Where the system runs, and what moves through it
Cloud architecture, environments and deployment on one side; pipelines, warehousing, APIs and integration into systems of record on the other. Two disciplines that fail together, which is why we do not sell them separately.
Infrastructure that holds under production load
AWS, Azure and Google Cloud, with Kubernetes where the workload justifies it and without it where it does not.
Cloud architecture
Networking, compute, storage, identity and the boundaries between environments, designed around the product rather than around whichever managed service was in the last conference talk.
Environments and infrastructure as code
Development, staging and production defined in code and reproducible. If an environment can only be recreated by the person who built it, you have a single point of failure with a payroll number.
Deployment and release
Automated pipelines, staged rollout and a rollback that has actually been exercised. The point of a release process is that it is boring.
Observability and alerting
Logs, metrics, traces and alerts on the paths that carry revenue, instrumented on open standards so you are never locked into whoever is monitoring you.
Resilience and recovery
Backups that have been restored from, failure modes that have been rehearsed, and a written recovery expectation rather than an assumed one.
Cost engineering
What the estate costs, which components drive it, and where an architectural change is worth more than another round of instance resizing.
Pipelines, models and the integrations underneath them
The layer underneath that decides whether reporting, automation and any AI you later buy are possible at all.
Pipelines and ingestion
Moving data from the systems that create it into the places that use it, with schedules, retries, schema handling and a way to tell when a load silently did nothing.
Warehousing and modelling
A structure the business can query without three caveats per number, and definitions that survive somebody renaming a column.
APIs and service interfaces
Documented, versioned access to data for the applications, partners and teams that need it, with authentication and entitlements designed first.
Integration into systems of record
CRM, ERP, finance, case management and document stores. Most data problems are integration problems wearing a reporting costume.
Quality, lineage and provenance
Where a figure came from, which source, which version, at what time. Your auditor will ask, and so will the first person who disagrees with the number.
Model-serving layers
Where AI is part of the product, the serving, caching and evaluation hooks it needs, built as infrastructure rather than bolted to the application.
A model cannot use data it cannot reach
Automation and AI projects stall on access far more often than on capability: the information sits in four systems, the permissions were never modelled, and nobody can say which copy of a record is authoritative. That is a data and integration problem, and it is solvable by ordinary engineering.
Doing it first also has the useful property of being valuable on its own. Better reporting and fewer manual reconciliations pay for themselves whether or not a model is ever pointed at the result.
- 01Assess the estate
What runs where, what it costs, and what is fragile
- 02Environments and pipeline
Reproducible infrastructure and a dull release
- 03Data layer
Ingestion, modelling, entitlements and lineage
- 04Observability and cost
Alerting on what matters, then the spend curve
Questions worth answering
Which cloud platforms do you work in?
AWS, Azure and Google Cloud, with Kubernetes where the workload justifies it. The platform is usually a constraint set by the client rather than a preference we impose, and the architecture is designed around the product and its operating requirements either way.
What is the difference between cloud engineering and data engineering here?
Cloud engineering is where the system runs: environments, deployment, observability, resilience and cost. Data engineering is what moves through it: pipelines, warehousing, APIs, integration into systems of record, and the lineage that lets you defend a number. Most engagements need both, which is why they sit on one page.
Do you do this work without also building the application?
Yes. Cloud architecture, environment rebuilds, pipeline work and integration layers are all deliverable against an existing estate, and are a common first engagement where the application itself is sound but the infrastructure or data layer underneath it is not.
Estate not behaving?
Tell us what runs where and what it is costing you, in money or in Fridays. We will tell you which layer is actually the problem.