS). Note: Because this tool uses an "Edge-first" design, it uses the same authentication pattern for both backends. You will see InnovatorEdge terminology in the configuration variables (specifically EDGE_API_KEY). Don't worry, this works perfectly with CWS. # --- Backend Setup --- # Use 'ARAS' for standard CWS instances API_BACKEND=ARAS # Your Innovator URL (The server handles the /Server/Odata path automatically) API_URL=https://myserver.com/InnovatorServer # --- Auth Credentials --- AUTH_MODE=API_KEY # NOTE: Even though we are connecting to CWS, we use the EDGE_API_KEY # variable name to maintain compatibility with the tool's internal auth logic. EDGE_API_KEY=your_cws_api_key_here # --- Optional Configuration --- ITEMTYPE_ALIASES=Problem:PR;Doc:Document;Part:Part 3. Verify the installation You generally do not need to run the server manually. Claude Desktop will handle that for you. However, it is good practice to run it once just to ensure your environment is set up correctly and there are no missing dependencies. python -m src.server If the process starts and waits silently (or "hangs"), it is working correctly. The server is simply listening for commands over standard input. You can stop it with Ctrl+C and move to the next step. 4. Add to Claude Desktop Open your Claude Desktop configuration file. Mac: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Add the following entry (ensure you update the path to your cloned repository): { "mcpServers": { "aras-api": { "command": "python", "args": ["-m", "src.server"], "cwd": "/absolute/path/to/aras-claude-agent" } } } Once you save this file and restart Claude Desktop, it will automatically launch the Python server in the background. In Practice: Generating Test Data One of the most powerful applications of this tool is accelerating test preparation. Because the AI can read the OData metadata, it understands your specific data model without requiring detailed instructions. For example, you can use a prompt like this: "I need to test the Part Form with a new Robotic Arm assembly. Create 10 Parts for me. Number them ROBOT-001 through ROBOT-010. For the name, generate realistic component names." Behind the scenes, the Agent will: Call api_resolve_itemtype to confirm "Part" matches the correct ItemType. Call api_get_metadata to verify the property names for item_number and classification. Execute a loop of api_create_item calls to generate the records, using its own creativity to fill in the names. This automation dramatically reduces the time spent on repetitive data setup. Want to learn more about InnovatorEdge? We're preparing a series of technical blogs about what's new and what you can do with it, but in the meantime, you can check out this overview from product manager Benjamin Chaillon, or this article on AI data governance with Aras InnovatorEdge. Join the Conversation This project is just the beginning of what’s possible when we combine the structure of PLM with the flexibility of Large Language Models. By bridging Aras Innovator with tools like Claude, we can reduce the friction of context switching and make data creation and inspection significantly faster. A special thanks again to Daan Theoden for pioneering this integration and sharing it with the community. Open collaboration like this is what makes the Aras ecosystem so powerful, and we are thrilled to support and extend his original vision. Now, we want to hear from you. If you could automate one mundane PLM task with an AI assistant today, what would it be? Let us know in the comments below, or head over to the GitHub repository to star the project, file an issue, or contribute your own improvements!
Say Goodbye to DLL Headaches with the Aras IOM SDK on NuGet
If you’ve ever built a .NET application that connects to Aras Innovator®, chances are you’ve worked with the Aras IOM (Innovator Object Model) SDK. It's a powerful tool to have in your back pocket - packed full of handy classes and functions to streamline development. You just download the IOM SDK for your version of Aras Innovator and then reference the local copy in a Visual Studio project. Pretty easy, right? It was easy... until you wanted to build an integration that supported multiple versions of Aras Innovator. Or you shared a project with a colleague who saved their local IOM.dll in a different location. Or that colleague's version of the SDK didn't match yours. Sound familiar? Then I have good news for you! Aras published the IOM SDK as a NuGet package, making it easier than ever to integrate with Aras Innovator in a modern development environment. Meet the Aras IOM SDK The Aras IOM SDK is a .NET client library that lets you connect to Aras Innovator and interact with the platform using a high-level API. It abstracts the details of SOAP and simplifies tasks like authentication, querying, and item manipulation. If you're more familiar with the Aras REST API, you might wonder which you should use in your projects. The IOM SDK and the REST API each offer unique advantages. Choosing between them depends on your application's specific requirements and context. You might use the IOM SDK if: In contrast, the Aras REST API may be a better fit when: You're building a custom integration or automation tool in .NET. You're building lightweight or cross-platform applications. You want synchronous or stateful behavior that REST doesn't offer. You prefer a stateless, standard HTTP interface. You're working in a Windows-native context and prefer a .NET-first approach. You need easier integration with non-.NET tech stacks. Your app should send and receive XML. Your app should send and receive JSON. Both the standalone IOM SDK on NuGet and the Aras REST API are suitable for connectors and applications that need to support multiple versions of the Aras Innovator platform. Curious about how Aras InnovatorEdge APIs stack up? You get all the benefits of the standard Aras REST API plus API key authentication and a low-code editor to publish your own custom versionable APIs. Add the SDK to your Visual Studio project To get started, all you need is Visual Studio and a .NET project. Here’s how to add the SDK from NuGet: Open your solution in Visual Studio. In Solution Explorer, right-click your project and select Manage NuGet Packages for Solution. In the Browse tab, search for Aras.IOM Select the package published by Aras Corporation and choose Install. Accept any license prompts. That's it! No more copying DLLs by hand. You can use the package manager to check for updates and install the latest Aras.IOM release in your project. Updating a project that uses a local IOM.dll If you’re upgrading an existing project that has a hardcoded IOM.dll reference, here's how to switch over to the NuGet package: Remove the old reference: In Solution Explorer, right-click the Iom.dll reference and select Remove. Add the NuGet package: Follow the same steps above to install Aras.IOM from NuGet. Rebuild your project: The namespace and classes remain the same, so no code changes should be required. Takeaways Switching your .NET projects to the Aras IOM SDK package on NuGet is an easy way to simplify resource management and improve compatibility across Aras Innovator releases. Want to play with the SDK in the sample app shown in this post? Check out the aras-innovator-cli repo on the Aras Labs GitHub.What's New in Release 35?
Available since May, Release 35 introduces a significant update to the Aras Innovator® user interface, delivering a modern and streamlined look and feel throughout the platform. These changes aim to improve clarity while keeping the layout similar enough to be familiar. In addition to the UI changes, we'll go over a handful of nice-to-have features in this blog. New UI features As you can see, the platform's general look has shifted slightly, but not enough that you’d be unable to navigate as expected. As seen below, things like forms and search grids also got a facelift. Additionally, new helpful messages appear when there is a lack of data in the system. Now it’s instantly clear why a grid might be blank: For example, the above grid is empty because the Manufacturer Parts relationship has no data in that relationship, while the below icon is shown before you initiate a search in the main grid. Theme customization New in R35 is the ability to theme your instance of Aras Innovator. New properties in the Global Presentation Configuration allow you to set your instance's primary, secondary, and branding colors. This will change the default color of things like buttons, grids, and some client theming. Pick related type-ahead When adding an existing item as a relationship, you can search directly for the item you want with an embedded smart type-ahead search box. If you know what you're looking for, this will save you the effort of going through an additional modal search dialog. Rules-Based Grid Display This release introduces a no-code interface that empowers administrators to define rules for dynamically formatting data within tabular grids. Now, you can easily color-code critical information based on specific conditions. Highlight lifecycle state values, flag numerical data exceeding defined thresholds, or apply sophisticated styling based on complex combinations of properties – all without writing a single line of code. In the toolbar of an ItemType, there is a new Manage Rules button, which will open a dialog for setting rules for individual properties. Here’s an example: This quick example, which is provided out of the box, will highlight your Lifecycle State in green when it matches the condition provided. This works very similarly to our responsive forms rule editor and can be utilized for some very powerful, low-effort improvements to your team’s effectiveness. Here’s what the result looks like in the search grid. The formatting of released parts is immediately noticeable. This isn’t limited to the state of the item; any property could be used to have some conditional formatting. Takeaways Those are the major improvements in Aras Innovator Release 35. It’s important to note that we also just made the open release 2025 available. This will allow you to download the newest version for free and test it out. What’s your favorite addition? I’m excited to see how people utilize the new rules-based grid customization. Feel free to use the comments below to share interesting ways of using it!A Complete Guide to the 2025 Aras Community Edition
If you're new to Aras Innovator® or just curious about the latest Community Edition, here’s some good news: the 2025 release is available and packed with the latest features to explore. Whether you're a developer, low-code builder, or systems thinker, this edition is your invitation to dive into a powerful, composable PLM platform at no cost. Why choose the Community Edition? The 2025 Community Edition gives you access to a full-featured PLM platform without any licensing fees. It’s ideal for: Exploring Aras architecture and tooling Prototyping digital thread strategies Learning low-code app development on an enterprise-grade platform Demonstrating value to stakeholders Bootstrapping PLM processes for small organizations You'll get access to core applications like Product Engineering right out of the box, with optional modules including Variant Management, Requirements Engineering, Simulation Management, and more - all designed to help you build traceable, connected solutions. Wondering how the Community Edition compares to a full subscription? This side-by-side breakdown can help you decide if you'll need additional support or features as your project grows. How to try Aras Innovator without installing Want to see Aras in action before installing? Try one of these: Take the interactive product tour to explore digital thread use cases in your browser Request a demo to see how Aras Innovator fits your team’s goals Watch an on-demand webinar to dive deeper into use cases, platform capabilities, and best practices Ready to install? Here's how to get started Here's what you need to know if you're ready to install the Aras Innovator 2025 Community Edition. 1. Check your system requirements The first step is to ensure that your systems are up to the task of running Aras Innovator. Don't worry if that sounds intimidating. While it's important to carefully consider production system specs, an average Windows 10/11 laptop or Windows Server VM should be sufficient to help you evaluate the platform or start building solutions. Client Requirements Server Requirements Windows 10/11 or macOS 15 Windows Server 2012 or newer Chrome (v135+), Firefox ESR (115/128), or Edge .NET 4.7.2 and ASP.NET Core 8.0.1 2.0 GHz processor, 2 GB RAM, 80 MB free disk space SQL Server 2016 or newer 2.0 GHz processor, 32 GB RAM recommended, 1 GB disk space Check out the Installation Guide (PDF) for more details. 2. Install prerequisites Before running the installer, make sure these components are installed and configured: .NET Framework 4.7.2: Download from Microsoft's .NET site ASP.NET Core Runtime & Hosting Bundle 8.0.1: Available at dotnet.microsoft.com SQL Server: Use SQL Server 2016 or newer. You can install SQL Server Express for development purposes if you don't have a full SQL Server license. Enable mixed-mode authentication and remember your admin credentials IIS (Internet Information Services): Enable via "Turn Windows features on or off" on the server. Ensure that Application Development Features (e.g., .NET Extensibility, ASP.NET, CGI) are also enabled Microsoft Visual C++ Redistributable: Install versions for 2015, 2017, and 2019. These are often bundled together in the latest Microsoft Visual C++ Redistributable Tip: After installing all prerequisites, restarting your machine can help ensure everything is properly registered and ready before launching the Aras Innovator installer. 3. Download the installer Visit the Download page to download the installation files. Be sure to save the .msi file—you'll need it for reinstallation or repair. 4. Obtain your license key You'll need your server's MAC address to generate a Framework License key: Run ipconfig /all in Command Prompt to find your MAC address. Visit the license request page and request a key using that address. 5. Run the installer Double-click the InnovatorSetup.msi file to launch the installation wizard. Here's a step-by-step guide: Welcome Screen: Click Next to begin License Agreement: Read the agreement, accept the terms, and click Next System Prerequisites: Confirm you have: Admin access to the server SQL Server installed and accessible A valid Aras license key. Select all checkboxes to enable the Next button Destination Folder: Choose the install path (default is C:Program Files (x86)ArasInnovator ) and click NextAdmin access to the serverSQL Server installed and accessible Setup Type: Choose Complete for a full install Choose Custom to configure features like Vault Server, OAuth, and more Custom Setup (if selected): Select or deselect optional components License Configuration: Enter your license key Server Web Alias: Define the alias used in the URL (e.g., http://localhost/InnovatorServer ) SMTP and Language Settings: Configure as needed or use defaults Database Configuration: Specify your SQL Server instance Set admin credentials Choose to create a new database or connect to an existing one Begin Installation: Review your settings and click Install Installation Progress: Wait for the wizard to complete the process Finish: Click Finish to exit the installer A detailed guide with screenshots is also available in the Installation Guide. 6. Log in and explore Once installation is complete, open a browser and go to http://localhost/<your_web_alias>; . Log in with the default admin credentials and start exploring. Need help or inspiration? If you have trouble getting started with the Community Edition, you can check out the documentation or visit the Aras Community Forums with your questions. They're a great resource for troubleshooting issues, learning how companies are using Aras to solve problems, and connecting with fellow community members. I hope we'll see you there!Meet the Builders Behind the ACE 2025 Innovation Challenge
Every year, I leave ACE inspired by the Aras community's enthusiasm for problem-solving and openness to sharing solutions. This year's event was one that will stand out in my memory for a long time though. We had a mind-blowing keynote from CERN (yes, that CERN), an amazing turnout for the Tech Summit with six community demos, and the first-ever Aras Innovation Challenge on the main stage! Inspired by hackathons and the traditional "rapid-fire demos" at ACE, the Innovation Challenge provided community members an opportunity to show off the creative and impactful solutions they're building on the Aras Innovator® platform. Participants submitted projects in one of three topic areas: real-time shop floor data, digital thread, or sustainability. And of course, no competition would be complete without some stakes. Participants competed for a speaking slot on the main stage in Boston, complimentary ACE registrations, a joint workshop with Aras, and glory (that is, to get their name etched on the Innovation Challenge hammer, the "Stanley Cup" of the Aras Community). Meet the finalists Four finalists presented their solutions to a bustling crowd of more than 250 attendees at the end of the first conference day. Yogesh & Vilas AI Advisor for Engineering Design Yogesh Kulkarni and Vilas Sarangdhar from TCS presented an AI-powered solution that facilitates the import of unstructured data into Aras Innovator. The tool processes engineering drawings, extracts data characteristics, and creates corresponding item structures. Tom Smith Enhancing Shop Floor Feedback with Aras & LLMs Tom from SMC developed a solution to collect and analyze shop floor feedback. The solution uses AI to categorize and summarize input to help teams consume and respond to feedback. Konrad Golińczak Document Comparison Tool At Demant, Konrad built a solution that compares versions of Word documents in Aras Innovator. The tool uses AI to make intelligent comparisons and summarize the differences, helping users quickly understand changes. Gilbert Delabrousse Aras Lite MES: Streamlined Shop Floor Management Gilbert at Inensia shared a lightweight manufacturing execution system (MES) built on Aras Innovator. The solution helps operators follow and log work instructions while managers monitor assembly line status in real time. And the winner is... After each finalist made the case for their solution on the main stage, the audience voted for their favorite solution. The next day our CEO, Roque Martin, announced the community's choice at the "Aras in the Round" session. Congrats to Tom Smith, the first winner of the Aras Innovation Challenge! Missed the session or want to learn more about Tom's solution? I was curious about how he chose the topic and how he implemented it, so I followed up with him after the event to chat about it. Why shop floor data? Tom's a senior integration engineer at SMC, one of the largest pneumatics manufacturers in the world, so it's no surprise that he was drawn to the real-time shop floor data theme. The opening moments of his presentation made it clear that some shop floor challenges aren't exclusive to manufacturing spaces though: "Every manufacturing environment thrives on continuous improvement, but collecting and making sense of real-world shop floor feedback can be a challenge. Imagine you're on the shop floor in a manufacturing environment, and you have an idea. Where does that feedback go? Do you write it on a sticky note and put it on your manager's monitor? Or maybe you write it up in an email that gets buried in with a thousand other emails. Or worse, maybe it goes nowhere at all." Who can't relate to the experience of having an idea, but no idea what to do with it? Where to start? Tom's solution addresses three common challenges teams face when improving shop floor operations. 1. Feedback collection Shop floor feedback is often recorded manually, leading to delayed responses or lost ideas. Tom addresses this pain point with a super simple form, making it easy for users to submit feedback. 2. Categorization When feedback is reported, it's typically unstructured, which makes it hard to interpret and act on. Tom's solution makes it easier for teams to search and filter by using AI to categorize input. 3. Summarization AI also helps Tom summarize and recommend actions based on the feedback. These insights are delivered via weekly email, ensuring feedback is shared in an easily digestible format. This strategy enables faster issue resolution, supports data-driven decision-making, improves team communication, and ultimately aligns stakeholders around continuous improvement. How does it work? Tom's solution begins in Aras Innovator, where he created an ItemType to store all the shop floor feedback. To collect the input, he built a lightweight web form with three simple fields: Employee ID, Job ID, and Description. The submit button sends the form data to Aras Innovator via the OData REST API. Next, an onAfterAdd server event sends the description to a large language model (LLM) for categorization. Tom uses Ollama 3.2 LLM, which is open-source and runs locally to ensure that no sensitive data leaves the company's systems. The code prompts the model to categorize feedback into three key areas: Cost, Delivery, and Quality. The server method updates the feedback item's category property with the result. To close the loop, Tom uses the Aras scheduling service to trigger a weekly server method that uses the LLM to summarize the latest feedback and suggest actionable tasks. The method then emails the insights directly to managers. Takeaways Tom's solution demonstrates that leveraging AI in PLM doesn't need to be a "big bang" effort to have a tangible impact on the business. I think the key is identifying the right opportunity and using AI as a tool rather than the whole solution. By letting the LLM handle the tasks it does best (like categorization and summarization) and keeping the rest of the implementation simple, you can quickly deliver a valuable solution that's ready to iterate and improve. What do you think? Could you incorporate LLMs, generative AI, or machine learning in your future Aras projects? Are you already doing something similar today? Let us know in the comments below! Thanks again to Tom and all of the Innovation Challenge finalists. Thank you for reading along and supporting your fellow builders!
