Taking complicated technical requirements and building working systems around them.

Some problems do not have a product. The requirement is specific to how one business actually works, the off-the-shelf options each solve eighty percent of it, and the remaining twenty percent is the part that matters.

That is the work: business applications, internal systems, APIs, background processing, integration services, dashboards, administrative tools, data-driven applications, automation and developer tools — designed around the requirement rather than around a product someone is already paying for.

This ranges from a small, well-built professional website through to a full application with APIs, databases, authentication and cloud infrastructure behind it.

Both ends of that range are real engineering. A brochure site still needs to be fast, accessible, maintainable and deployed properly; an application needs all of that plus data modelling, identity and infrastructure. Websites are one capability here, not the whole business.

Most software that matters is already running. It was written to a deadline, by people who have moved on, against a version of the business that has since changed. It still works, which is exactly why replacing it wholesale is rarely the right answer.

The work is incremental: moving legacy .NET applications onto supported runtimes and modern Azure hosting, replacing obsolete front-end frameworks, modernising authentication, paying down the technical debt that makes every change expensive, refactoring the components that everybody is afraid of, and reworking deployment so a release stops being an event.

The constraint that shapes all of it is that the application has users today. Each step has to leave the system working, which means understanding what is actually there before changing it — not assuming the previous team was wrong.

Substantial experience building and running systems in Azure: Functions, App Service, Static Web Apps, Container Instances, Azure SQL, Storage across blobs, tables and queues, Service Bus, SignalR, API Management, Azure DNS, Microsoft Entra ID, Managed DevOps Pools and Azure DevOps Environments.

Just as important is designing infrastructure that spans environments — development, QA, demo, staging and production — so that what runs in production is the same shape as what was tested, and a new environment is something you create rather than something you negotiate.

Infrastructure that was configured by hand is infrastructure nobody can confidently rebuild, and that uncertainty quietly shapes every decision made around it.

Defining it in code — Pulumi or Terraform, with the Azure CLI, PowerShell and Docker around it — makes an environment reviewable, reproducible and disposable. The work covers provisioning cloud resources, multi-environment deployments, secrets and configuration, CI/CD, repeatable infrastructure and environment isolation.

Designing and implementing the automation that gets code from a pull request to a running environment: GitHub Actions and Azure DevOps pipelines, branch protection, pull request workflows and required checks, automated deployments, and release workflows.

The parts that are easy to skip matter most — Azure authentication, service principals, deployment identities and environment-specific configuration. A pipeline that deploys using a long-lived secret somebody pasted in once is not automation, it is a liability with a green tick next to it.

Most businesses already own the systems they need. What they do not have is the wiring between them, so somebody ends up exporting a spreadsheet from one and typing it into another.

This covers accounting integrations, email integrations, authentication providers, cloud services and third-party APIs, plus data synchronisation, workflow automation and bespoke connectors where nothing off the shelf fits.

E-PATH builds and uses AI-enabled developer tooling on its own work: coding-agent integrations, MCP servers, semantic code search and indexing, persistent agent memory, LLM-assisted workflows, local LLM integration, automated data retrieval and workflow orchestration.

The framing here is deliberate. The useful question is not whether a model is involved, it is whether a task that used to need a person now runs reliably and inside a boundary somebody designed. An MCP interface that gives an agent a set of reviewable operations is engineering; handing a model raw API credentials is not.

Not every engagement needs someone to write the code. Sometimes a team already knows how to build the thing and wants to be sure it is building the right one — or has built it, and wants to know what an experienced outsider sees.

That covers architecture reviews, technology selection, feasibility assessments, code review, cloud solution design, performance investigations, reviews of development and deployment workflow, and technical mentoring alongside a team rather than instead of one.

The useful version of this is specific and written down: what the trade-off actually is, what it would cost to change later, and which option is defensible given the constraints the team is really working under. E-PATH can take this on as a short, bounded piece of work or stay involved through the implementation that follows.