Service
Cloud, Data & AI
Migration and modernisation, a data layer you can trust, and AI applied only where it genuinely helps. We build on Azure, Postgres and open standards, and we hand you the environment, the pipeline and the runbook so your team can run it.
What you might recognise
Cloud spend is rising and nobody can explain which workload is responsible
Two departments report the same figure differently and both are defensible
Reporting is a person exporting to Excel every Monday morning
A lift-and-shift moved the servers but none of the problems
Someone wants to add AI, and nobody can say to what, or how it would be checked
The challenge
Most cloud programmes move the workload. Far fewer change what it costs to run.
Lifting a server into a hosted VM changes the invoice, not the operating model. The backups are still manual, the deployment is still someone's laptop, and the only person who can restore the environment is on annual leave. Meanwhile the data question goes unanswered: there is no agreed definition of a customer, so every report is arguable.
We treat cloud, data and AI as one problem, because they fail as one. The platform has to be reproducible, the data has to have an owner and a definition, and any AI has to show its sources and route through a human before it touches a decision that matters. We will tell you when a well-built report answers the question better than a model would.
What we do
The work itself
Migration and modernisation
Assessment, landing zone, migration and the decommissioning nobody budgets for — with the operating model changed, not just the hosting.
Environments and delivery pipeline
Reproducible environments, automated build and deploy, and monitoring set up so your team can operate it without depending on us.
Data platform and modelling
A single agreed definition for the entities that matter, a modelled data layer, and lineage so a disputed number can be traced to its source.
Reporting and analytics
Dashboards built on the modelled layer rather than on exports, with the awkward but necessary work of agreeing what each metric actually means.
Applied AI with human review
Summarisation, extraction, classification and retrieval on your own documents — every output carrying its sources and requiring approval before it is acted on.
Cost, resilience and security
Right-sizing and cost attribution, backup and recovery actually tested, identity and access reviewed rather than assumed.
What changes
A platform your team can operate, and numbers nobody argues with
What we design towards on every cloud and data engagement.
Environments rebuilt from code, not from one person's memory
Cloud spend traceable to the workload and team responsible
One definition per metric, with lineage back to the source system
AI outputs that show their sources and pass through human approval
How we engage
Three ways to start
Pick the smallest one that answers your question. Every engagement is designed so you can stop, change direction or change supplier at the end of it.
Technical review
A short assessment of your current platform, data estate and spend, ending in a prioritised list of what to fix and what to leave alone.
- Architecture and resilience review
- Cost and right-sizing analysis
- Data quality and ownership gaps
- A sequenced plan
Migration and build
Landing zone, migration waves, pipeline and monitoring, with decommissioning of the old estate included rather than quietly dropped.
- Landing zone and guardrails
- Wave-based migration
- Pipeline and monitoring
- Decommissioning and handover
AI proof of value
One narrow, high-volume task, evaluated honestly against doing it the current way — including the outcome where the model does not earn its place.
- One task, clearly scoped
- Measured against the manual baseline
- Human review built in
- A go or no-go recommendation
Our approach
How the work runs
We sequence platform, then data, then AI — in that order. Applying a model to data nobody trusts produces confident answers that are wrong.
Assess what is actually running
Workloads, dependencies, spend and the recovery position. We test whether the backups restore, which is not always the answer people expect.
Build the landing zone
Identity, network, guardrails and cost attribution set up before any workload moves, so the estate stays governable as it grows.
Migrate in waves
Lowest-risk workloads first to prove the pattern, then progressively more critical ones, each with a tested rollback.
Model the data
Agree the definitions with the people who argue about them, build the modelled layer, and expose lineage so disputes resolve against the source.
Prove AI narrowly, if at all
One task, measured against the current manual baseline, with human review in the loop. We report honestly when it does not pay for itself.
Hand over the keys
Runbooks, pipeline, monitoring and cost dashboards transferred to your team, with a defined support taper rather than an open-ended retainer.
Where it meets our products
Advice from people who ship the software
Most consultancies stop at the recommendation. Because we build and operate our own platforms, this service is informed by the migrations, go-live weekends and support calls we have had to handle ourselves.
Enluka EMR
Built on Postgres with access rules as row-level policies — the pattern we bring to any system holding data that must not leak between roles.
Explore Enluka EMREnluka LexSuite
Where our applied-AI position is clearest: answers are permission-aware, show their sources, and require human approval before use.
Explore Enluka LexSuiteRelated services
What usually runs alongside
Bring us the number two teams disagree about
It is usually the fastest way into the real problem — the definition, the source system, and whoever owns neither. Ninety minutes is normally enough to find it.