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Private AI for Word: Using Phi-4 for Q&A
Last Updated on March 2, 2026 Looking for a Microsoft Copilot alternative without recurring inference costs? Consider using local LLMs directly within Microsoft Word. For example, Mehul’s post highlights that Phi-4 is currently considered the leading choice among small-sized LLMs due to its significant improvements over previous versions. Therefore, it’s worth experimenting with integrating Phi-4 into Microsoft Word to explore its capabilities. This direction is at the core of our Local LLM Benchmarks for Microsoft Word, where we explore the move toward 100% data security on your intranet. Here’s a quick demo. The demo is powered by GPTLocalhost, which offers the same
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Intrane AI. Tailor LLM’s responses to your personal style in Microsoft Word.
Last Updated on March 1, 2026 📖 Part of the Secure AI Writing Workflows for Teams: A Complete Guide This post is a deep-dive cluster page focusing on various use cases enabled by LocPilot in Word. Visit the pillar page to master the basic functions, explore advanced editing and content refinement, and dive into power user experiments—all designed to keep your writing process efficient and secure on intranet. This morning, I discovered that Anthropic has introduced a new feature for writing in personal styles. To learn more about this feature, I visited their announcement page and found that Claude can generate custom styles
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Intranet AI. Basic functions of LocPilot in Word.
Last Updated on March 1, 2026 📖 Part of the Secure AI Writing Workflows for Teams: A Complete Guide This post is a deep-dive cluster page focusing on various use cases enabled by LocPilot in Word. Visit the pillar page to master the basic functions, explore advanced editing and content refinement, and dive into power user experiments—all designed to keep your writing process efficient and secure on intranet. The following screenshots are powered by GPTLocalhost, which offers the same core features for individual use. LocPilot in Word is the intranet edition of GPTLocalhost designed for teams with air-gapped security. For a quick demo of LocPilot, please
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Intranet AI. Using Microsoft Word with Two Workspaces in AnythingLLM.
Last Updated on March 1, 2026 When writing my last post using Word and AnythingLLM, I encountered a situation where I needed to work with different workspaces and settings. For instance, AnythingLLM provides a Chat mode with two configuration options: Chat and Query. When testing the effectiveness of RAG, I switched to Query mode in AnythingLLM. This allowed the model to provide answers only if document context is found in its vector database. In that experiment, I prepared two documents as the context for RAG. The main difference between these modes is how they handle answers. The Chat model provides answers with the LLM’s general knowledge and document context that is found. To
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Intranet AI. Local RAG in Microsoft Word: using AnythingLLM + LM StudioÂ
Last Updated on March 1, 2026 In my recent post, I explored AI-assisted writing by using LM Studio in Microsoft Word, utilizing my local Word Add-in (LocPilot in Word) as a bridge. During the experiment I realized that Retrieval-Augmented Generation (RAG) was an important aspect to consider. In this follow-up story, I’ll demonstrate how to enable local RAG in Microsoft Word using AnythingLLM and LM Studio as the backend. Local RAG means that all data processing happens on your computer, without any data leaving your device. This approach has several benefits, including increased security and reduced reliance on cloud services. 📖 Part
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LM Studio vs Microsoft Copilot: Choosing the Right AI for Word
Last Updated on March 1, 2026 Now that I’ve integrated Microsoft Word locally with several local LLM servers, it’s time to put its capabilities to the test. I’ll be benchmarking Microsoft Copilot in Word with using LM Studio in Word. In such integration, my local Word Add-in (LocPilot in Word) acts as a bridge between Microsoft Word and LM Studio. To evaluate its effectiveness, I’ll review the key features outlined in Holger’s story “Using Microsoft Copilot in Word”. Additionally, I’ll reflect on the benefits and drawbacks of my local first approach. In fact, I’m writing this story in Word right now with the assistance of the llama-3.2–3b-instruct model loaded in
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Local. Private. An alternative to Microsoft Copilot in Word.
Last Updated on February 26, 2026 Large language models (LLMs) have been rapidly advancing over the past few years. While bigger and more powerful versions are being developed in the cloud, there’s a growing trend towards running LLMs on your own computer. This shift aims to bring LLMs closer to your device, providing faster and completely private processing of your data. By doing so, you can unlock new possibilities for your writing and reduce costs associated with cloud-based solutions such as Microsoft Copilot in Word. After conducting research on affordable alternatives at Microsoft AppSource, we discovered several promising Word Add-ins that
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Local. Private. Use Apple Intelligence in Microsoft Word.
Last Updated on March 3, 2026 At the 2025 Worldwide Developers Conference, Apple introduced its new Foundation Models framework, which gives app developers direct access to the on-device foundation language model at the core of Apple Intelligence. The enhanced model is efficient for text generation with improved reasoning capabilities. The compact and approximately 3-billion-parameter model supports 15 languages and is specifically optimized for Apple silicon. LocPilot for Word is a local, low-cost alternative to Microsoft Copilot in Word. With LocPilot for Word, you can now effortlessly integrate Apple Intelligence directly into your Microsoft Word. Apple Intelligence provides complete data privacy and
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Private AI for Word: Using GPT-OSS-20B and Phi-4 for Text Rewriting
Last Updated on March 2, 2026 If you’re interested in utilizing powerful LLMs within Microsoft Word while prioritizing data privacy, we invite you to explore gpt-oss-20b via LocPilot in Word. You can view a comparison of OpenAI’s gpt-oss-20b and Microsoft’s Phi-4 by watching our demo video, which highlights their capabilities side-by-side. LocPilot in Word enables you to run these advanced models directly on your computer without needing internet access. By hosting them locally, you ensure complete data privacy, avoid monthly fees, and benefit from cutting-edge LLMs. This direction is at the core of our Local LLM Benchmarks for Microsoft Word, where
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Private AI for Word: Contract Analysis with IBM Granite 4
Last Updated on March 2, 2026 Introduction Recent findings show that 77% of workers disclose confidential business information via ChatGPT and other cloud AI platforms, posing significant security and compliance challenges. Given Microsoft Word’s status as a widely used word processor, it’s not surprising that some workers might transfer sensitive information to these external AI services by copying and pasting. In light of these concerns, organizations are increasingly turning to on-premise Large Language Models (LLMs) to safeguard sensitive data and ensure regulatory compliance. By hosting LLMs internally on your intranet, businesses can exert greater control over their information environments, reducing the