## Build with Azure AI # Azure AI built into the Microsoft stack your organisation already runs on Copilot licences, SharePoint data, Teams workflows. The infrastructure is paid for. QED42 connects Azure AI to it: RAG on your documents, agents in your workflows, Copilot that knows your business. 2-week pilot on your data first [Share a use case](/contact) ## What we build ### Make Copilot useful Default Copilot draws on general knowledge and limited Microsoft Graph data. It does not know your policies, your products, or how your teams operate. We wire it into your SharePoint libraries, CRM, ERP, and internal knowledge bases through Copilot Studio. Where the base model needs adapting, we apply Copilot Tuning to shape responses to your domain without writing code. Available since June 2025 ### Search that reasons across your data Information scattered across SharePoint, Confluence, OneLake, file shares, and internal databases. Standard search returns links. Foundry IQ treats retrieval as a reasoning task: it plans multi-step queries across sources and returns a grounded answer with citations and source links. Microsoft reports up to 36% better relevance on complex queries versus traditional RAG. We configure the full pipeline with permission-aware access through Entra ID ### Agents that act inside Microsoft 365 Tasks that follow a pattern but require judgment at certain steps: expense approvals, onboarding sequences, procurement reviews, IT ticket triage. We deploy agents via Copilot Studio directly into Teams with one-click publishing, or via Foundry Agent Service for workflows that span multiple systems and need tool access, memory, and escalation logic ### Extract data from documents at scale Contracts, invoices, claims, applications: still moving through manual review in most organisations. Azure Document Intelligence provides 12 prebuilt extraction models covering the most common formats. We add confidence scoring, exception routing for edge cases, and downstream integration into your business systems through Logic Apps or Power Automate ### Govern and monitor AI in production Models drift. Agents access data they should not. Answers degrade and nobody notices until someone complains. We configure Foundry Control Plane for continuous tracking, Purview sensitivity labels that follow content through RAG pipelines, and Entra ID agent identities with scoped permissions. Audit logging and alerting from day one ### Fine-tune models for specialised accuracy When GPT-4o or another foundation model does not reach the accuracy a specific task demands because your domain is narrow or your data is proprietary. We fine-tune using parameter-efficient methods on Azure Machine Learning and manage the model lifecycle through production. Scoped only when Foundry model customisation is insufficient for the task ## Azure AI services we apply in production QED42 works across the Azure AI stack, selecting the right services for the use case rather than defaulting to the same configuration for every client. 01 ### Language models inside your security boundary We use Azure-hosted language models for enterprise tasks such as summarization, reasoning, classification, and conversational experiences, deployed within yourtenant and aligned to your compliance requirements. 02 ### Retrieval grounded in enterprise knowledge We connect AI to documents, business systems, and internal knowledge sources so answers stay grounded in your own data with permissions preserved. 03 ### Agent orchestration across workflows We use Azure capabilities that allow AI to trigger actions, interact with systems, and support multi-step business processes across Microsoft 365 and beyond. 04 ### Document understanding at operational scale We apply document processing services to extract structured data from contracts, forms, invoices, and other high-volume enterprise content. 05 ### Identity and governance built in Security, permissions, auditability, and content controls are designed into every AI workflow using your existing Microsoft identity and governance model. 06 ### Model customization where accuracy demands it When standard retrieval and prompting are not enough, we apply targeted tuning and model adaptation for specialized enterprise tasks. ## Why QED42 for Azure AI ### The Microsoft investment is already there. The AI connection is not. Most organisations on Microsoft 365 have years of documents in SharePoint, conversations in Teams, and operational data in Dynamics. Copilot and Azure AI do not reach any of it by default. Building that connection is the entire engagement. ### We know where AI meets the content layer QED42 has built enterprise content platforms and customer-facing applications for 17 years. When we connect Azure AI to an editorial workflow or a public-facing site, AI shows up where the work happens. Not in a separate dashboard nobody opens. ### Content management systems running on Azure Governments, universities, publishers, and large enterprises run content infrastructure on platforms like Drupal, WordPress, and headless CMS solutions. QED42 has 17 years of content platform expertise and leads the Drupal AI initiative. If your content layer runs on Azure, we connect AI directly to the systems your editors and teams already use. ### Copilot suggests. Agents escalate. A person decides. Every deployment includes defined checkpoints where a human reviews, approves, or overrides AI output before it reaches the end user. That is the design architecture, not a setting someone can toggle off. ## Already on Microsoft 365? [ Talk to us ](/contact) FREE GUIDE ### Why most AI pilots never reach production The gaps that stall AI projects between a working demo and a deployed system. Data quality, integration, monitoring, governance, and team adoption. Applies whether you are on Azure, AWS, or both. You must have JavaScript enabled to use this form. Name Work email\* Leave this field blank {"@context":"https://schema.org","@type":"FAQPage","@id":"https://qed42.com/ai-solutions/build-with-azure-ai#faq","isPartOf":{"@id":"https://qed42.com/ai-solutions/build-with-azure-ai#webpage"},"mainEntity":[{"@type":"Question","name":"We pay for Copilot but nobody uses it. What is missing?","acceptedAnswer":{"@type":"Answer","text":"Out of the box, Copilot draws on general knowledge and limited Microsoft Graph data. It does not know your policies, your products, or how your teams actually work. The gap is the connection between Copilot and your organisational data:SharePoint libraries, CRM records, internal knowledge bases, and business applications. QED42 wires Copilot into these sources through Copilot Studio and configures it so responses reflect your domain. Where the base model needs shaping, we apply Copilot Tuning so answers match your terminology and workflows without custom code."}},{"@type":"Question","name":"Can we get a single answer from data spread across SharePoint,Confluence, and internal databases?","acceptedAnswer":{"@type":"Answer","text":"Yes. Azure AI Search and retrieval services can query across multiple data sources and return a grounded answer with citations instead of a list of links. The key is configuring permission-aware access so the answer respects who is asking,not just what is being asked. QED42 sets up the retrieval pipeline, connects the relevant sources, and ensures access controls carry through from source to response."}},{"@type":"Question","name":"Can you build agents that handle approvals, onboarding, or ticket routing inside Teams?","acceptedAnswer":{"@type":"Answer","text":"Yes. Copilot Studio and Azure agent services allow us to deploy agents directly into Teams and Microsoft 365 workflows. These agents follow structured logic: they can look up data, apply business rules, route requests, and escalate to a person when the situation requires judgment. QED42 defines the agent scope, builds the logic, configures permissions, and sets up escalation paths so the agent operates within clear boundaries."}},{"@type":"Question","name":"Can you automate extraction from invoices, contracts, and claims?","acceptedAnswer":{"@type":"Answer","text":"Yes. Azure Document Intelligence provides prebuilt extraction models for common enterprise document types. We add confidence scoring so your team knows when to trust the output and when to review it, exception routing for edge cases, and downstream integration into your business systems. The result is a pipeline that handles volume, not a tool that processes one document at a time."}},{"@type":"Question","name":"How do you stop AI from drifting or returning wrong answers once it is live?","acceptedAnswer":{"@type":"Answer","text":"After deployment, models can drift as source data changes, user behaviour shifts, or content is updated. Monitoring means tracking output quality, logging usage patterns, flagging accuracy drops, and running periodic evaluations against ground truth. On Azure, we configure monitoring and governance controls,sensitivity labels that follow content through AI pipelines, and scoped agent identities so every action is auditable. Without this, deployed AI degrades silently."}},{"@type":"Question","name":"When does fine-tuning a model make sense versus using Copilot or RAG?","acceptedAnswer":{"@type":"Answer","text":"RAG works best when responses need to be grounded in specific, frequently updated organisational data like policy documents, product catalogues, or knowledge bases. Copilot with the right data connections handles most enterprise Q&A and productivity tasks. Fine-tuning is for cases where neither approach reaches the accuracy a specialised task demands, such as classifying domain specific documents or generating content in a narrow technical domain. QED42 evaluates all three during the build and selects based on accuracy, cost, and maintenance overhead."}},{"@type":"Question","name":"How much does Azure AI cost to run once it is in production?","acceptedAnswer":{"@type":"Answer","text":"Azure AI costs depend on the services used, the volume of requests, and the models selected. Language model inference is priced per token, with significant differences between model sizes. The cost risk comes from routing all tasks through a single large model without evaluating whether a smaller model would perform equally well. QED42 architects the cost model during the build, selects the right model tier for each task, and deploys spend monitoring so there are no surprises as production usage scales."}},{"@type":"Question","name":"How long does it take to go from pilot to a working production system?","acceptedAnswer":{"@type":"Answer","text":"A readiness assessment that evaluates data, infrastructure, and use case fit takes two weeks. A focused use case build, from architecture through production deployment, typically runs eight to twelve weeks. Agentic deployments take longer depending on how many systems the agent orchestrates. The biggest variable is data readiness. Organisations with well-structured, accessible data move faster.Those with fragmented data across disconnected systems may need a data foundation phase before the use case build starts. QED42 agrees a fixed timeline with the client before every engagement begins."}},{"@type":"Question","name":"We run on Microsoft. Can you build AI on Azure with our existing data?","acceptedAnswer":{"@type":"Answer","text":"Yes. We build AI on Microsoft Azure around your existing enterprise data and systems. Whether data lives in Microsoft platforms, business applications, APIs, or internal infrastructure, we apply the Azure AI capabilities best suited to your use case, from search and document intelligence to workflow automation and conversational AI, while aligning with your security and compliance requirements."}}]} ## Common questions ### We pay for Copilot but nobody uses it. What is missing? Out of the box, Copilot draws on general knowledge and limited Microsoft Graph data. It does not know your policies, your products, or how your teams actually work. The gap is the connection between Copilot and your organisational data:SharePoint libraries, CRM records, internal knowledge bases, and business applications. QED42 wires Copilot into these sources through Copilot Studio and configures it so responses reflect your domain. Where the base model needs shaping, we apply Copilot Tuning so answers match your terminology and workflows without custom code. ### Can we get a single answer from data spread across SharePoint,Confluence, and internal databases? Yes. Azure AI Search and retrieval services can query across multiple data sources and return a grounded answer with citations instead of a list of links. The key is configuring permission-aware access so the answer respects who is asking,not just what is being asked. QED42 sets up the retrieval pipeline, connects the relevant sources, and ensures access controls carry through from source to response. ### Can you build agents that handle approvals, onboarding, or ticket routing inside Teams? Yes. Copilot Studio and Azure agent services allow us to deploy agents directly into Teams and Microsoft 365 workflows. These agents follow structured logic: they can look up data, apply business rules, route requests, and escalate to a person when the situation requires judgment. QED42 defines the agent scope, builds the logic, configures permissions, and sets up escalation paths so the agent operates within clear boundaries. ### Can you automate extraction from invoices, contracts, and claims? Yes. Azure Document Intelligence provides prebuilt extraction models for common enterprise document types. We add confidence scoring so your team knows when to trust the output and when to review it, exception routing for edge cases, and downstream integration into your business systems. The result is a pipeline that handles volume, not a tool that processes one document at a time. ### How do you stop AI from drifting or returning wrong answers once it is live? After deployment, models can drift as source data changes, user behaviour shifts, or content is updated. Monitoring means tracking output quality, logging usage patterns, flagging accuracy drops, and running periodic evaluations against ground truth. On Azure, we configure monitoring and governance controls,sensitivity labels that follow content through AI pipelines, and scoped agent identities so every action is auditable. Without this, deployed AI degrades silently. ### When does fine-tuning a model make sense versus using Copilot or RAG? RAG works best when responses need to be grounded in specific, frequently updated organisational data like policy documents, product catalogues, or knowledge bases. Copilot with the right data connections handles most enterprise Q&A and productivity tasks. Fine-tuning is for cases where neither approach reaches the accuracy a specialised task demands, such as classifying domain specific documents or generating content in a narrow technical domain. QED42 evaluates all three during the build and selects based on accuracy, cost, and maintenance overhead. ### How much does Azure AI cost to run once it is in production? Azure AI costs depend on the services used, the volume of requests, and the models selected. Language model inference is priced per token, with significant differences between model sizes. The cost risk comes from routing all tasks through a single large model without evaluating whether a smaller model would perform equally well. QED42 architects the cost model during the build, selects the right model tier for each task, and deploys spend monitoring so there are no surprises as production usage scales. ### How long does it take to go from pilot to a working production system? A readiness assessment that evaluates data, infrastructure, and use case fit takes two weeks. A focused use case build, from architecture through production deployment, typically runs eight to twelve weeks. Agentic deployments take longer depending on how many systems the agent orchestrates. The biggest variable is data readiness. Organisations with well-structured, accessible data move faster.Those with fragmented data across disconnected systems may need a data foundation phase before the use case build starts. QED42 agrees a fixed timeline with the client before every engagement begins. ### We run on Microsoft. Can you build AI on Azure with our existing data? Yes. We build AI on Microsoft Azure around your existing enterprise data and systems. Whether data lives in Microsoft platforms, business applications, APIs, or internal infrastructure, we apply the Azure AI capabilities best suited to your use case, from search and document intelligence to workflow automation and conversational AI, while aligning with your security and compliance requirements. ### Bring your use case, we will build it Not sure where AI fits? We will help you figure that out You must have JavaScript enabled to use this form. First name Work email\* What best describes where you are? Exploring where AI fits Have a defined use case Ready to run a pilot Scaling AI to production Anything else you would like to share [Privacy policy](/privacy-policy) Leave this field blank