Ground answers in evidence
For knowledge scenarios, evaluate retrieval with Azure AI Search and provide relevant passages to the model. Design source citations, document updates and permission filtering as part of the application.
Data & AI / Overview
DIGITAL CLOUD / Data & AI
Azure AI
Explore AI capabilities around documents, knowledge and business tasks. Test quality, data boundaries and operating costs before wider deployment.
Plan your solution01
Azure AI
Understanding the solution
Azure AI services can support document extraction, knowledge retrieval and assisted drafting. Digital Cloud turns a selected task into a testable application design, separating the model, approved information and business workflow. We agree acceptable behaviour and human review before deciding how the solution should be deployed.
Business AI should begin with a bounded task and a way to judge the output. Good results depend on suitable information, defined access and clear handling of uncertainty or mistakes.
Capabilities and scope
Evaluate before release
Build representative test questions and expected outcomes. Review answer relevance, support from sources, refusals and unsafe requests alongside latency and consumption; include difficult cases, not only demonstrations.
Plan deployment and operation
Select supported models and regions for the requirements. Define identities, secret handling, endpoint access, version changes and monitoring, with a rollback path when quality or behaviour changes.
A practical example
A support team drafts answers from approved manuals, checks the cited passages and routes unresolved cases to a specialist before responding to customers.
What your project can deliver
- A scoped architecture with data flows and access boundaries.
- An evaluation set and documented results for the selected task.
- A deployment backlog covering monitoring and human escalation.
The final deliverables, licensing and responsibilities are agreed for your environment before implementation.
02
Requirements and considerations
Identify permitted sources, sensitive data, expected outputs and human review points. Evaluate quality, costs and failure cases before allowing broader access or connected actions.
Azure AI
Project priorities
Task evaluation
Data boundaries
Operating costs
We begin with a conversation about the task, the people involved and the systems already in place. Together we identify what a useful result would look like and which dependencies need attention first. The agreed proposal sets the delivery boundaries, responsibilities and acceptance criteria.
Your engagement
How Digital Cloud can help
| Project phase | What happens |
|---|---|
| 01Define the design | Translate the requirements into a practical design. Confirm product choices, interfaces, permissions and the responsibilities needed to operate the solution. |
| 02Deliver in stages | Configure or implement the agreed scope, test representative workflows and resolve material issues. Plan user communication and any controlled transition from existing systems. |
| 03Prepare for ongoing operation | Confirm acceptance, document the relevant configuration and prepare the people responsible for daily use. Define maintenance and support arrangements before handover. |
Azure AI
Questions before you start
Can the model guarantee factually correct answers?
No. Grounding and evaluation help assess quality, but the workflow must handle uncertainty and require review where the consequences justify it.
What determines the cost and schedule?
The starting environment, integrations, user groups and agreed outputs determine the effort. We confirm scope and commercial terms before work begins. Software licenses, infrastructure consumption and ongoing support may be separate items.
What will we receive?
The proposal identifies the deliverables: these may include findings, a prioritized roadmap, a tested configuration, a prototype, documentation or training. We agree what is included and how completion will be assessed.
Can this work with our existing systems?
We review the actual applications, data sources and access requirements before recommending an integration. Dependencies and compatibility limits are recorded so the delivery plan reflects your environment.
What happens after the initial work?
You can use the findings to guide your own team or discuss a follow-on phase. Any maintenance, monitoring or support includes separately agreed service hours, responsibilities and response targets.
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