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, products, or workflows. The gap is connecting Copilot to your organisational data like SharePoint, CRM, and internal systems. We integrate and tune Copilot so responses reflect your business context.

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. We configure permission-aware access so responses respect user roles and ensure secure, accurate results.

Can you build agents that handle approvals, onboarding, or ticket routing inside Teams?

Yes. Copilot Studio and Azure agent services allow deployment of agents inside Teams and Microsoft 365 workflows. These agents can automate tasks, apply business rules, route requests, and escalate when needed.

Can you automate extraction from invoices, contracts, and claims?

Yes. Azure Document Intelligence provides prebuilt extraction models for enterprise documents. We add validation, confidence scoring, and integration into business systems to handle high-volume processing efficiently.

How do you stop AI from drifting or returning wrong answers once it is live?

We implement monitoring, evaluation, and governance controls to track output quality and detect drift. This includes logging usage, running evaluations, and applying access controls so AI systems remain reliable over time.

When does fine-tuning a model make sense versus using Copilot or RAG?

RAG works best for grounded responses using enterprise data. Copilot handles general productivity tasks with connected data. Fine-tuning is used for specialised tasks requiring high accuracy or domain-specific outputs.

How much does Azure AI cost to run once it is in production?

Costs depend on services, request volume, and model selection. We design cost-efficient architectures, select appropriate models, and implement monitoring to control and optimise spend in production.

How long does it take to go from pilot to a working production system?

A readiness assessment typically takes around two weeks. A focused production build usually takes 8 to 12 weeks depending on data readiness and system complexity.

We run on Microsoft. Can you build AI on Azure with our existing data?

Yes. We build AI solutions on Azure that integrate with your existing Microsoft data and systems, including SharePoint, APIs, and internal infrastructure, while maintaining security and compliance.

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

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 caserather than defaulting to the same configuration for every client.

Language models insideyour security boundary

Retrieval grounded inenterprise knowledge

We connect AI to documents, business systems, andinternal knowledge sources so answers stay groundedin your own data with permissions preserved.

Agent orchestrationacross workflows

We use Azure capabilities that allow AI to triggeractions, interact with systems, and support multi-stepbusiness processes across Microsoft 365 and beyond.

Documentunderstanding atoperational scale

We apply document processing services to extractstructured data from contracts, forms, invoices, andother high-volume enterprise content.

Identity andgovernance built in

Security, permissions, auditability, and content controlsare designed into every AI workflow using your existingMicrosoft identity and governance model.

Model customizationwhere accuracydemands it

When standard retrieval and prompting are notenough, we apply targeted tuning and modeladaptation 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 18 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 contentinfrastructure on platforms like Drupal, WordPress, and headless CMS solutions.QED42 has 18 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 youreditors 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.

In practice

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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.

Common questions

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