Typical engagements

Six problems we are often asked to solve.

Each one describes the situation, how we usually approach it, and which part of our work it draws on. If yours is close to one of these but not quite, that is normal.

For review before launch

These describe engagement types, not completed client projects.

01

Intelligent Process Automation

Invoices, claims, or support tickets that people copy between systems by hand. We automate the routine steps and leave the judgment calls to people.

We map the process before choosing tools. Fixed rules stay fixed rules. AI handles only the parts that need interpretation, such as classifying a free-text request, and anything with consequences goes through an approval step.

Workflow designHuman oversight
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02

Cloud Modernization & Optimization

Moving workloads out of a data center, or tidying up a cloud estate that grew faster than anyone planned, including the bill.

We map dependencies first, then sequence the migration so the riskiest systems are not moved first. Cost is reviewed alongside architecture: idle resources, oversized clusters, and missing tagging.

Cloud architectureModernization
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03

AI-Driven IT Operations

Help for on-call engineers: pull together the relevant logs, metrics, and runbook for an incident, and suggest a next step.

We test suggestions against past incidents before anyone relies on them. Restarting a service or changing configuration stays behind an explicit human approval.

ObservabilityControlled automation
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04

Enterprise AI Applications

An assistant that answers from your own policies, contracts, or technical documentation, and only shows each user what they are allowed to see.

Retrieval, permissions, and evaluation are designed together. We start with one team and one document set, and measure answer quality on real questions before rolling out further.

Enterprise integrationAI applications
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05

Data Platform Modernization

Replacing an ageing warehouse or a tangle of scripts with a lakehouse, without a big-bang cut-over.

We list sources and consumers, agree data contracts for the critical datasets, and migrate one domain at a time. Old and new run side by side with reconciliation checks until the numbers match.

Data architectureLakehouse
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06

Platform Reliability & Engineering

Fewer surprises in production: health checks that mean something, alerts someone acts on, and a restore that has actually been tested.

We define service objectives with the business owner, move manual changes into code, and run a restore exercise with your team. Developer experience is part of the scope, because a platform people work around is not reliable.

ReliabilityPlatform engineering
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Describe the situation in a few lines: what runs today, what is going wrong, and any deadline. We will reply with the questions we would ask first.

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