Responsible AI Use in FileMaker 2025
Reading Time: 8 minutesExplore how FileMaker 2025 puts AI front and center, and why understanding it is more important than ever. This blog series breaks down what responsible AI use means inside FileMaker, starting with the basics: AI literacy.
Responsible AI use starts with AI literacy, which means making an effort to understand how AI systems work. That’s why I’m kicking off this series of posts on AI in FileMaker alongside the release of FileMaker 2025. This release marks a significant point in time in the platform’s 40 year history. AI features are making their way squarely in the middle of the product, and my goal is to help you navigate the latest features, understand responsible AI adoption, and help keep the human element at every step along the way. As we go through this series, I’ll keep these two ideas at the front of my mind:
- How do I keep the human element central in this process?
- What does responsible AI mean for FileMaker 2025?
There is a plethora of valuable content and how-to guides from FileMaker experts, and I’m excited to see what evolves into best practices. This series is meant to provide you with a keen angle on why you would want to use these features and how to do so responsibly. (More below on what I mean by this.)
I believe harnessing the power of AI means not just knowing what these tools can do, but also understanding when and where to use them. This ensures your people, data, and company remain safe and responsible as you adopt AI.
Responsible AI use begins with AI literacy, which involves making an effort to comprehend how AI systems function. To support this, I’m launching a series of posts on AI in FileMaker, breaking down the new features by purpose and use cases. As we go through this series, I’ll keep these two ideas at the front of my mind:
- How do I keep the human element central in this process?
- What does responsible AI mean for FileMaker 2025?
Key Highlights of FileMaker 2025
The 2025 release is perhaps the largest in FileMaker’s history, introducing features such as RAG, semantic image search, natural language search, text extraction from PDFs, and performance enhancements to ensure these features operate faster and more reliably. I’ll be honest. There is a lot to this release, and much of it is advanced but extremely powerful. With great power comes great responsibility. And that means taking the time to understand these powerful features in order to implement them in a way that helps your business thrive.
I believe overwhelming people with too much information at once is not effective, so I’ll be gradually rolling out each post in this series, focusing on practical use cases. This involves examining how to apply these features, understanding dependencies, and how they work together. This series is meant to evolve and keep up with trends in AI, focusing on how to adopt AI responsibly. Let’s get started.
Getting Started with AI in FileMaker
I think the best place to start with grasping how you will use AI in FileMaker is to start with the AI model.
What is an AI Model?
An AI model is a system trained to recognize patterns in data so it can make decisions, predictions, or create new content. These models are the backbone of AI systems, enabling them to process information and deliver outputs based on learned patterns.
There are many types of AI models, each built for a specific purpose. For example, decision trees help answer simple yes/no questions (like “Is this email spam?”), while neural networks are used for tasks like facial recognition or translating languages. Generative models, like GPT, can even write stories or draw original pictures. Knowing what kind of model you’re working with helps you build better, more responsible AI.
Start with the Model
When we talk about the current AI in FileMaker, we are primarily referring to the different functionality that allows us to use AI models natively in the product. By native, I don’t mean these model reside entirely within the product. There are still several steps you’ll need to take to integrate AI and one basic step includes selecting the model you wish to use. This is a central part of an AI strategy, so I’m including a basic description of the current types of models, and therefore AI features, we can take advantage of in FileMaker 2025. Keep in mind the topic of model selection, as well as model categorization is vast, so this is just the briefest of brief, meant to give you an easy to understand overview.
AI Models Supported Natively in FileMaker 2025
This section outlines the four core types of AI models supported in FileMaker 2025, including how they work, what they’re used for, and key considerations for responsible implementation.
1. Sentence Transformers (Text Embedding Models)
These models power advanced semantic search and text similarity features by converting text into vector embeddings, which are numeric representations that capture the meaning of words and phrases in a multidimensional space. This allows FileMaker to “understand” text beyond keywords, enabling smarter search, classification, and analysis.
- Examples of use:
- Semantic Search: Find documents or records that are conceptually similar to a user’s query—even without shared keywords.
- Text Similarity: Detect duplicates, group similar entries, or classify content based on meaning.
- Responsible use: Always normalize embeddings and ensure they’re generated from the same model. Evaluating the quality of embeddings is especially challenging because the resulting vectors are essentially a black box. They can be difficult to interpret or validate directly. Developers should incorporate validation strategies such as sanity checks, benchmark comparisons, and cross-model consistency checks to help ensure embeddings reflect intended meaning.
2. Multi-Modal Embedding Models (Text + Image)
These models extend the idea of embeddings to both text and images, enabling FileMaker to bridge language and visuals, like searching for an image using a sentence or identifying similar images from a visual sample.
- Examples of use:
- Semantic Image Search: Search for images using text prompts or find similar images based on appearance.
- Digital Asset Management: Organize and retrieve images using both visual content and associated text metadata.
- Responsible use: The same principles apply here as with text embeddings. Because the resulting vectors are difficult to interpret directly, it’s essential to evaluate the accuracy and quality of image embeddings through careful testing. Responsible use of multi-modal embeddings depends on a rigorous and well-documented creation process to ensure the outputs are meaningful and trustworthy.
3. Text Generation Models
Text generation models produce relevant, human-like responses based on written prompts. These capabilities are ideal for drafting content, summarizing information, answering questions, and powering conversational interfaces in FileMaker.
Note: Image generation is not currently supported within FileMaker 2025. While this may be added in future updates, text generation is the sole focus for now.
- Examples of use:
- Natural Language Queries: Perform SQL or FileMaker finds using plain English instead of structured commands.
- Content Creation: Draft templates, support messages, product descriptions, or reports.
- Research & Analysis: Summarize source material, spot trends, and synthesize information.
- Coding Assistance: Generate or debug code snippets, or write SQL and Python scripts from natural language.
- Strategy & Ideation: Brainstorm ideas, structure documents, or plan workflows.
- Responsible use: Text generation models can introduce risks such as hallucinated facts, inappropriate or harmful content, and legal or credibility concerns related to privacy, plagiarism, or misinformation. Basic safeguards start with keeping a human-in-the-loop review. (More come on this topic in a future post). Documenting prompt templates and applying output validation workflows can also reduce risk and improve trust in the generated content.
4. Prediction Models (Regression Models)
FileMaker 2025 introduces support for prediction models that can forecast numerical values based on structured data. Currently, the only supported regression model is the Random Forest Regressor, which works by aggregating the predictions of many decision trees to improve accuracy and reduce overfitting. These models are useful for estimating outcomes from patterns in embedded vectors or numerical inputs.
- Examples of use:
- Financial Forecasting: Predict outcomes based on reports or sentiment data.
- Customer Insights: Estimate churn or purchase likelihood from user feedback.
- Predictive Maintenance: Anticipate failures based on logs and sensor data.
- Responsible use: As with other AI models, predictive accuracy depends heavily on the quality and representativeness of the data. Predictive systems can produce false-positives and false-negatives, which can have significant consequences depending on the application, especially if they are use to directly impact decisions. Given that FileMaker 2025 currently supports only Random Forest Regressors, it’s important to understand how this algorithm behaves with different data data sets and build a reliable validation and establish human oversight.
Sneak Peek of the FileMaker 2025 Blog Series
Below are some of the thoughtful blog posts we have planned for 2025, focusing on FileMaker and responsible AI. Each post emphasizes an intentional approach to implementing AI responsibly . In this series, you can look forward to in-depth explorations of innovative features, practical guides for AI integration, and insightful discussions on emerging technologies. Each post is crafted to offer clear, actionable insights to help you maximize FileMaker’s capabilities while adopting AI responsibly.
Reverse Image Search in FileMaker – Let’s Have a Look
This post will provide tips and strategies for using FileMaker’s new image search features. It will include guidance on passing containers in semantic search and optimizing image data for AI-powered queries.
Safe & Responsible AI Integration Tips for FileMaker
This practical guide will offer actionable advice for integrating AI into FileMaker solutions safely and responsibly. It will present how you can effectively deploy AI responsibly in your FileMaker solutions by looking at the three parts of the AI system—software, hardware, and data.
How to Make Practical Use of Semantic Search
This deep dive will explore FileMaker’s new embedding and semantic search tools, focusing on practical use cases. It will explain server-side semantic find, improvements to embedding, and how to implement these features responsibly to enable smarter and faster data retrieval.
Keyword vs Semantic Search Strategy for FileMaker
This comparison piece will help users understand the differences between traditional keyword search and the alternative AI powered search capabilities in FileMaker. It will offer advice on when to use each approach for optimal results.
Image Models Comparison
This article will compare different AI image models available in FileMaker. It will provide practical advice on selecting the right model for various use cases and highlight the strengths and limitations of each option.
Retrieval Augmented Generation (RAG) in FileMaker
This overview will introduce Retrieval Augmented Generation (RAG) in FileMaker. It will describe how RAG grounds AI responses in an organization’s private data, improving both accuracy and security.
History of AI in FileMaker – What’s New and Hidden
This reflective post will trace the evolution of AI in FileMaker. It will highlight both the new features and the “hidden” AI functionalities that users may not realize they are already benefiting from.
Prompt Injection Explained: A Responsibility Tip for Safer AI Use
This article will discuss the risks associated with AI prompt injection in FileMaker. It will offer strategies for mitigating these risks and ensuring safe usage of AI-driven features.
FileMaker AI Lexicon / Glossary
This glossary will define essential AI terms for FileMaker users. This guide is intended to boost AI literacy and support responsible adoption of new technologies within the FileMaker ecosystem.
What Responsible AI is and Why it Matters for FileMaker 2025
At Violet Beacon, we prioritize responsible AI, and deploying this in FileMaker is no exception. We believe it is vital to approach any AI project with thoughtful analysis and planning. This ensures that AI technologies are designed and applied in ways that prioritize fairness, transparency, privacy, and human oversight. In the context of FileMaker 2025, responsible AI is critical to maintaining trust, enabling safe adoption, and aligning with compliance standards.
To a degree, making responsible decisions about AI should feel familiar. We’ve been making thoughtful choices about technology for years, like selecting trusted providers for email, storage, or communication. Even though AI introduces new dimensions, such as bias, transparency, and automation at scale, the core decision-making process isn’t entirely new.
What’s different is the impact. With AI, choices about how we build, use, and disclose tools can affect not just productivity—but trust, reputation, and human well-being. That’s why this blog series focuses on helping you make those choices with clarity and care.
By embedding responsible AI principles into FileMaker 2025 adoption, users can confidently harness the platform’s capabilities while safeguarding their data, people, and organizational values.
Area | Why It Matters |
AI Governance & Oversight | Ensures responsible, standardized and compliant AI deployment |
Human Oversight & Guardrails | Maintains trust and safety, prevents edge-case failures |
Data Quality & Bias | Prevents unfair outcomes, improves AI reliability |
Privacy & Security | Protects user data, aligns with regulations |
Transparency & Documentation | Builds trust, supports user understanding |
Continuous Education | Empowers users, supports responsible adoption |
Essential Reading on FileMaker 2025 and AI
As I mentioned above, there is a plethora of excellent writing out there on FileMaker 2025. I am especially impressed with the articles below and fully suggest them to further your continuing education.
What if users could ask questions the way they think?
FileMaker 2025 Executive Summary
Final Thoughts for Now
The introduction of AI in FileMaker 2025 brings powerful new tools and a meaningful responsibility to use them well. If you’re feeling both intrigued and a little uncertain, you’re in good company. Responsible AI adoption begins with curiosity, thoughtful questions, and a commitment to keeping people at the center of every decision.
This series is here to support that process. Rather than covering everything at once, we’ll move gradually as we build understanding, explore practical use cases, and highlight how to apply these tools with clarity and care. You don’t need to be an expert to make good decisions.
Until next time…How are you approaching responsible AI in FileMaker?
How AI Was Used in This Post
AI supported this post by assisting with topic brainstorming, research, drafting, and proofreading. The header image was generated using ChatGPT. All contributions were overseen to ensure a human-centered tone.