What we do

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 achieve measurable 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 transfer established LLMOps practices deliberately – with documented handover rather than undocumented experiments. We collaborate remotely across Europe with HQ visits and onsite workshops whenever the engagement requires 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 and defined KPIs.

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.