Azure AI · strategy · RAG · governance
Retrieval is where most enterprise AI projects quietly fail.
Azure AI consulting covers three things that decide whether a deployment survives contact with production: retrieval that returns the right passage, access control applied during retrieval instead of after it, and an evaluation harness that tells you when either has drifted. The model is rarely the problem. Chunking, embedding strategy and evaluation are, and none of them are visible in a demo.
Scope
What we deliver
Azure OpenAI Service
Private model and embedding deployments in your own subscription, with tenant isolation, customer-managed keys and fine-tuning. Model availability moves quickly — we scope against what your region actually offers on the day, not against a slide.
Azure AI Search & Vector RAG
Build hybrid semantic search engines indexing Dataverse, SharePoint, and SQL databases into high-dimensional vector embeddings.
Document Intelligence (OCR)
Extract structured line items from PDFs, invoices, customs declarations, and receipts into ERP databases automatically.
Azure Content Safety
Implement automated prompt injection filtering, toxicity monitoring, and PII masking for enterprise AI compliance.
Azure Hybrid Data Pipelines
Connect on-premise databases and legacy ERPs to Azure AI endpoints via Azure API Management and private Azure ExpressRoute.
Custom AI API Engineering
Develop high-throughput REST APIs, microservices, and Webhooks linking Copilot Studio agents to backend databases.
AI Governance & Evaluation
An evaluation harness that runs on every change, model and prompt versioning so an answer can be traced to what produced it, and the documentation an EU AI Act risk classification requires. This is the difference between a deployment you can defend and one you can only demonstrate.
Cost per Decision
Token spend attributed to the workload that incurred it, so the unit is cost per resolved query, not a monthly total nobody owns. Where an Azure estate needs this beyond AI, it is the same practice as our Azure FinOps work.
Questions
Answered straight
Three things, in order of what decides success. Strategy: which workloads justify a model at all, and what the unit economics look like. Engineering: retrieval, chunking, embedding strategy, access control applied during retrieval, and the integration back into the system of record. Governance: an evaluation harness, model and prompt versioning, and the documentation a risk classification requires. Most projects that fail have done the middle one well and skipped the other two.
Four controls. An evaluation set that runs on every model or prompt change, so a regression is caught before a user finds it. Versioning that ties an answer back to the exact model and prompt that produced it. Content Safety and PII handling at the boundary. And a documented risk classification, which under the EU AI Act is an obligation rather than good practice for systems in scope.
Apply access control during retrieval, not after it. Filtering results post-retrieval means the restricted document still influenced what the model was given, and in a generated summary that leakage is invisible. Security trimming belongs in the query against the index, using the same identity the user holds in the source system.
Azure OpenAI Service provides enterprise-grade SLA, dedicated tenant isolation, data privacy compliance, and native integration with Microsoft Entra ID. Your enterprise data is stored in your private Azure subscription and is never used to train public foundational AI models.
Azure AI Search indexes your proprietary PDFs, ERP records, and SQL databases into high-dimensional vector embeddings. When a query is submitted, Azure AI Search retrieves the precise relevant passages and passes them to Azure OpenAI models, delivering accurate answers grounded in enterprise facts without hallucinations.
Yes. Azure AI Document Intelligence uses advanced OCR and deep learning models to extract line items, tax numbers, vendor details, and totals from structured, semi-structured, or handwritten documents directly into Dynamics 365, Odoo, or Dataverse.
We implement Azure AI Content Safety filters, Microsoft Entra ID role-based access control (RBAC), private VNet endpoints, customer-managed encryption keys (CMK), and automated telemetry monitoring.
Next
Send us the questions your retrieval gets wrong.
Failure is almost never the model. It is chunking, embedding strategy, or access control applied after retrieval instead of during it.