Custom AI on Azure for the
30–300 person business.
When Microsoft 365 Copilot runs out of road, this is what comes next. Custom Copilots, RAG over enterprise content, document-processing pipelines and agentic workflows — built on Azure OpenAI and Azure AI Foundry with production-grade evals and governance.
Three signals you’ve outgrown M365 Copilot.
If any of these are true, a custom Azure build is usually the better next move than buying more Copilot seats.
You need retrieval over 10,000+ documents.
M365 Copilot’s grounding works to a point. Past that point you need a real vector index, hybrid search, reranking, and chunking strategies tuned to your content type — which is Azure AI Search territory.
You need an agent that acts, not just answers.
Copilot Studio is the right answer up to a certain orchestration ceiling. Above that — multi-step agents that call APIs, branch on outputs, write back to systems of record — you’re in Azure AI Foundry with custom agent frameworks.
You have data the off-the-shelf model can’t see.
Regulated data, customer PII, IP-sensitive content, residency-bound workloads. Azure’s private endpoints, customer-managed keys, and tenant isolation give you the controls Copilot’s shared infrastructure doesn’t expose.
The Azure AI capability surface we build on.
Six tiles that compose into every Azure-hosted custom AI workload we ship at SMB scale.
Azure OpenAI Service
GPT-4o-class foundation models with enterprise SLAs, private networking, customer-managed keys, and no data used for model training. The default substrate for any generative workload that touches business data.
Azure AI Foundry
Microsoft’s build-and-ship platform for custom AI: model catalogue, prompt flows, evaluations, agent frameworks, content safety, observability. The path from prototype to production when Copilot Studio runs out of road.
Azure AI Search
Hybrid vector + keyword search, semantic ranking, scoring profiles, integrated vectorisation. The retrieval layer behind every serious RAG application on Azure — and the one Copilot Studio quietly uses under the hood.
Document Intelligence
Pre-built and custom models for extracting structured data from invoices, contracts, forms, receipts, IDs. The right answer when SharePoint Premium + Syntex don’t cover your document type.
AI agents & orchestration
Multi-step agents that plan, call tools, branch on results, and write back to systems of record. Built on Foundry’s agent service or open-source frameworks (Semantic Kernel, LangGraph) deployed on Azure Container Apps.
Speech, Vision & Translator
Real-time speech-to-text and synthesis, custom voice, image and video analysis, 100+ language translation. The supporting cast for multi-modal workflows that pure text models can’t handle.
Four patterns we ship over and over.
Most SMB Azure AI builds fall into one of these shapes. We’ve hardened the structure across 20+ engagements.
Custom Copilot grounded on enterprise content
Branded conversational interface (Teams, website, or embedded), retrieving across 10K–500K documents in SharePoint, Confluence, file shares, ticketing systems. Hybrid search + reranking. Auth-aware so users only see what they should. Six-week build, including evals.
Document-processing pipeline
Email or SFTP intake → Document Intelligence extraction → LLM-based reasoning over extracted fields → write-back to system of record (Dynamics, Salesforce, NetSuite, Workday). Used for invoice intake, contract triage, claims processing, RFP scoring. Four to eight-week build.
Internal-facing agent with tool use
Multi-step agent that reads from your data warehouse, calls internal APIs, drafts an artefact (report, deck, model output), and posts it where the human can review. Eight-week build with hardening; runs as a scheduled or on-demand workflow.
Customer-facing voice or chat agent
Speech-enabled, multi-language, with safety rails, redaction, escalation logic, and CRM integration. The most operationally demanding pattern; we budget twelve weeks and a serious eval suite before production.
The 8-week shape of a first build.
Four phases, named exit criteria. The same discipline we apply to Copilot activation sprints, extended for the deeper build cycle Azure needs.
Weeks 1–2
Discovery & design
Workshop your top three candidate workflows. Pick one. Map data sources, access boundaries, eval criteria, and the smallest defensible v0 we can ship in eight weeks. Output: a one-page solution design + risk register signed by your COO/CTO.
Weeks 3–6
Build v0
Working application end-to-end in your Azure tenancy. Foundry prompt flows, retrieval pipeline, evals running on real test cases, baseline content-safety policies. Weekly demos against the eval suite. No surprise integrations late.
Weeks 7–8
Pilot & harden
Real users on real work. Eval scores tracked daily. Cost telemetry instrumented. Failure modes catalogued and either fixed or documented as known limitations. Cutover checklist signed.
Week 9+
Production & handover
Production deployment with monitoring, alerting, cost ceilings, and runbook authored by your team (not us). 90-day post-launch support and a roadmap for v1 features earned by v0 results.
How the math works out.
Same four ROI levers we use on Copilot engagements, applied to deeper Azure builds. See the full framework in our ROI write-up.
Hours saved
Document-processing pipelines routinely save 4–8 FTE-equivalents of manual data entry per pipeline. Custom Copilots save 20–40 minutes per knowledge worker per day on retrieval-heavy roles.
Revenue won
Internal-facing agents that triage and respond to inbound RFPs or sales enquiries 4–10× faster compress the top of the funnel materially. Documented impact: 18–34% increase in qualified pipeline at constant headcount.
Costs removed
Custom builds replace point-SaaS tooling ($30K–$150K/yr stacks) with a single governed Azure deployment, often at one-third the run-rate and with the data inside your tenant rather than a vendor’s.
Risk reduced
Azure’s private endpoints, customer-managed keys, audit logs, and content-safety pipeline mean an agent built here passes enterprise vendor-risk review where an off-the-shelf SaaS would not.
Frequently asked.
When does it make sense to build on Azure OpenAI vs. just buying Copilot?+
When you need custom retrieval over more content than Copilot can ground on (~10K+ documents start to expose limits), when you need an agent that takes actions outside the Office surface, or when you have data residency / IP / regulatory constraints that the shared Copilot infrastructure doesn’t meet. For most workflows below those thresholds, M365 Copilot or Copilot Studio is faster, cheaper, and lower-governance.
How do you compare to AWS Bedrock or Google Vertex AI?+
For SMBs already on Microsoft 365 and Entra, Azure’s identity, networking, and compliance posture composes with what you have rather than introducing a parallel stack. For greenfield builds or workloads where the specific model selection matters more than ecosystem fit, Bedrock and Vertex are legitimately competitive. We build on Azure 90% of the time because 90% of our SMB clients are already Microsoft-centric.
What does a typical Azure AI consulting engagement cost?+
A first production workflow — Custom Copilot or document pipeline — lands at $60–120K all-in for a 30–300-person company, plus Azure consumption (typically $500–$3,000/month at SMB scale). More ambitious patterns (multi-agent workflows, voice agents) start at $90K and scale with scope.
Do we need a data lake first?+
No. The "boil the ocean" data-foundation project that should precede an AI build is the most expensive way to never ship anything. We start with the data that exists where it lives — SharePoint, file shares, your CRM, your data warehouse — and only invest in consolidation when a specific workflow proves the need.
How do you handle evaluations and quality?+
Every build ships with an Azure AI Foundry eval suite specific to your domain: groundedness, relevance, fluency, custom business-logic checks, and red-team tests for prompt injection. Evals run on every commit and weekly against production. Without evals you don’t have an AI product — you have a demo that occasionally embarrasses you.
Not sure whether to buy more Copilot, or build on Azure?
The free 4-minute AI Readiness Assessment surfaces which of your candidate workflows fit Copilot, which need a custom Azure build, and which shouldn’t be AI workflows at all.