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Microsoft Azure Cloud Services

Microsoft Azure Consulting and Architecture for the Enterprise

We architect, migrate, and operate Azure environments aligned to the Microsoft Cloud Adoption Framework - delivering Landing Zones, AKS platforms, Azure AI integrations, and enterprise data platforms that meet the security, governance, and performance standards of regulated organisations.

ISO 27001SOC 2 ReadyNDA Day 1MSA AvailableIP Protection

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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

The Challenge

Unstructured Azure Adoption Creates Compounding Technical and Compliance Debt

Most organisations that moved workloads to Azure opportunistically now carry fragmented subscription structures, inconsistent policy enforcement, and no clear security baseline. Without a deliberate architecture foundation, every new workload adds complexity, every audit surfaces new gaps, and cloud spend grows without corresponding business value. The cost of remediation scales non-linearly the longer foundational work is deferred.

68%
of enterprises report Azure governance gaps discovered during audits
3.4x
cost overrun on unplanned cloud migrations vs. structured programmes
$2.1M
average annual waste from oversized and untagged Azure resources
47%
longer time-to-market when CI/CD pipelines are absent or fragmented

Why QuickHire

Why Enterprises Choose QuickHire

01

CAF-Aligned Architecture

Every engagement is structured against the Microsoft Cloud Adoption Framework, ensuring your Azure environment has a defensible, auditable architecture rationale. We produce Architecture Decision Records for every major design choice.

02

Security-First by Design

Security controls are embedded from day one - not retrofitted. We implement Defender for Cloud, Sentinel, and CIS benchmark policies before any workload is onboarded to your environment.

03

Azure AI and OpenAI Expertise

Our team designs production-grade Azure OpenAI and Azure AI Services integrations with private networking, responsible AI governance, and enterprise-grade observability built in from the start.

04

Enterprise Data Platform Depth

We architect modern data platforms on Synapse Analytics, Azure Data Factory, and Azure Databricks, with Purview-governed data catalogues and medallion lake architectures supporting real-time and batch analytics.

05

Infrastructure as Code Discipline

All Azure resources are defined in Terraform or Bicep and managed through Azure DevOps or GitHub Actions pipelines, eliminating configuration drift and enabling repeatable, auditable deployments.

06

FinOps Embedded in Delivery

Cost governance is a design constraint, not an afterthought. We model Reserved Instance commitments, enforce tagging policy, and deliver Power BI FinOps dashboards so finance and engineering share a single cost truth.

Challenges

Common Enterprise Pain Points

01

Uncontrolled Subscription Sprawl

Subscriptions created ad-hoc for projects accumulate without management group hierarchy, making policy enforcement, cost visibility, and access control unmanageable at scale. Remediation requires careful restructuring to avoid service disruption.

02

Insecure Public Endpoints on PaaS Services

Azure Storage, SQL, and Key Vault instances exposed to public endpoints represent significant data exfiltration risk that is often invisible until a security assessment is conducted. Migrating to private endpoints requires coordinated network and application changes.

03

Lift-and-Shift IaaS Without Optimisation

Workloads migrated as direct VM lifts often run overprovisioned, lack autoscaling, and carry on-premises operational patterns that do not exploit Azure-native capabilities. Right-sizing and replatforming require workload-specific analysis.

04

Fragmented DevOps Toolchains

Teams using a mix of Azure DevOps, GitHub, Jenkins, and manual deployments create inconsistent release processes, audit gaps, and inability to enforce security scanning gates uniformly across projects.

05

Data Silos Blocking Analytics Initiatives

Business units with independent data stores, incompatible schemas, and no shared integration layer prevent enterprise analytics programmes from delivering value. Establishing a governed data platform requires both technical architecture and organisational alignment.

Our Approach

A Structured Azure Consulting Programme That Builds Durable Cloud Capability

Our Azure consulting engagements deliver a governed, secure, and cost-optimised cloud foundation aligned to the Microsoft Cloud Adoption Framework - not just a collection of deployed resources. We combine deep Azure platform expertise with a repeatable delivery methodology that builds your internal team's capability in parallel with each phase of implementation.

01
Azure Landing Zone Foundation
We deploy a policy-driven Landing Zone with management group hierarchy, hub-and-spoke or Virtual WAN networking, and security baseline policies using Azure Verified Modules and infrastructure as code.
02
Workload Migration and Modernisation
Workloads are assessed by the six Rs, migrated in risk-sequenced waves, and optimised for Azure-native capabilities including autoscaling, managed identities, and PaaS replatforming.
03
Azure AI and Data Platform Build-Out
We implement Azure OpenAI, Azure AI Services, Synapse Analytics, and Azure Data Factory within governed, private-network architectures with end-to-end data lineage via Microsoft Purview.
04
Continuous Governance and FinOps
Defender for Cloud, Azure Policy, and Cost Management are configured from day one and handed over with operational runbooks, dashboards, and trained internal teams ready to sustain the environment.

Delivery Models

How We Deliver

Azure Foundation Sprint

Focused Landing Zone, security baseline, and identity architecture delivery for organisations establishing their first production Azure environment.

Timeline
6 weeks
Team Size
3-4 engineers
Migration Programme

End-to-end workload assessment, wave-based migration execution, and post-migration optimisation for organisations moving 20 or more applications to Azure.

Timeline
12-24 weeks
Team Size
5-8 engineers
Azure AI and Data Platform

Design and implementation of Azure OpenAI integrations, RAG pipelines, Synapse Analytics, and Azure Data Factory within a governed enterprise data architecture.

Timeline
8-16 weeks
Team Size
4-6 engineers

Capabilities

Technical Capability Matrix

Cloud Architecture and Migration
Azure Landing ZonesCAF Strategy and PlanningAzure Migrate AssessmentsSix-R Workload ClassificationBrownfield Remediation
Platform Engineering and DevOps
AKS Design and HardeningAzure DevOps PipelinesGitHub Actions on AzureTerraform and Bicep IaCGitOps with Flux and Argo CD
AI and Data Services
Azure OpenAI IntegrationAzure AI Search and RAGSynapse AnalyticsAzure Data FactoryMicrosoft Purview Governance
Security and Governance
Microsoft Defender for CloudMicrosoft SentinelEntra ID and PIMAzure Policy AuthoringCIS Benchmark Compliance

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Discovery and Assessment

Days 1-5

We conduct stakeholder interviews, Azure environment discovery, and workload inventory to produce a current-state assessment and target architecture recommendation.

2

Architecture Design and Review

Week 2

Our architects produce a detailed solution design covering networking, identity, security, and workload placement, reviewed against Well-Architected Framework pillars.

3

Foundation Deployment

Weeks 3-4

Landing Zone, policy assignments, networking, and security baseline are deployed as infrastructure as code with all configurations version-controlled from the first commit.

4

Workload Onboarding and Migration

Weeks 5-20

Workloads are onboarded in risk-sequenced waves, with parallel-run periods and validated rollback procedures for each migration cohort.

5

Optimisation and Handover

Ongoing

Post-migration right-sizing, FinOps dashboard deployment, operational runbook handover, and knowledge transfer sessions complete the engagement.

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Security & Compliance

Enterprise-Grade Security by Default

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

Governance

Programme Governance

✓

Architecture Decision Records

Every material design decision is documented in an ADR with context, options considered, and rationale - creating a living audit trail for internal and external reviewers.

✓

Azure Policy as Code

All governance policies are defined in Terraform or Bicep and stored in version control, ensuring policy assignments are reproducible, reviewable, and change-managed.

✓

Sprint-Level Stakeholder Reviews

Bi-weekly sprint reviews present completed deliverables, updated risk register, and next-sprint scope to keep executive sponsors aligned without requiring technical depth.

✓

Well-Architected Framework Reviews

We conduct WAF assessments at project midpoint and completion across all five pillars - reliability, security, cost optimisation, operational excellence, and performance efficiency.

Team Structure

Your Enterprise Team

Our Azure consulting teams combine Microsoft-certified cloud architects, security engineers, and data platform specialists who have delivered CAF-aligned programmes for enterprises across financial services, healthcare, and public sector. Each engagement is led by a Principal Architect responsible for design integrity, supported by engineers and a FinOps specialist embedded from day one.

✓Principal Azure Architect
✓Azure Security Engineer
✓AKS Platform Engineer
✓Azure DevOps Engineer
✓Azure Data Platform Engineer
✓Azure AI and ML Engineer
✓FinOps Analyst
✓Engagement Manager

Project Lifecycle

From Kickoff to Production

01
1-2 weeks

Strategy and Assessment

Current-state assessment, TCO analysis, target architecture proposal, CAF strategy document.

02
3-6 weeks

Foundation Build

Azure Landing Zone, policy baseline, networking topology, identity architecture, security baseline - all as IaC.

03
8-20 weeks

Workload Migration or Build

Migrated or newly built workloads in production Azure environment with DR configurations and runbooks.

04
6-12 weeks

AI and Data Platform

Synapse workspace, ADF pipelines, Azure OpenAI integration, Purview catalogue, and analytics dashboards.

05
Ongoing

Operate and Optimise

Monthly WAF reviews, cost optimisation reports, Defender for Cloud Secure Score tracking, and retained advisory.

Case Studies

Enterprise Outcomes

Financial Services

A regional bank needed to migrate 60 on-premises applications to Azure within 12 months to exit an expiring data centre contract.

We delivered a CAF-aligned Landing Zone in five weeks, executed three migration waves using Azure Migrate, and replatformed 14 applications onto Azure SQL and App Service.

34%reduction in infrastructure operating cost in year one
Healthcare

A healthcare network required a compliant Azure data platform to consolidate clinical and operational data from six source systems for population health analytics.

We implemented a Synapse Analytics medallion architecture with Purview governance, row-level security, and Azure Data Factory pipelines, delivered in 14 weeks.

$1.8Min deferred legacy data warehouse licensing costs
Retail

A multi-brand retailer needed to integrate Azure OpenAI into their customer service platform to reduce agent handling time and improve self-service resolution rates.

We deployed a private-endpoint Azure OpenAI environment with RAG against Azure AI Search and a semantic kernel orchestration layer integrated into the existing contact centre platform.

41%reduction in average customer service handling time

Start Your Engagement

Ready to Build Your Enterprise Engineering Team?

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Hiring Models

One platform, two ways to hire

Not ready for a long-term commitment? QuickHire Instant lets you book a vetted engineer in 10 minutes - no contracts required.

Both models use the same vetted talent network · PM always included · Multi-country billing

Frequently Asked Questions

The Microsoft Cloud Adoption Framework (CAF) is a structured methodology covering strategy, planning, readiness, adoption, governance, and management of Azure workloads. Our engagements are aligned to CAF phases so every decision - from landing zone design to policy enforcement - has a defensible rationale traceable to Microsoft best practice. This alignment reduces audit risk, accelerates time-to-production, and ensures your organisation is positioned for ongoing Well-Architected reviews. It also provides a shared vocabulary between your internal teams, Microsoft account teams, and our consultants.
An Azure Landing Zone is a pre-configured, policy-driven environment that provides the networking, identity, management, and security scaffolding required before any production workload is onboarded. We deploy Landing Zones using Azure Verified Modules and Terraform or Bicep, with hub-and-spoke or Virtual WAN topology chosen based on your connectivity requirements. A greenfield Landing Zone for a mid-market enterprise typically takes four to six weeks, encompassing management group hierarchy, policy assignments, RBAC roles, and connectivity to on-premises environments. Brownfield remediation of an existing subscription environment is scoped separately after an assessment.
We follow a Discover, Assess, Pilot, Migrate, and Optimise sequence anchored to Azure Migrate and the CAF Migrate methodology. Discovery uses Azure Migrate appliances combined with dependency mapping to produce an accurate inventory and TCO comparison. We then categorise workloads by the six Rs - rehost, replatform, refactor, rearchitect, rebuild, retire - and sequence migrations to minimise risk and business disruption. Post-migration optimisation addresses right-sizing, Reserved Instance purchasing, and operational runbooks before handover.
Our AKS practice covers cluster design, multi-tenancy patterns, network policy enforcement with Calico or Azure CNI Overlay, GitOps-driven delivery with Flux or Argo CD, and integration with Azure Container Registry and Azure Key Vault via the Secrets Store CSI Driver. We implement node pool autoscaling, cluster autoprovisioner, and Vertical Pod Autoscaler to optimise cost and performance. Security hardening includes Microsoft Defender for Containers, pod identity replacement with Azure Workload Identity, and CIS benchmark compliance. We also assist with AKS upgrade strategies and blue-green cluster migration patterns.
We design Azure OpenAI deployments that respect enterprise data residency, compliance, and access control requirements - ensuring models are accessed through private endpoints within your virtual network. Our integration patterns include retrieval-augmented generation (RAG) with Azure AI Search, semantic kernel orchestration, and tool-use patterns for agentic workflows. We implement token-based rate limiting, prompt logging to Log Analytics for auditability, and content filtering policies aligned to your responsible AI governance standards. Post-deployment, we provide monitoring dashboards for latency, cost per token, and prompt rejection rates.
Our Azure security baseline service implements controls mapped to the Azure Security Benchmark v3 and, where required, CIS Microsoft Azure Foundations Benchmark. Deliverables include Microsoft Defender for Cloud activation across all subscriptions, Secure Score remediation roadmap, Defender for Servers and Defender for SQL enablement, and Azure Policy assignments enforcing encryption, diagnostic settings, and approved SKUs. We configure Microsoft Sentinel with Azure-native data connectors, analytic rules, and SOAR playbooks for common incident types. A final report documents the before-and-after Secure Score, residual risk items, and a prioritised remediation backlog.
Yes - our Azure DevOps engagements cover pipeline architecture for CI/CD, branch strategy governance, environment management with deployment gates and approvals, and self-hosted agent pool design for private network builds. We integrate Azure DevOps with Boards, Test Plans, and Artifacts to create an end-to-end software delivery lifecycle. For teams transitioning from Jenkins or GitLab, we provide migration tooling and parallel-run periods to de-risk cutover. We also establish pipeline templates in YAML as reusable assets, reducing per-project boilerplate and enforcing security scanning gates.
Azure Data Factory serves as the orchestration layer for data movement and transformation within a medallion architecture hosted on Azure Data Lake Storage Gen2 or Synapse Analytics. We design ADF pipelines with parameterised linked services, managed virtual network integration runtime for secure on-premises connectivity, and mapping data flows for code-free transformation of large datasets. Pipeline monitoring is configured with Azure Monitor alerts and Log Analytics dashboards to detect SLA breaches. We complement ADF with Azure Databricks for complex Spark workloads and Synapse Pipelines where native Synapse integration is preferred.
We embed FinOps practices from the initial architecture phase rather than treating cost as a post-deployment concern. During design, we apply Azure Advisor recommendations, select the correct VM SKUs, and model Reserved Instance and Savings Plan commitments against your projected consumption. Post-deployment, we configure Azure Cost Management budgets and anomaly alerts, tag governance enforced via Azure Policy, and monthly cost reviews against allocated budgets by business unit. Chargeback and showback reports are built in Power BI connected to the Cost Management export to ADLS, giving finance teams granular visibility without Azure portal access.
We design identity architecture using Microsoft Entra ID as the authoritative identity provider, with Privileged Identity Management (PIM) for just-in-time elevation of privileged roles. Management group and subscription-level RBAC is defined as code using Terraform AzureRM or Bicep, ensuring all role assignments are version-controlled and auditable. For hybrid environments we configure Entra ID Connect with password hash sync or passthrough authentication, and Entra Application Proxy for legacy application access. Cross-tenant B2B collaboration policies and Conditional Access baselines are configured to enforce MFA and device compliance for all administrative access.
For organisations with multiple regions or business units we recommend Azure Virtual WAN with Secured Virtual Hubs to centralise routing, firewall inspection, and branch connectivity. Smaller footprints or those requiring deep packet inspection with custom rule sets benefit from a traditional hub-and-spoke topology with Azure Firewall Premium. Private endpoints are mandated for all PaaS services - Storage, SQL, Key Vault, Service Bus - to eliminate data exfiltration risk through the public internet. ExpressRoute with GlobalReach is recommended where latency to on-premises data centres is a critical application requirement, with VPN Gateway as a resilient failover path.
Our Synapse practice covers workspace provisioning with managed private endpoints, dedicated SQL pool design with distribution and partitioning strategies for optimal query performance, and Spark pool configuration for large-scale data processing. We implement row-level security and dynamic data masking in dedicated SQL pools to enforce data access policies across business units. Integration with Azure Purview provides data cataloguing and lineage tracking, critical for regulated industries. We also design Synapse Link connections to Azure Cosmos DB and Dataverse for near-real-time operational analytics without impacting transactional systems.
We design BCDR architectures aligned to your RTO and RPO requirements, validated through structured Failure Mode and Effects Analysis (FMEA) of each workload tier. Azure Site Recovery is configured for IaaS virtual machine replication, with automated failover runbooks and regular DR drills documented as version-controlled procedures. For PaaS workloads, we implement geo-redundant storage, active geo-replication for Azure SQL, and Traffic Manager or Azure Front Door for DNS-level failover. All BCDR configurations are tested through chaos engineering exercises and documented in a Business Continuity Plan that satisfies ISO 22301 and common regulatory audit requirements.
Our Azure AI practice covers Azure AI Document Intelligence for structured and unstructured document extraction, Azure AI Language for entity recognition, sentiment analysis, and custom text classification, and Azure AI Vision for image analysis and OCR at scale. We integrate Azure AI Search with semantic ranking and vector search capabilities as the knowledge retrieval layer for enterprise RAG pipelines. Azure Machine Learning is used for custom model training, MLOps pipeline automation, and responsible AI fairness assessment. All AI services are deployed within private networks with customer-managed encryption keys and audit logging to meet enterprise data governance requirements.
Engagement duration depends on scope. A focused Azure Landing Zone and security baseline engagement typically runs six to eight weeks. A full-scale migration of 50 or more workloads with data platform build-out runs six to nine months. We structure all engagements in two-week sprints with clear milestone gates, allowing scope adjustments based on discoveries made during the engagement. Each sprint closes with a stakeholder demo, updated Architecture Decision Records, and a risk register review so your leadership team maintains full visibility throughout the programme.
Every engagement includes a dedicated knowledge transfer workstream running in parallel with delivery, not bolted on at the end. We produce Architecture Decision Records for every major design choice, runbooks for operational procedures, and annotated Terraform or Bicep repositories with inline documentation. Your internal engineers pair with our consultants during implementation sprints to build hands-on capability. Post-engagement, we offer a 30-day hypercare period with priority support SLAs, followed by optional retained advisory services for ongoing governance reviews, architecture consultation, and cost optimisation cycles.
Industries
Financial ServicesHealthcare and Life SciencesPublic SectorRetail and E-CommerceManufacturing