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Azure AI
Azure AI consultancy and development
Konsolute designs, builds and runs Azure AI solutions that work with your own data, inside your own Azure tenant. From AI search and document processing to custom copilots and machine learning, our security-cleared engineers take you from use case to a governed, production-ready service.
BUSINESS OUTCOMES
Put AI to work on the information you already hold
Off-the-shelf AI only goes so far. Azure AI lets you build solutions around your documents, systems and processes, with the security, access controls and data residency your organisation needs.
Find answers faster
Give staff and customers accurate answers drawn from approved policies, guidance and records, with sources shown for every response.
Process documents automatically
Extract, classify and route information from forms, invoices, letters and reports, reducing manual rekeying and errors.
Automate everyday decisions
Use AI to triage requests, summarise cases and recommend next steps within the business systems your teams already use.
Keep control of your data
Run AI in your own Azure tenant and UK regions, with identity-based access, logging and content safety built in from the start.
OUR AZURE AI SERVICES
From first use case to production AI
We combine Azure AI services, software engineering and governance so your solution is useful on day one and supportable for years.
AI use-case discovery
Identify where AI will add measurable value, test feasibility against your data and agree success criteria before you invest.
AI search and knowledge
Build retrieval-augmented generation (RAG) solutions with Azure AI Search and Azure OpenAI that answer from your approved content.
Document intelligence
Automate document capture and classification with Azure AI Document Intelligence, with human review where accuracy matters.
Custom copilots and apps
Develop AI-powered applications and copilots in Microsoft Foundry, integrated with Teams, SharePoint and your line-of-business systems.
Machine learning
Build, deploy and monitor predictive models with Azure Machine Learning, from forecasting and classification to anomaly detection.
Responsible AI and governance
Put evaluation, content safety, access control and monitoring in place so AI is used safely and can be audited.
POWERED BY MICROSOFT
The right Azure AI services for each use case
Not every problem needs a large language model. We choose the Azure AI services, models and architecture that fit the task, your data and your cost and security requirements.
Azure OpenAI and Microsoft Foundry
Build generative AI using models deployed in your own Azure subscription, with prompts and safeguards managed.
Azure AI Search
Ground AI responses in your own content, so answers are accurate, current and respect existing permissions.
Document Intelligence
Turn forms, invoices and scanned documents into structured data your business systems can use.
Language, speech and translation
Summarise, translate and analyse text and audio at scale, from case notes to call recordings.
Predictive models
Use your historical data to forecast demand, predict risk and spot anomalies before they become problems.
MLOps on Azure
Keep models accurate in production with repeatable training, versioning, monitoring and release controls.
Secure AI architecture
Design AI services that meet your security standards, network boundaries and data residency needs.
Run and improve
Operate AI services with clear ownership, monitoring and a plan for continuous improvement.
RESPONSIBLE AI
AI your organisation can trust
AI is only useful if people can rely on it. We build evaluation, safeguards and accountability into every Azure AI solution, aligned with Microsoft's Responsible AI Standard and your own policies.
Tested before release
Evaluate accuracy, relevance and safety against agreed test sets before any AI solution reaches users.
Permissions respected
Make sure AI only uses and returns information each user is allowed to see, using Microsoft Entra ID and existing permissions.
Content safety
Filter harmful content, block prompt injection and log interactions so issues can be investigated.
Human oversight
Define where people review, approve or override AI outputs, especially for decisions that affect customers or citizens.
OUR APPROACH
From idea to production Azure AI
A staged approach proves value early, limits risk and leaves you with an AI service your team can own.
1. Discover
Identify use cases, users and the outcomes that matter.
2. Assess
Check data readiness, security requirements and feasibility.
3. Design
Agree the architecture, models, safeguards and success measures.
4. Implement
Build a working proof of value, then the production solution.
5. Validate
Evaluate accuracy, safety, performance and cost with real users.
6. Adopt & improve
Roll out, train users, monitor and improve over time.
Discover.Build.Improve.
AI IN PRACTICE
Responsible AI adoption in central government
Konsolute led the discovery and controlled rollout of Microsoft 365 Copilot for a central government organisation, preparing data, permissions and governance so staff could use AI within the organisation's security standards.
CLEAR SCOPE. SHARED EXPECTATIONS.
Agree what good looks like before you build
We agree the use case, data, safeguards and success measures up front, so your AI investment has a clear purpose and a realistic route into production.
Use case and success measures
Define the problem, the users and how success will be measured, from time saved to accuracy targets.
Data and content sources
Confirm which documents, systems and data the AI can use, who owns them and how current they are.
Security and safeguards
Agree access, data residency, content safety, logging and human review requirements before build starts.
Costs and ownership
Estimate token, compute and licensing costs, and agree who will own, monitor and improve the solution.
FAQ
Azure AI FAQs
Services, security, costs and delivery, explained.
Azure AI is Microsoft’s set of cloud services for building AI solutions, including Azure OpenAI models, Azure AI Search, Document Intelligence, language and speech services, and Azure Machine Learning. They run in your own Azure subscription, so you control data, access and security.
We cover use-case discovery, architecture, solution development, integration, evaluation, security and ongoing support. Typical projects include AI search over internal knowledge, document processing, custom copilots and machine learning models.
Microsoft 365 Copilot is a ready-made assistant inside Microsoft 365 apps. Azure AI is for building your own AI solutions around specific processes, data and systems. Many organisations use both.
No. Under Microsoft’s terms for Azure OpenAI, your prompts, data and outputs are not used to train Microsoft’s foundation models. Your data stays within your Azure tenant and chosen region.
Yes. Many Azure AI services and models are available in UK regions. We confirm regional availability for the services your solution needs and design around your data residency requirements.
Yes. Using Azure AI Search with Azure OpenAI, we build solutions that retrieve relevant content from your approved sources and generate answers with citations, while respecting existing access permissions.
Yes. Azure AI Document Intelligence can extract and classify information from forms, invoices and letters. We add confidence thresholds and human review so accuracy meets your requirements.
Usually some preparation is needed. We assess the quality, structure, ownership and permissions of the content in scope.
We test solutions against agreed evaluation sets for accuracy, relevance and safety, apply Azure AI Content Safety, and monitor quality in production. Human review is built in where decisions carry risk.
Yes. We build agents that can retrieve information and take actions in your systems, using Microsoft Foundry or Copilot Studio depending on the use case.
Yes. Our security-cleared engineers design Azure AI solutions with private networking, managed identities, logging and access controls to meet public sector and regulated industry standards.
A focused proof of value typically takes a few weeks. Production timescales depend on the number of data sources, integrations, security requirements and the level of evaluation needed. We agree phases after discovery.
Costs include delivery effort plus Azure consumption, such as model tokens, search and compute. We estimate running costs during design and build in monitoring so spend stays predictable.
TAKE THE NEXT STEP
Ready to put Azure AI to work?
Tell us about the process, problem or question you want AI to help with. We will help you test the idea, identify the right Azure AI services and plan a practical route to production.