Azure in production — delivered, not just recommended
One of four Consulting engagements: we run Azure AI Search, Azure OpenAI, and Microsoft 365 / Copilot inside your own tenant — provisioning approved model access, wiring retrieval to your governed knowledge, and keeping data in-region under cost governance.
The work ranges from provisioning approved model access inside an existing subscription to designing Copilot connector and agent patterns that keep answers grounded in your governed knowledge. Microsoft-sponsored Azure credits give us demo capacity for Azure AI Search, Azure OpenAI, and Copilot-oriented prototypes; Blue Note Logic remains an independent delivery partner.
Four ways we deliver Azure
Each engagement starts from what you already run and ends with a system in production — not a slide deck.
Azure AI Search
Hybrid keyword + vector retrieval across PDF, Word, and structured data — cognitive skills, scoring profiles, and RAG wiring to your existing LLM stack.
Learn more
Azure OpenAI Service
GPT-4o inside your own subscription, region, and compliance boundary — provisioning, quota planning, and LiteLLM routing so existing apps reach it without new middleware.
Learn more
Microsoft 365 AI
Copilot enablement, Teams bots, SharePoint corpora indexed for semantic search, and Power Automate flows that trigger AI actions — no rip-and-replace.
Learn more
Azure Data & Analytics
Data Factory pipelines, Synapse workspaces, and Azure ML experiment tracking with governance, lineage, and cost control baked in from day one.
Learn more
Retrieval that actually finds the right document.
Azure AI Search is Microsoft's fully managed information retrieval platform — but what makes it useful in enterprise AI isn't the index. It's the retrieval pipeline that sits in front of your language model.
Blue Note Logic designs and deploys Azure AI Search for organisations with large, messy document estates: legal contracts, policy libraries, technical manuals, compliance archives. We combine keyword precision with semantic vector search so the retrieval layer actually finds the right document — not just a document that contains the right words.
What the engagement covers
- Index design and document pipeline (PDF, Word, SharePoint sources)
- Cognitive skills configuration for entity extraction and enrichment
- Hybrid retrieval: BM25 keyword + Azure OpenAI Embeddings vector search
- Custom scoring profiles and faceted navigation
- RAG integration: wiring retrieval output to GPT-4o or your existing LLM
- Query telemetry and relevance tuning after go-live
Legal contract corpus
A full contract archive — spanning decades and formats — indexed in Azure AI Search. Hybrid retrieval returns precise results across tens of thousands of documents, and a RAG layer connects to Azure OpenAI so staff get answers cited back to the source clause.
Legal / ComplianceCompliance policy retrieval
A policy and procedure library indexed with cognitive skills enrichment, tagged by jurisdiction, effective date, and department at index time. The retrieval layer surfaces the right version of the right policy — not a list of results to dig through.
Regulated SectorTechnical knowledge base
Internal documentation — runbooks, architecture diagrams, incident reports — indexed and connected to a Teams bot. Engineers ask in plain language and get answers drawn from the right document, with the source linked.
Engineering
GPT-4o inside your own compliance boundary.
Azure OpenAI Service gives your organisation access to GPT-4o, GPT-4o-mini, and Azure-native embeddings — inside your own Azure subscription. No data leaves your tenant. No shared inference pool. Your compliance posture stays intact.
Blue Note Logic handles the provisioning, quota planning, and integration work so your team is using the model in weeks, not quarters. We connect it into existing applications via LiteLLM routing, so most internal tools that already call an OpenAI-compatible endpoint can be pointed at Azure without rewriting the integration.
What the engagement covers
- Azure OpenAI resource provisioning in your subscription and region
- GPT-4o and GPT-4o-mini deployment and quota planning
- text-embedding-3-small deployment for vector search pipelines
- LiteLLM routing layer for multi-model and fallback support
- Prompt engineering and system instruction design
- Cost monitoring and usage governance setup
Regulated industry deployment
An organisation in a regulated sector needed GPT-4o-level capability without data leaving their EU Azure tenant. We provisioned Azure OpenAI in their subscription, handled regional deployment and quota planning, and integrated via LiteLLM — so legal and compliance could sign off on a clear, documented data boundary.
Regulated SectorInternal assistant on sovereign infra
A public sector organisation needed an AI assistant meeting data sovereignty requirements: Azure OpenAI in a specific region, integrated with an existing identity provider, serving responses only from a governed corpus — on infrastructure the organisation controls.
Public SectorMulti-model application routing
A software team needed to route query types to different models: GPT-4o-mini for high-volume simple queries, GPT-4o for complex reasoning, Azure embeddings for retrieval. We built the LiteLLM routing layer — one API call, routing handled behind it.
Software
Build the AI layer on the estate you already run.
Most organisations that want to add AI to their workflows are already running Microsoft 365. Teams, SharePoint, Outlook, Power Automate — the infrastructure for communication and knowledge storage is already there. Blue Note Logic builds the AI layer on top of it.
That means Copilot readiness assessments for organisations that have licences but haven't seen results yet, Teams bots that answer internal questions from a governed knowledge base, SharePoint corpora indexed and connected to a retrieval pipeline, and Power Automate flows that trigger AI actions based on business events.
What the engagement covers
- Copilot for M365 readiness review and enablement planning
- Teams bot development and deployment (Azure Bot Service)
- SharePoint library indexing for semantic search
- Power Automate workflow design with AI-triggered actions
- Microsoft Graph API integration for calendar, mail, and directory data
- Governance: who can ask what, which corpus answers which team
Corporate knowledge base in Teams
HR policies, operational procedures, and product documentation connected to a Teams bot. Employees ask in natural language and get answers from the official source material, with the document linked — cutting repeat policy questions sharply.
Corporate M365Copilot for M365 readiness
An organisation with Copilot licences but limited results engaged us to assess and remediate. The issues were consistent — SharePoint governance gaps, document quality, permission structures — so we fixed the underlying data estate the licence depended on.
CopilotPower Automate AI workflow
Incoming email enquiries classified, routed, and summarised automatically: Power Automate triggers on mail, Azure OpenAI classifies and summarises, and the result posts to a Teams channel with a recommended owner and draft response.
Automation
The data foundation your AI systems actually use.
AI systems are only as good as the data that feeds them. Azure provides the infrastructure layer — Data Factory for pipeline orchestration, Synapse Analytics for large-scale processing, Azure ML for experiment tracking and model deployment. Blue Note Logic designs the architecture that connects these into something your AI systems can actually use.
We work on the data side of AI projects: ingestion pipelines that clean and structure source data, transformation layers that produce training-ready or retrieval-ready datasets, and governance frameworks that track lineage and flag data quality issues before they become model quality issues.
What the engagement covers
- Azure Data Lake architecture and Data Factory pipeline design
- Synapse Analytics workspace setup and query optimisation
- Azure ML workspace: experiment tracking, model registry, deployment
- Data governance and lineage tracking with Microsoft Purview
- Cost governance: resource tagging, budget alerts, reserved capacity planning
- Monitoring: pipeline health, data drift detection, alerting
AI training data pipeline
A financial services organisation needed clean, governed data flowing into Azure ML before any training began. We designed the Data Factory ingestion pipeline, transformation layer, and Synapse workspace with lineage tracking and quality validation — a stable, reproducible data foundation.
Financial ServicesDocument corpus preparation
A large unstructured document estate prepared for AI retrieval: Data Factory pulling from multiple source systems, Azure Cognitive Services for OCR and entity extraction, output formatted for Azure AI Search indexing — running nightly to keep the index current.
Data EngineeringCost governance on Azure ML
An Azure ML environment that had grown without cost controls. We implemented resource tagging, budget alerts, compute auto-shutdown policies, and a reserved-instance plan for steady-state workloads — cutting spend substantially without reducing productivity.
FinOpsStart with an Azure / Copilot readiness audit.
We assess Azure AI Search, Azure OpenAI, Graph connectors, Teams, Outlook, Word, and SharePoint readiness, then scope the shortest path from where you are to a system in production.
Not specifically on Microsoft Azure? See our general & sovereign AI consulting.