WHAT WE DO
What we do
Agents · Terraform · Eval Stacks · SaaS Metering
WHAT WE DO
Capability – not theatre.
We blueprint hyperscaler-ready estates, integrate SaaS backbones, and operationalise AI Foundry when SMEs need retrieval and inference governed inside procurement timelines – not aspirational slide cadences.
- Cloud Advisory
- Cloud Migration
- Kubernetes and OpenShift Advisory
- Platform Engineering
- Operator Development
- FinOps and Analytics
- AI Platforms
- AI Development
- DataOps and MLOps
- Data Migration
- Real-Time Data Platforms
- Metrics Engineering & Semantic Layer
- Custom Software Development
- Vibecoding
- Software Architecture
- Software Modernization
- Software Maintenance
FAQ
Clear answers for decision-makers.
Straight responses about engagements, pilots, and what AI Foundry actually covers technically.
- Should we build in-house, use Azure OpenAI, or AI Foundry?
- Building an in-house platform team takes months to hire and years to mature — fine at hyperscaler scale, rarely optimal for mid-sized organisations. Azure OpenAI and Databricks excel at model access but leave tenancy, retrieval orchestration, approvals, and spend visibility for you to assemble. AI Foundry is the managed middle path: production runtime, governance, and metering from week one — with cloudstrata operating the platform layer while your teams own use cases.
- What does a typical project cost?
- Project costs depend on scope, complexity, and timeline. We offer fixed-price proposals for defined scopes and time-and-materials for exploratory work. After a Discovery Call, we provide a tailored proposal with clear pricing.
- How long does an MVP take?
- Most MVPs take 8–16 weeks from kickoff to first release, depending on complexity. We work in agile sprints with regular demos so you see progress early.
- Do we need internal ML engineers or a platform team?
- Rarely as a prerequisite for pilots – you ship outcomes with product owners steering retrieval datasets while we embed architects who instrument tenancy, guardrails, and telemetry inside AI Foundry. When internal juniors emerge, we pair-teach LLMOps patterns deliberately – rather than handing off brittle notebooks. We collaborate remotely across Europe with HQ visits and onsite spikes whenever rituals demand presence.
- What does a typical engagement look like?
- Discovery aligns on KPIs and success criteria → we blueprint retrieval, orchestration, and evaluation → iterative demos show measurable progress → expansion follows jointly agreed thresholds — not gut feel alone.
- How do I get started?
- Book a free 30-minute Discovery Call. We'll discuss your goals, challenges, and technical landscape – no obligation. From there, we propose next steps and a tailored approach.
CONTACT
Get in touch
Tell us about your use case — we'll respond with a tailored next step.
We aim to reply within one business day.