Azure AI Solutions
Generative AI that does real work on your own data, inside your own Azure tenant, with the security, evaluation and governance an enterprise needs.
From idea to production AI
We help you find the AI use cases that are worth doing, prove them quickly, and take the good ones all the way to production.
AI readiness & use-case discovery
Workshops to identify, size and prioritise use cases against value, feasibility, data readiness and risk.
Knowledge assistants (RAG)
Chat and search over your documents, policies and systems with Azure OpenAI and Azure AI Search, with citations and access control.
Intelligent document processing
Extract, classify and validate data from invoices, forms and contracts with Document Intelligence and LLMs, then feed it into your systems.
AI agents & automation
Agents that call your APIs and workflows to complete multi-step tasks, built with Azure AI Foundry and connected through your integration layer.
Responsible AI & governance
Content safety, prompt-injection defences, evaluation pipelines, data protection and usage policies built into the solution.
LLMOps & cost control
Model versioning, automated evaluation, monitoring, token budgets and API Management as an AI gateway in front of your models.
AI is an integration problem.
A model on its own knows nothing about your customers, orders or policies. The value comes from connecting it, securely, to the systems where that knowledge lives, and letting it act through governed APIs.
Because we build integrations every day, our AI solutions come with the plumbing done properly: authenticated APIs, event-driven updates to search indexes, and workflows that put a human in the loop where it matters.
- Grounded in your dataRetrieval over current, permission-trimmed content, not a model's memory.
- Acts through your APIsAgents use the same managed, audited APIs as your other applications.
- Stays in your tenantAzure OpenAI and supporting services deployed in your subscription, with private networking where needed.
- Measured, not guessedEvaluation sets and quality metrics before and after every change.
Where Azure AI pays off
Employee knowledge assistant
Answer HR, IT and policy questions from approved documents, with sources cited and access respected.
Customer support co-pilot
Draft replies, summarise case history and suggest next steps for agents, using CRM and knowledge-base data.
Invoice and order automation
Read incoming documents, match them to purchase orders and post them to ERP, with exceptions routed to people.
Contract and compliance review
Pull out key clauses, dates and obligations, and flag deviations from your standard terms.
Operations agents
Agents that check stock, raise tickets or update records by calling the APIs you already manage.
Reporting in plain language
Let users ask questions of operational data and get explained, traceable answers.
Prove value in weeks, not quarters.
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Discovery workshop
Shortlist and score use cases; agree on success metrics and the data involved.
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Proof of concept
A working prototype on real data, with an evaluation set to show how well it actually performs.
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Production build
Security, integration, monitoring, CI/CD and responsible AI controls added for real users.
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Scale & improve
Measure adoption and quality, tune cost, and roll out to further teams and use cases.
Common questions
Is our data used to train the models?
With Azure OpenAI, your prompts and data are not used to train the underlying foundation models, and they stay within the Azure services you deploy. We also design access so the AI only sees what each user is allowed to see.
Do we need a data platform before starting with AI?
Not necessarily. Many valuable use cases work directly from documents and existing systems. Where data needs cleaning or connecting first, our integration practice handles it as part of the same engagement.
How do you deal with wrong answers ("hallucinations")?
By grounding answers in retrieved sources, requiring citations, testing against evaluation sets, and designing the workflow so people review outputs where the stakes are high.
What does it cost to run?
It depends on usage and model choice. We estimate running costs during the proof of concept and build in token budgets, caching and model selection to keep them predictable.
Have an integration or AI idea on the table?
Tell us what you're connecting or automating. You'll get a straight answer on approach, effort and the Azure services that fit, usually within one working day.