An operations manager at a Flemish logistics player asks his new AI assistant which deliveries are at risk today. Within seconds, the assistant gives a list, perfectly worded, with justification. One line jumps out immediately: a transport that the assistant says has been delayed for hours. The manager calls the driver. The driver has been unloading at the correct address for twenty minutes.
The assistant had made it up. The model never accessed actual transportation data and answered the question based on patterns from its training, rather than today’s reality.
This is not an exception. This is the pattern we see time and again in organizations that deploy AI without a foundation. And it is exactly why grounded AI exists.
What exactly is grounded AI?
Grounded AI is the architectural approach where you connect an AI system to reliable, current and organization-specific sources of knowledge. The model bases its answers on facts you verify, rather than solely on what it has learned during training.
Its opposite is a floating model: a language model that stands alone, generates answers purely from training data that is months or years old, and has no sense of what has changed in your organization or in yesterday’s world.
Grounding solves three fundamental problems faced by every unsecured AI system.
- PROBLEM 1: Hallucinations
Models invent convincing but incorrect answers when they do not know the answer. Grounding provides the model with a source to fall back on. - PROBLEM 2: Outdated knowledge
Training data has a cutoff. Grounding connects the model to live business data, documents, and systems that are always up-to-date. - PROBLEM 3: Lack of context
A generic model does not know your processes, products, and customers. Grounding injects that organizational knowledge at the right moment. - GROUNDING SOLVES THIS; Verifiable, traceable answers
Every answer is traceable to a source. You not only know *what* the system says, but you also know *why* and on what basis.
“An AI model without grounding is like an expert who sounds brilliant but makes up his facts. Impressive at first glance, very dangerous in production.”
The three pillars of grounded AI
Grounding is more than one technique. It is an interplay of three principles that you combine depending on the use case.
- Retrieval-Augmented Generation (RAG). For each question, relevant information is first retrieved from a knowledge base: a vector database, a document store, SharePoint or your data store. That context is passed along to the model, which generates an answer based on those sources. Result: answers that are traceable to sources you manage. This is the most commonly used approach.
- Fine-tuning on domain knowledge. The model itself is tutored on organization-specific data. More expensive and slower than RAG, but powerful for applications where language style, domain terminology or specific reasoning patterns are critical. A complement to RAG, not a replacement.
- Tool use and live data integration. The model gains access to tools (APIs, databases, internal systems) that it can invoke in real time. Think of an AI assistant retrieving live inventory information, querying a CRM system or checking current legislation before responding. This is the foundation of modern AI agents.
When do you choose what?
The choice between RAG, fine-tuning and tool use is an architectural decision based on three parameters: how fast the information changes, how specific the domain knowledge is, and how much latency the use case allows. A concrete case makes it tangible.
The situation. A Belgian insurer wanted an AI assistant to help claims examiners review files. Policy terms change, legal case law evolves, and each file requires access to customer data and history.
The solution. RAG for policy documentation and legal sources, tool use for live customer data from the core insurance system, and light fine-tuning on the language of claims reports.
The answer. An assistant who suggests a reasoned opinion in thirty seconds, with explicit source citations for each statement. The expert still decides, but works three times faster and with less risk of a missed clause.
So in most enterprise use cases, you combine the three principles. And that brings us to the architecture that makes this possible.
“Grounding is not a choice between security and speed. Organizations that implement it properly get both: AI that is lightning fast and demonstrably reliable.”
The architecture in five layers
A grounded AI system is built in five layers. Read the architecture from top to bottom as a question traveling through the system, and from bottom to top as the flow of information underpinning the answer.
Layer 5 — User Interface
Chat, Copilot, application, API consumer. The only thing the end user sees.
Layer 4 — Orchestration
LLM (Azure OpenAI / open-source) · prompt engineering · memory · agent logic. Receives the query, drives the grounding engine, and formulates the response.
Layer 3B — Grounding engine: RAG
Query → embed → retrieve → rerank → context inject. Retrieves the most relevant passages from the knowledge base for each query and injects them as context into the prompt.
Layer 3A — Grounding engine: Tool use
API calls · live queries · system integrations. Allows the model to actively call external systems for real-time data at this moment.
Layer 2 — Security & governance
Access control · audit logging · content filtering · EU AI Act compliance · Microsoft Purview. Makes every answer traceable and enterprise-worthy.
Layer 1 — Data Sources & Knowledge Layer
Vector DB (embeddings, semantic search) · OneLake / Microsoft Fabric (structured data) · Documents (SharePoint, PDF, wiki) · External systems (ERP, CRM, APIs, IoT). The foundation upon which all grounding rests.
The user interface is the only thing the end user sees. What lies behind it is completely transparent to him, but as an architect or IT leader, it is exactly that hidden infrastructure that makes the difference between a reliable system and risky toys.
The security and governance layer is what makes grounded AI enterprise-worthy. Access control ensures that an employee can only see information they are authorized to see, even if that information is technically available in the knowledge base. Audit logging makes every response traceable. Through Microsoft Purview, you integrate this seamlessly with your existing compliance framework and EU AI Act requirements.
The whole forms a system where you as an organization can always say: this answer is based on this source, on
How do you implement this securely in your organization?
The architecture above is the final goal. The road to it proceeds in five steps, and that order is not arbitrary.
- Lay the data foundation. Make sure your core data, policy documents, product information, process manuals and customer data are accessible, current and with clear ownership. Without this, grounding is built on sand. Microsoft Fabric and OneLake are the most pragmatic choice for this if you are already working in the Microsoft ecosystem. Also read our article on why your AI strategy starts with your data foundation.
- Define your security perimeter before you build. Which user gets to see what information through the AI? This is not an afterthought, but an architecture decision that you build in from day one. Role-based access control, combined with Microsoft Purview for audit trails, is the enterprise standard.
- Start with a defined RAG use case. Choose an internal use case with a clear success criterion: an HR knowledge assistant, a technical documentation search engine, a contract analysis tool. Quick results, manageable risk, and an architecture that you can extend afterwards.
- Expand with tool use and agents. Once the RAG foundation is stable and has built trust in the organization, add live data integrations. Agents that handle multiple steps autonomously are the end point, but they require a mature grounding foundation to operate securely. Learn more in our article on Power Platform as the most underrated AI bridge.
- Monitor, evaluate and continuously improve. Grounded AI is not a project with an end date. Measure the quality of answers systematically, monitor which sources are most frequently accessed, and adjust your knowledge layer based on what the system teaches you about the information needs in your organization.
“Grounded AI is not the end point of your AI journey. It is the foundation upon which everything that comes after can be built safely and reliably. Agents, automation and decision support start here.”
The question is no longer whether to deploy AI, but how
Organizations that embrace AI today without grounding are building on a foundation that will collapse sooner or later. One glaring error by a hallucinating system (a misdiagnosis, an incorrect contract, a manufactured compliance check) and trust in AI within the organization is damaged for a long time. It gets worse when faulty information goes public and hits your image.
So the question is no longer whether to deploy AI. The question is how to build it so you don’t lose trust. The answer is grounded AI: the combination of architecture, governance and data that makes every response traceable, current and appropriate.
Xylos helps you build grounded AI: architecture and adoption
At Xylos, we combine deep technical expertise in data architecture, Microsoft Fabric, Azure AI and open-source models with a pragmatic approach that starts with your business question, not the technology.
We guide organizations every step of the way: in outlining the initial data strategy, establishing the RAG foundation and rolling out production-ready grounded AI systems that employees trust every day.
Want to start with an initial RAG use case, ground an existing AI system or build out a complete enterprise AI architecture? Xylos brings both the technical depth and organizational context needed to make that a sustainable success.
Also read the other articles in this series
This is the fourth article in our series on Data and AI. Previously, you read about how AI wildfires and shadow IT in 2026, why Power Platform is the most underrated AI bridge, and why your AI strategy starts with your data foundation.
Want to talk through what grounded AI looks like for your organization? Our AI architects are happy to think through your data foundation, your use case and the architecture that fits your risk profile. Get in touch for an exploratory discussion.
About the author
Peter Verrykt is Data and Analytics Business Lead at Xylos and guides organizations in turning data into concrete business value. He helps companies look beyond technical implementations and use data as a foundation for better decisions, greater agility and sustainable growth.
