We bridge the gap between platform and product teams — turning infrastructure investment into systems developers actually use, operate and own.
Book Your 1h SessionExamples from our platform advisory work: faster delivery, better performance and teams with greater autonomy. Figures are approximate; client details are anonymized.
20 min → 5 min
A platform team had built pipelines and infrastructure-as-code that developers barely used. Working across both teams helped align tooling with everyday development, increase adoption and bring deployment time down.
50% better page-load performance
We migrated around 50 microservices from on-premise infrastructure to AWS. Page-load performance improved while cloud costs fell below the previous setup. Developers gained control of infrastructure changes through Terraform and automated pipelines.
A team that runs it independently
Kubernetes infrastructure spanning around 10 AWS regions and 40 regional-environment deployments handled production traffic during major advertising campaigns. After handover, the client’s operations team ran it independently, and the system remains operational years later.
Platform and development working together
Working across platform and development teams helped improve collaboration and adoption of existing tooling. We adapt to your needs, with engagements ranging from a few weeks for an assessment to collaborations lasting several years.
We work as a senior technical partner — not an order-taker. We bring strategic depth to infrastructure challenges that matter most to your business.
A deep-dive assessment of your platform — architecture, reliability, scaling risks and hidden costs, alongside developer adoption and operational ownership. You get a clear prioritised roadmap.
See how it works →We embed with your engineering team to make your platform investment actually land — aligning infrastructure, pipelines and tooling with what developers will adopt, operate themselves and continue owning after the engagement.
See how it works →We break down where your cloud spend is going, identify which savings are worth making, and which would cost more in engineering time than they save.
See how it works →Emerging capability
We're building the future of engineering teams — and we can help you do it too.
We've spent the past year building a real multi-agent system: autonomous AI agents that write production code, deploy, review each other's work, and coordinate through shared task boards — without constant human intervention.
This isn't theory. It's a working system we operate daily. If your engineering team is exploring AI agent workflows — or wondering whether they should — we bring first-hand experience of what actually works, what breaks, and what matters for production-grade agentic systems.
Whether you're scaling fast, bleeding cloud spend, or standing at a critical architecture crossroad — you don't have to figure it out alone.
Exploring an AI or agentic initiative and want an independent technical second opinion? We’re hands-on with these systems ourselves and can help you pressure-test architecture, trade-offs and risk in the same paid 1-hour advisory format.
After successful careers as technology leaders in Barcelona and London, we decided to take the leap and help several local startups enhance their cloud infrastructure.
On the night Spain entered lockdown, we were helping a major streaming provider cope with traffic that suddenly increased by 1200% in just a few hours.
We also secured our first project assisting a European multi-cloud SaaS (AWS, Azure, on-premises) client who needed a white-label infrastructure solution for their European clients.
We embedded with engineering teams to work on a familiar gap: what the infrastructure makes possible and what developers need in their daily work.
We connected infrastructure, pipelines and tooling with actual development workflows. Security and cost control were part of the same effort: building a platform the team could adopt and use day to day.
International projects brought us into multi-region infrastructure and more complex engineering environments. Alongside architecture decisions, we had to address how software was deployed and how the platform was operated.
We combined technical guidance with improvements to delivery workflows and knowledge transfer to internal teams. The aim was for those teams to take ownership of the platform, understanding both how it worked and the decisions behind it.
We began applying our infrastructure experience to a new question: how to coordinate multiple AI agents so they can work together. We build and test systems for our own internal use, exploring orchestration, task handoffs and shared memory.
Review loops let us examine how work is coordinated and where intervention is needed. This practice informs our advice with firsthand experience of the architecture decisions and operational challenges these systems bring.
Eight years of delivering strategic impact. Want to be the next chapter?
