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AI Engineering is the engineering implementation of AI systems: architecture, data flows, integrations, evals, deployment and operations. Unlike PoCs and demos, AI Engineering targets AI implementation that runs sign-off-ready in production — with measurable quality, controllable cost and solid evidence for security, data protection, procurement and audit.

We step in where the initiative currently stands: at cloud and data platforms, at the selection of viable use cases, at delivery via AI Engineering Sprints, or at the review of existing AI agents in production. Not every project starts with a workshop — production-adjacent workflows often already exist where cost, quality or evidence are unresolved.

Use Case Workshops clarify which AI initiatives make sense technically, commercially and from a regulatory perspective (business value, data situation, integration effort, risk profile, production readiness). AI Engineering Sprints turn a prioritised use case into a running system — not a deck, not an isolated demo.

When AI initiatives are blocked by data access, permissions, security sign-offs, missing interfaces or unstable deployment structures. Productive AI agents need clean data flows, IAM, logging/monitoring, CI/CD, cost structure and clear operational ownership.

Integrations happen via approved interfaces and operational paths: SAP connectors, existing data platforms, APIs, databases, event systems and secure on-prem/cloud links. Vendor lock-in is reduced: models, vector stores, tools and orchestration remain interchangeable as far as possible.

We use evals instead of gut feeling. For critical use cases we build test sets, metrics and monitoring on output quality, error behaviour, latency, cost and tool usage. Drift is detected via continuous re-evaluation against gold-standard data. For critical paths we use guardrails, human-in-the-loop and structured outputs.

Costs are modelled before go-live per use case and workflow (model usage, token volume, tool calls, data volumes, infrastructure). In production we add cost attribution per use case/model/workflow plus FinOps measures to make cost drivers visible and reduce them.

When AI agents or LLM workflows already run in production or close to it and cost grows, quality drifts or evidence is missing. We review architecture, observability, eval findings, drift, cost drivers and governance gaps and deliver a prioritised optimisation roadmap with concrete actions for models, prompts, tools and infrastructure.

Both. Sometimes integrating into an existing flow is enough. Often impact only emerges when the process is re-cut with AI: different handovers, different data points, different review and sign-off steps. We decide in the Use Case Workshop based on impact, risk, data situation and regulatory requirements.