AI & Cloud Convergence
Cloud-native AI stacks engineered for production — foundation models, retrieval, your data, and the governance discipline that turns experiments into systems your board can defend.
Production-grade AI on your cloud stack — not lab demos.
Fourteen capabilities that together turn AI ambition into shipped, governed systems. We start where you are: green-field, hybrid, or deep in an existing platform.
Foundation Model Selection
Pick the right model family (OSS, closed, fine-tuned) for cost, latency, sovereignty and accuracy — not vendor hype.
RAG & Retrieval Stacks
Hybrid vector + keyword retrieval over your data, hardened for production: chunking, freshness, eval and observability built in.
MLOps & LLMOps
CI/CD for prompts, models and evals — versioning, rollback, A/B and guardrails so AI ships like the rest of your software.
Cloud-native Architecture
Reference architectures on AWS, Azure, GCP — and the on-prem extensions for workloads that can't leave the perimeter.
Data Foundations
Lakehouse, governance, lineage and quality — without which AI is decorative. We build them in flight, not as a 2-year prelude.
AI Governance & Eval
Risk-tiered policies, golden datasets, automated eval pipelines and a register your DPO and your auditor can both read.
Cost & Latency Engineering
Model routing, caching, distillation and inference tuning — AI economics designed for scale, not a flat monthly surprise.
Adoption & Enablement
Patterns, playbooks and training so your teams own the platform — not just consume our outputs.
From fourteen tools to one accountable platform.
AI ambition fails as fragmented vendor sprawl. We collapse the stack into one reference architecture — your cloud, your data, your governance — and ship the first system into production behind real users in eleven weeks.
Pilot to production in four moves.
Each stage has explicit entry criteria, deliverables and exit gates. No project ever leaves a stage on hope alone.
Assess
Map the use cases, the data, the constraints — and the existing stack. Rank by value-over-risk; cut the hype.
Architect
Design for integration, security and sovereignty. Choose models, retrieval, governance, ops — agree the eval bar.
Productionize
Ship the first system into production behind real users. Iterate weekly on quality, latency and cost.
Govern & Scale
Operate with measurable ROI and a risk register your board can defend. Roll the platform out to the next use case.
Designed to plug into your existing stack — not replace it.
AI doesn't live in isolation. We architect every system around three integration surfaces so it lands as part of your platform, not next to it.
Data & identity
Reads from your data lake, warehouse and operational stores via governed contracts. Identity inherits from your existing IdP — no shadow user pool.
Cloud & platform
Runs on your cloud accounts (AWS, Azure, GCP, hybrid) under your IaC and your SRE practices. Reference Terraform modules ship with the engagement.
Governance & ops
Hooks into your existing SIEM, DLP, audit and incident processes. The AI risk register lives alongside your other risk records — not in a new tool.
Measured in P&L and risk — not slideware.
Production outcomes from recent AI & Cloud Convergence engagements — governed, audit-ready, and operated by the client's own teams.
From a stalled pilot to a board-defensible AI platform — in eleven weeks.
A €4bn industrial group had spent eighteen months on AI pilots that never reached the P&L. We architected one AI & Cloud Convergence platform, productionized the first use case in eleven weeks, and handed back a stack their own teams now operate — with an ISO 27001-aligned audit trail their DPO signed off.
Every AI & Cloud engagement delivered on a stack you can defend.
GDPR-aligned, ISO 27001-ready, outside CLOUD-Act reach. Your data, your DPO, your audit trail.
Move AIto production.
From a stuck pilot to a shipped platform — eleven weeks, governed, with an audit trail your DPO will sign off. Bring the use case; we bring the path.