Cloud, DevOps & AI platform engineering
Give your teams a secure, self-service platform to build on – cloud foundations, automated delivery, reliability and the infrastructure to run AI models where your data needs to stay. Infrastructure is defined as code, with reliability and cost always in view.
Your platform now has to run AI as well as apps
Platform engineering has replaced ticket-driven operations: teams get paved roads to ship safely on their own. The platform must now also host models, control AI spend and keep data in the right region – without slowing anyone down.
of cloud spend is wasted, by organisations’ own estimates – the first increase in five years, driven by cloud AI workloads.
- BeforeTickets to get an environmentNowSelf-service environments on paved roads
- BeforeSecurity checks at the endNowSecurity built into every pipeline
- BeforeAI services added ad hocNowGoverned AI infrastructure with cost and data controls
Rehost, replatform or refactor: how should you move to the cloud?
Most estates use more than one approach, chosen system by system.
| Approach | What changes | Best for | Trade-off |
|---|---|---|---|
| Rehost (lift and shift) | Systems move as they are to cloud servers | A fast exit from a data centre | Little gain in cost or agility |
| Replatform | Small changes, such as managed databases or containers | Quick wins in cost and operations | Some testing; the architecture stays largely the same |
| Refactor | Rebuilt to use cloud-native services | Systems that must scale or change often | The most effort and time |
| Retire or replace | Switched off, or replaced with SaaS | Commodity functions | Data and processes still need migrating |
What this service covers
Each capability can be used on its own or combined with capabilities from other services. “Also part of” shows the other services that include it.
Cloud foundations & platform engineering
Secure cloud landing zones, infrastructure as code and self-service environments, so teams ship without waiting on tickets.
CI/CD & DevSecOps
Pipelines with automated tests, security scans, software bills of materials and secrets management on every change.
Reliability, observability & FinOps
Service-level objectives, monitoring, incident response and cloud cost control, with AIOps to spot problems early.
AI infrastructure & model hosting
Run models where your data has to stay: managed AI services or private and open-weight models, behind a gateway that controls access and spend.
Combined with capabilities from other services
This service draws on capabilities owned by other teams. Each is explained in full on its own service page.
Specialist services within cloud & platform engineering
Each builds on the capabilities above and adds its own.
Data & AI operations
Data platforms, MLOps, LLMOps and AI monitoring.
- AI-ready data platforms
- MLOps & LLMOps
- AI observability & cost control
- Analytics & decision dashboards
Identity & access (IAM)
Passkeys, zero-trust access and identity for AI agents.
- Passwordless & customer identity
- Identity for AI agents & services
- Zero-trust access & governance
How we deliver it
- 01
Assess
Architecture, delivery pipeline, security and cost review.
- 02
Design
The target platform and the paved roads your teams will use.
- 03
Build and migrate
Infrastructure as code, pipelines and migration in safe waves.
- 04
Operate
Monitoring, incident response and cost reviews – with your team or for you.
- A secure cloud foundation defined as code
- Pipelines with tests and security scans on every change
- Monitoring, alerts and regular cost reviews
- AI infrastructure with data residency and spend controls
- Start with one capabilityA fixed-scope piece of work, such as an assessment, a prototype or a proof of concept.
- Combine capabilities into one projectCapabilities from several services, delivered by one team with one plan and one point of contact.
- Add a dedicated teamEngineers who join your team to build and run it with you.
Selected client work
Related guides
Top 10 cloud security risks and how to mitigate them
The ten most common cloud security risks – misconfiguration, weak identity, exposed APIs, data leaks and more – with practical mitigations for each.
Read the articleHow to ensure quality in custom software delivery
How to ensure quality in custom software delivery: clear requirements, definition of done, code reviews, automated testing, CI/CD, security checks and…
Read the articleMLOps best practices: how to keep AI models reliable in production
MLOps keeps machine learning and LLM systems accurate after launch. Learn the core practices – versioning, CI/CD, monitoring, retraining and governanc…
Read the articleHow to scope an AI proof of concept that reaches production
Business outcome, use case, data checks, success criteria and a go/no-go decision.
Read the articleFrequently asked questions
Which cloud platforms do you work with?
AWS, Microsoft Azure, Google Cloud and DigitalOcean. Recent work includes a Kubernetes platform on Azure for Aristiun and AWS infrastructure for a large gaming community.
What is platform engineering?
Platform engineering builds an internal platform – infrastructure, pipelines, templates and guardrails – that lets development teams ship safely on their own. It replaces waiting for operations tickets with self-service paved roads.
Can you host AI models in our own cloud or region?
Yes. We set up managed AI services or private and open-weight models in your cloud account and region, behind a gateway that controls access, logs use and caps spend.
Do you provide ongoing cloud operations and support?
Yes. We offer managed cloud operations and L2/L3 production support, for example the long-running support engagement for Kibono’s HSA platform.
What is AIOps?
AIOps uses machine learning on logs, metrics and alerts to detect anomalies, group incidents and speed up root-cause analysis, so operations teams spend less time on noise.