Amazon Web Services
Get more out of the AWS you already have.
Most enterprises completed their AWS migration years ago. The bill kept growing, the architecture stayed where the migration left it, and the first real AI workload is what finally exposes both. We fix the platform underneath, then build the AI workloads that run on it.
What we usually walk into
Three things tend to be true at once.
The estate works, but nobody can explain the bill.
Instances sized for a 2021 workload and never revisited. Savings Plans expired. Two teams running the same pipeline.
The architecture reflects the migration, not the business.
Applications moved as they were. The database is still the bottleneck it was on-premises, only now it costs more.
The AI pilot won't survive production.
No cost ceiling, no evaluation harness, no way to tell a wrong answer from a right one at scale.
None of these are AWS problems. They are problems in how AWS was set up and is being run. This is where Expleo can help.
What we do
1.
Move and modernise
We take applications the rest of the market calls too hard to touch and get them onto AWS in a form that’s cheaper to run afterward.
Dependency mapping first, so the sequence is based on what actually calls what. Then migration in waves, with rollback at every stage. Finally, modernisation where it creates value: containers where they earn it, serverless where traffic is spiky, Graviton where the workload is portable.
We tell you which applications fall outside that. Not everything should be modernised.
2.
Build the landing zone properly, or fix the one you have
Accounts, guardrails, networking, identity and logging are set up once, configured correctly, and delivered as code that your own teams can extend. Most of our work here is remediation rather than greenfield: estates that grew account by account, with security groups nobody will touch. We untangle it without a freeze on delivery.
03.
Run GenAI and agents in production
This is where most enterprise AI programs stall. On Bedrock and SageMaker, we put in place what a demo doesn’t need and a production system can’t live without: retrieval that returns the right document rather than a plausible one, agent orchestration with a hard stop on steps and spend per task, evaluation that runs on every model change, and guardrails configured to your policy rather than the defaults.
A frontier model answering a question a small one handles just as well is the most common source of runaway AI spend we find.
04.
Keep the bill honest
FinOps as an operating habit, not a one-off cleanup. Right-sizing and commitment coverage across compute, storage, and data transfer. Cost allocation that tells you what a product line costs, not what an account costs. Unit economics for AI specifically, including cost per thousand requests, per agent run and per user, so the finance conversation is about what each thing returns.
Our proof
Preferred partner
to the education loans division of the world’s 10th-largest bank.
Key partner
to the software division of the world’s second-largest automotive manufacturer.
What we standardise on
For the architects
Foundations
Multi-account structure on Control Tower and Organisations. Transit Gateway with centralised egress inspection. Everything as Terraform or CDK in your own repo, with policy checks in the pipeline rather than in a review meeting.
Compute and data
EKS where orchestration is needed, Lambda and Step Functions for event-driven work, Graviton wherever the workload is portable. Aurora and RDS for transactional systems, S3 and Glue for analytics, with lifecycle policy applied from day one.
AI platform
Bedrock as the primary model layer, SageMaker where a model needs fine-tuning or dedicated hosting, Inferentia or Trainium where volume justifies moving off general-purpose GPU. Retrieval on OpenSearch or Aurora with pgvector, chosen on query pattern, not preference.
Operations and compliance
Evaluation suites that run before a prompt or model reaches production. Drift monitoring alongside infrastructure signals. Least-privilege IAM through permission boundaries. Data residency mapped to region choice before architecture, not after.
How it starts
A two to three week assessment.
We look at your AWS estate as it is, including cost, architecture, security posture, and any AI work already in flight, and come back with a prioritised plan: what to fix first, the savings available, the cost of remediation, and what we’d leave alone.
You own that output whether or not you engage us for the delivery.
Tell us where your AWS estate hurts.
Costs, architecture, or AI workloads that won’t make it to production. We’ll tell you what we’d do about it.
