Cloud application architecture
Workload design across App Service, Container Apps, and Functions with clear boundaries, scaling behaviour, and failure modes.
Azure cloud and AI
Cloud architecture that holds up under load.
We design and build Azure workloads with a clear position on identity, integration, observability, and deployment — so the system is straightforward to run, safe to change, and honest about what it costs.
The problem
What usually brings people here
If more than one of these is familiar, they are probably connected. We look for the underlying cause before proposing a build.
- Systems that only one person understandsInfrastructure was created by hand, the diagram is out of date, and nobody is confident about what will break if a component is changed.
- Integrations that fail silentlyData moves between systems through scripts and scheduled jobs with no retry policy, no alerting, and no way to tell whether today's run succeeded.
- Identity and access as an afterthoughtShared accounts, long-lived secrets, and overly broad permissions accumulate because access was never designed as part of the architecture.
- No usable operational pictureWhen something is slow or failing, there is no consolidated view of logs, traces, and dependencies to work from.
Capabilities
What we build
Azure AI services
Document intelligence, search, language, and model integration applied where they solve a specific business problem.
API and integration services
API design and management, event-driven patterns, messaging, and reliable system-to-system integration with proper error handling.
Identity and access
Microsoft Entra ID, managed identities, least-privilege role assignment, and secret handling through Key Vault rather than configuration files.
Serverless workloads
Azure Functions and event-driven components for background processing, scheduled work, and integration endpoints that scale to zero.
Monitoring and observability
Application Insights, structured logging, health checks, dashboards, and alerts tied to the behaviour that actually matters to the business.
Delivery approach
How the work runs
A repeatable path from an unclear problem to a system someone owns. The lifecycle decisions happen early, where they are cheap.
Discover
Understand the workload, the integration surface, the compliance and residency constraints, and the operational reality of the team who will run it.
Design
Produce a target architecture with named services, an identity and network position, a deployment model, and an explicit cost shape.
Build
Implement with infrastructure as code, automated deployment, secure defaults, and instrumentation built in from the first environment.
Evolve
Review performance, cost, and reliability against real telemetry, then tune the parts of the system the data points at.
Technology ecosystem
Product names are used descriptively. The right combination is decided during design, not assumed at the start.
- Azure App Service
- Azure Functions
- Azure Container Apps
- Azure API Management
- Azure AI services
- Azure Key Vault
- Microsoft Entra ID
- Application Insights
- Bicep
Outcomes
What changes afterwards
Reproducible environments
Infrastructure defined as code means a new environment is a deployment rather than a project.
Integrations you can trust
Retries, dead-letter handling, and alerting turn silent failures into visible, actionable events.
A defensible security position
Managed identities, least privilege, and centrally managed secrets replace shared credentials and configuration sprawl.
Cost you can explain
Service choices, scaling rules, and tagging make the monthly bill traceable to specific workloads.
Related work
Automation • Azure • AI Agents
Document Automation Service
Regulated services provider
An Azure service that classifies and extracts data from inbound documents, routes low-confidence cases to a human, and writes verified results into the business system.
View case study
Data & Power BI • Azure
Executive Intelligence Hub
Regional services group
A certified semantic model and executive reporting layer that ended the reconciliation debate, replacing conflicting spreadsheets with one agreed set of measures.
View case study
Related insights
AI Agents
Governing enterprise AI agents
A practical approach to grounding, permissions, evaluation, and human oversight — the four decisions that separate an agent pilot from an agent in production.
Ahmed Salih5 min read
Questions
Frequently asked
For most business workloads, managed services are the better default. App Service and Azure Functions remove a large amount of operational work, and Container Apps covers the middle ground when you need container packaging without managing a cluster. Kubernetes earns its complexity when you have many services, specialised networking, or an existing platform team already running it. We choose based on the team who will operate the system, not on what is most interesting to build.
Managed identities wherever the service supports them, so there is no credential to rotate or leak. Where a secret is unavoidable, it lives in Key Vault and is referenced by configuration rather than copied into application settings, source control, or a deployment script. Access is granted by role to a specific identity rather than shared across a team.
Yes, and it is usually the more common starting point. We begin by documenting what exists — subscriptions, networking, identity, and deployment practice — then agree what stays, what gets brought under infrastructure as code, and what is genuinely worth changing. Wholesale re-platforming is rarely the right first move.
Cost is a design decision. Service tier, scaling rules, data egress, and storage lifecycle drive most of the bill, so we make those explicit during design rather than discovering them in the first invoice. After launch, tagging and budget alerts make it possible to attribute spend to a workload and catch a change in trajectory early.
Let's talk
Let's talk about Azure Cloud & AI.
Bring the problem rather than a specification. A short conversation is usually enough for us to tell you what we would do first — and whether we are the right people to do it.
Prefer email? info@aqlyst.ai