Cloud Transformation

Make cloud a better operating model. Not a bigger bill.

Modernise applications, build reliable infrastructure and bring cloud economics under control. Every decision starts with what your business actually needs.

Cloud control roomOrder platform · data centre to AWS

Data centre

AWS · eu-west-2Guardrails on

Web
Services
Data
  • Rehost
  • Replatform
  • Refactor

Workloads in cloud0 / 8Monthly bill$148KAvailability99.95%
2000+
Workloads migrated
30%
Typical cloud cost reduction
99%
Availability we design for
0
Big-bang cut-overs

The problems we solve

If this sounds familiar, your cloud is a cost centre, not a platform.

Cloud value gets lost in migrations without an operating model. Here is what we hear from platform and engineering leaders, and what changes.

“The cloud bill grows every month and nobody owns it.” Resources are oversized, idle or orphaned, with no showback to teams.
What changes

FinOps from day one: tagging standards, budgets per team, right-sizing and alerts before the bill lands.

“We lifted and shifted, but nothing improved.” Same bottlenecks, higher opex, and the business sees no speed gain.
What changes

Modernisation where it pays: re-architect the hot paths, automate delivery, measure lead time and reliability.

“Security says no, so teams build around them.” Guardrails arrive late, so engineers create shadow resources and drift.
What changes

Guardrails as code: landing zones, policy-as-code and paved roads that make the secure path the easy path.

“One region outage takes us down.” Architecture is single-region by design and recovery has never been tested.
What changes

Resilience engineering: multi-AZ by default, tested failover, and recovery objectives you can prove.

“Every team deploys differently.” Inconsistent pipelines, manual steps and releases that depend on one person.
What changes

A golden path: standardised CI/CD, infrastructure as code and environments that are identical everywhere.

Where cloud isn’t the answer

Not every workload needs to move. Not every system needs rewriting.

Good cloud advice includes knowing when to leave something alone. These are the patterns we steer clients away from, and what we do instead.

  1. 01

    Migration without a business case

    Changing where something is hosted is not an outcome.

    InsteadStart from the outcome, then decide what moves.
  2. 02

    Refactoring for its own sake

    Modernisation has to justify the disruption it causes.

    InsteadRefactor only where the change pays back.
  3. 03

    Cloud-native by default

    Unnecessary complexity has a running cost.

    InsteadThe simplest architecture that meets the need.
  4. 04

    Cost savings without ownership

    Spend creeps back when no team is accountable for it.

    InsteadCost visible to, and owned by, every team.
  5. 05

    Big-bang cut-overs

    Risk should be designed down before production, not during it.

    InsteadWaves, each with a tested rollback.

What we offer

Cloud foundations built to operate.

Modernise securely, control spend and give teams repeatable ways to deploy and run services.

01

Application & legacy modernisation

Rehost, replatform, refactor or replace workloads where change creates real value.

You getModernised workloads moved in waves
  • Containers
  • Managed services
  • Strangler pattern
  • Database migration
02

Cloud cost optimisation

Make usage, ownership and unit economics visible so teams can continuously control spend.

You getLower spend with cost per customer tracked monthly
  • FinOps
  • Cost allocation
  • Savings plans
  • Unit economics
03

Cloud infrastructure & operations

Build secure, resilient foundations and repeatable operations without relying on heroics.

You getA cloud foundation and run model your team can rely on
  • Landing zones
  • Terraform
  • SRE
  • Incident response

How AI uses CLEAR

Every Cloud Transformation engagement starts with C247 CLEAR.

Explore C247 CLEAR
  1. C ClarifyThe workload or process cloud should improve
  2. L LocateApplications, data and dependencies to move
  3. E EvaluateCost, resilience and team readiness targets
  4. A ArchitectLanding zone, migration waves and operating model
  5. R RoadmapOne workload migrated, then scale the factory

How we deliver

From first prompt to AI that earns trust.

We prove value on one workflow with real users before scaling, and measure accuracy, safety and cost at every release.

  1. 01

    CLEAR discovery

    Rank use cases, map data and systems, set accuracy, safety and cost targets.

    2–4 weeks
  2. 02

    Proof of value

    An evaluation set and a working prototype on real examples.

    Accuracy baseline
  3. 03

    Pilot in the workflow

    Live with real users, guardrails and human review in place.

    Value proven
  4. 04

    Scale & integrate

    More workflows and users on the same foundations.

    Workflow by workflow
  5. 05

    Run & improve

    Quality, drift and cost monitored; feedback turned into improvements.

    Ongoing
01

The team that joins

Senior people, accountable for the outcome

  • AI/ML lead accountable for the system
  • AI and full-stack engineers models, agents, product
  • Data engineer retrieval and pipelines
  • Delivery lead reports in your rituals
02

Quality & safety gates

Checked on every release, not once

  • Evaluation suite run on every change
  • Red-teaming and prompt-injection tests
  • PII and access review including data residency
  • Cost per task checked before release
03

After launch

Watched, improved and yours to run

  • Monitoring quality, drift and cost
  • Feedback loop from human reviewers
  • Runbooks and model cards in your repo
  • Hand-over or a managed run

"Think first. Then build." is not a delay before delivery. It is how we make delivery more dependable.

Technology and partners

The right models for your use case and risk.

We are model-agnostic and design for change: swap a model without rewriting the system. These are the platforms we deliver on most often, layer by layer.

Cloud

Build, deploy, and scale secure cloud infrastructure.

  • AWS
  • Terraform
  • Docker
  • Azure
  • GCP
  • Kubernetes

Stack decision principle: We do not force a fixed toolset. We choose technology after CLEAR clarifies the data, workflow, security posture, integration reality, operating cost, and ownership model.

Client impact

Building things that will last.

Featured work across verticals: proof that the method works beyond one flagship project.

View all work
Regional insurance carrier

Settling simple claims the same day with document AI and human review

  • InsurTech
  • Document AI
  • Workflow automation
Residential property manager

Filling vacancies faster with tenant matching and digital leasing

  • PropTech
  • Matching engine
  • Digital leasing
Online learning provider

An adaptive learning platform that keeps every learner on track

  • EdTech
  • Adaptive content
  • Web & mobile
Regional logistics operator

Turning delivery data into on-time decisions across the fleet

  • Logistics
  • Data pipelines
  • Routing intelligence
Precision components manufacturer

Predicting machine failures before they stop the line

  • Manufacturing
  • IoT data
  • Predictive models
Multi-specialty clinic network

Cutting patient intake time in half with a clinically reviewed assistant

  • HealthTech
  • Conversational AI
  • Human-in-the-loop
Digital lending platform

Same-day lending decisions with explainable risk scoring

  • FinTech
  • Risk models
  • KYC automation
Specialty retailer

One AI-assisted platform for every customer conversation

  • Retail
  • Conversational AI
  • Platform engineering
01 / 08

Connect

Clarity before code.

We will help clarify the workflows, data, users, risks, and build path before your team commits engineering budget.

  • Workflows
  • Data
  • Users
  • Risks
  • Build path