Azure OpenAI Integration Services
We build AI solutions on Azure OpenAI: GPT-5 and Claude class models, embeddings and fine-tuning, running in your own Azure tenant. Built for Swedish organizations that need GDPR compliance and EU data residency.
- Enterprise Security
- Swedish Data Residency
- Built on Azure SLAs
Why Azure OpenAI Instead of the Public API?
Data Boundaries You Can Verify
Azure OpenAI runs the same models as OpenAI inside your own Azure subscription: prompts and completions are not used to train models, and traffic can stay on your private network. The exact boundary depends on the model and deployment type you choose; Claude models in Microsoft Foundry have their own hosting options and processing terms. We document and verify the agreed data-processing boundaries for each deployment, covering inference geography, logging, retention, and operational access. Microsoft's GDPR commitments are set out in its Data Protection Addendum; your implementation's compliance is established in the design, not inherited from the platform.
- Your data never trains OpenAI models
- Security practices aligned with ISO 27001
- Swedish/EU data residency available
- Private networking with Azure VNet
The Full Model Catalog
GPT-5 and Claude class models for text, plus embeddings, image and speech models. When a general model is not accurate enough for your domain, we fine-tune one on your own data.
- GPT-5 class models with long context windows
- Fine-tuning on your own data
- Embeddings for semantic search and RAG
- Works in Swedish, not just English
Our Azure OpenAI Services
Custom Integration Development
- API integration and orchestration against your existing systems
- Chat and copilot interfaces in React/Next.js
- Typed SDK clients in TypeScript
- Streaming responses for low perceived latency
- Token budgeting that keeps API costs predictable
- Retry logic and graceful degradation when the API throttles
RAG & Vector Search
- Azure AI Search as the retrieval layer
- Embedding pipelines kept in sync with your documents
- Chunking strategies tuned to your document types
- Semantic search over your own content
- Hybrid search: vector plus keyword, with reranking
- Answers with citations back to the source document
Model Fine-Tuning
- Fine-tuning on your terminology and formats
- Cleaning and structuring your training data
- Hyperparameter tuning against a held-out test set
- Evaluation before and after, so the difference is visible
- Versioned deployments you can roll back
- Quality monitoring once the model is in production
Security & Compliance
- Secrets in Azure Key Vault, never in code
- Managed Identity instead of API keys where possible
- Content filtering tuned to your risk profile
- Audit logs of who asked what, and when
- GDPR review of data flows and retention
- Private endpoints, no exposure to the public internet
Production Operations
- Dashboards in Azure Monitor and Application Insights
- Cost tracking per feature, so you know what each use case costs
- Rate limiting that protects your quota during traffic spikes
- Failover between regions and deployments
- Latency work: caching, model choice, prompt size
- Support agreements after go-live
Training & Enablement
- Hands-on workshops for your developers
- Prompt patterns that hold up in production
- Architecture reviews of your existing AI plans
- Runbooks and documentation your team can operate from
- Handover, so you are not dependent on us
- Advisory hours when questions come up later
Technology Stack
Azure AI Services
- Azure OpenAI Service
- Azure AI Search
- Azure Cognitive Services
- Azure Machine Learning
Infrastructure
- Azure Container Apps
- Azure Functions
- Azure App Service
- Azure API Management
Data & Storage
- Azure Cosmos DB
- Azure PostgreSQL
- Azure Blob Storage
- Redis Cache
Development
- TypeScript/Next.js
- Python (FastAPI)
- LangChain/Semantic Kernel
- OpenAI SDK
Common Use Cases
Intelligent Document Processing
Pull structured data out of contracts, invoices, reports and policies
An LLM reads the document, classifies it and extracts the fields you care about. It handles messy scans and inconsistent layouts where rule-based extraction gives up.
Clients: Swedish government agencies and municipalities
Customer Support Automation
Chatbots and automatic routing of support tickets
A bot that answers from your own documentation, keeps the conversation context, and hands over to a human agent with the full history when it reaches its limits.
Clients: Telecom, energy and pharmacy retail companies
Knowledge Management & Search
Semantic search across internal documentation and data
Ask a question in plain Swedish or English and get an answer with sources, built on RAG over your wikis, file shares and systems of record.
Clients: Telecom, IT services and retail enterprises
Code Generation & Developer Tools
AI-assisted coding, documentation and code review
Code generation and review assistants set up inside your own environment, so proprietary source code never leaves your tenant. Includes test generation and documentation from code.
Clients: Aerospace, defense and automotive manufacturers
Two fixed-scope ways to start
Defined engagements with a clear deliverable, so the first step does not require trusting a pitch.
Document AI pilot
One document workflow, taken from sample documents to a working pilot with measured extraction quality, so the production decision rests on evidence instead of a demo.
- A working pilot for one agreed document workflow, in the Azure environment we agree on during scoping (yours or ours)
- Evaluation against a dataset you approve, with quality measured per extracted field
- A human-review process for the cases the model gets wrong
- Production architecture recommendation and a cost projection with explicit assumptions
What we need from you
- Representative sample documents together with the expected outputs
- An Azure subscription, or we run the pilot in ours
- A domain expert who can judge correctness, a few hours per week
Typically 3 to 5 weeks depending on document variety. Production rollout and integrations beyond one target system are follow-on work. Fixed quote after a scoping call.
Book a CallAI cost assessment
A short engagement that answers one question: what are you actually paying for LLM usage, and what would the same quality cost with caching, batching, and the right models?
- Per-workload breakdown of your current token usage and spend
- Caching and batching opportunities quantified against your real prompts
- Model comparisons with quality trade-offs measured on your workloads, not public benchmarks
- Deployment options priced out, including EU Data Zone versus Global
- A savings plan ranked by implementation effort, with explicit assumptions
What we need from you
- Usage exports or API logs from your current setup
- Sample prompts and expected outputs for each workload
Typically 1 to 2 weeks. Implementing the changes is follow-on work you can also do yourself with the report. Fixed quote after a scoping call.
Book a CallWant to Build on Azure OpenAI?
Book a free consultation. We go through your use case, sketch an architecture, and give you an honest view of cost, effort, and what the models can and cannot do.