Engagement 11 · AI Build
Custom AI systems on Azure — retrieval over your data, multi-step agents, and workflow integrations. Built with proper evaluation, guardrails, and observability so it works the same in production as it does in the demo.
Why this exists
Anyone can wire up a RAG demo over a folder of PDFs in an afternoon. Production is different. The retrieval is wrong half the time, the responses drift, costs blow up, and nobody knows whether the latest prompt change made things better or worse. AI System Build delivers a working system with the engineering discipline that makes it last — evaluation pipelines, guardrails, observability, and the patterns that hold up under real load.
What's included
What success looks like, what failure looks like, and the eval set that decides which is which. The most important part — done before any code.
Model choice, retrieval pattern (vector / hybrid / keyword), agent vs single-shot, orchestration framework. Justified, not defaulted.
Ingestion, chunking, embedding, indexing — built for the data you actually have, with re-indexing as a first-class operation.
The actual system: APIs, orchestration, prompts, tool integrations. Written in Python or .NET, deployed on Container Apps / App Service / Functions to suit your stack.
Automated eval pipeline measuring quality and safety on every change. Content safety, prompt injection defences, output validation, and the ability to actually compare two prompts objectively.
Token usage, latency, retrieval quality, and user signal in dashboards. Per-request and aggregate cost tracking so the bill doesn't surprise anyone.
Deliverables
Timeline
Use case, eval set, architecture decisions, data shape. Nothing else gets built until this is signed off.
Iterative build with evaluation running on every change. Quality compounds; we don't ship the first thing that works.
Walkthrough, runbook, and a real change made together. Your team owns it from day one.
FAQ
Do we need an OpenAI Landing Zone first?
If you have any meaningful Azure footprint and AI is going to matter long-term, yes — the Azure OpenAI Landing Zone gets the foundation right. For a one-off prototype it's overkill. We'll be honest in the discovery call about which is appropriate.
What kinds of systems do you build?
RAG over enterprise documents, internal Q&A and search, document processing pipelines, multi-step agents (task automation, data extraction, report generation), and Copilot integrations. We avoid use cases where current models can't deliver — and tell you that up front.
Can the system run on-premises or air-gapped?
Azure OpenAI runs in your tenant region with strong data controls, which covers most "we can't send data outside" requirements. True air-gap with self-hosted models is a different conversation — we'll discuss the trade-offs honestly.
How do you handle prompt injection?
Layered defences — input validation, prompt shielding via Azure AI Content Safety, output validation, and tool execution sandboxing. We treat user input as adversarial by default.
What about deploying it via CI/CD?
Pipelines are part of the build. If your wider release engineering is missing, the CI/CD & Release Engineering Setup engagement covers the platform side properly.
What about Power Platform / Copilot Studio?
For low-code AI workflows over M365 data, Power Platform & Copilot Automation is a better fit. AI System Build is for custom code-based systems.
Next step
Book a 30-minute discovery call. We'll talk through the use case, your data, and what success would look like before agreeing scope.
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