Spend less. Stay up. Move faster.

Why it matters

Infrastructure costs grow quietly: oversized servers, idle environments, forgotten resources and architectures that were never designed with cost in mind. Every unnecessary euro spent on infrastructure is money not spent on your product — and savings, once made, repeat every single month.

Sounds familiar?

• The cloud bill keeps rising, but traffic and revenue don't.
• Nobody can say clearly what each product, team or environment costs.
• Servers and environments run around the clock, even when nobody uses them.
• Storage, licences and data-transfer costs add up unnoticed.

Practical support and deliverables

• —Cost assessment: where the money goes and what can be saved
• —Right-sizing and removing idle or forgotten resources
• —Scaling to demand, so you pay for peaks only when they happen
• —Smarter pricing models, storage choices and data-transfer savings
• —Cost visibility per product, team or environment
• —Budgets and alerts that keep the savings in place

Why it matters

Every minute of downtime or slow response means lost revenue, frustrated users and stressed teams. Most outages don't come from one big failure but from single points of failure, untested backups and systems that were never put under real load.

Sounds familiar?

• Outages or slowdowns when traffic peaks.
• One server, database or location whose failure stops everything.
• Backups that exist but have never been restored.
• Customers notice problems before the team does.

Practical support and deliverables

• —Availability and performance assessment
• —Removing single points of failure
• —Backup and disaster recovery that has actually been tested
• —Performance tuning and load testing
• —Automatic scaling for peak demand
• —Monitoring and alerts that warn before users notice

Why it matters

Legacy infrastructure and monolithic applications get harder and more expensive to change every year. Modernising them is one of the most effective ways to cut running costs, improve availability and performance, and let teams deliver faster — as long as it happens step by step, without disrupting the business.

Sounds familiar?

• Ageing servers or data centres that are expensive to run and risky to change.
• Monolithic applications where every release is slow and risky.
• Cloud and on-premises systems that don't work well together.
• Environments nobody could rebuild from scratch.

Practical support and deliverables

• —Assessment and a step-by-step modernisation roadmap
• —Moving to cloud, hybrid or modern on-premises platforms
• —Splitting monoliths into manageable services where it pays off
• —Standardised, repeatable deployments
• —Environments that can be rebuilt from code at any time
• —Migration without disrupting day-to-day operations

Why it matters

Manual work is slow, error-prone and expensive, and it keeps skilled people from the work that matters. Automation today goes far beyond scripts: AI assistants can triage alerts, answer questions from your own documents and handle routine decisions — while people stay in control.

Sounds familiar?

• Releases and infrastructure changes still done by hand.
• Teams losing hours every week to repetitive tasks — copying data, preparing reports, chasing approvals.
• Alerts, tickets and requests that need a first look before anyone acts.
• Knowledge buried in documents that is hard to find when it's needed.

Practical support and deliverables

• —Automated build, test and release processes
• —Automated infrastructure and configuration changes
• —Workflows that automate everyday tasks: approvals, reports, data handling
• —AI agents for triage, monitoring and operations
• —AI search and assistants over your own documents and data
• —Human approval built in where it matters

Why it matters

Data pipelines that break silently and AI models that never leave the experiment stage are common — and expensive. The value comes from running them reliably in production, scaling them with demand and keeping their cost under control.

Sounds familiar?

• Data spread across systems, duplicated or hard to trust.
• Data processing that can't keep up with growing volumes.
• AI models that work in a demo but never reach production.
• Expensive computing power, especially for AI, used inefficiently.

Practical support and deliverables

• —Data management: organised, reliable and easy-to-find data
• —Data processing and real-time flows between your systems
• —Taking AI models from experiment to production
• —Infrastructure for AI assistants and search
• —Efficient use of computing and GPU resources
• —Monitoring for data and AI workloads