AI that ships, not AI that demos
Most enterprise AI initiatives stall between demo and production. We integrate AI into systems that already carry business weight — with proper evaluation, monitoring, cost control, and fallback paths — not as a side project.
Where AI actually earns its keep
Retrieval-augmented generation over your internal knowledge. Workflow automation that touches multiple systems. Copilots embedded in tools your team already uses. AI-accelerated internal operations. We start where the value is measurable and the risk is manageable.
Evaluation, not vibes
Production AI needs continuous evaluation, observability, and cost monitoring — or it quietly degrades. We build those in from day one, treat prompts as code, and version them like any other dependency.
Model-agnostic
We work across Anthropic, OpenAI, open-weight models, and self-hosted setups. The choice depends on data sensitivity, latency, cost, and quality — not vendor preference.
- How do you handle data privacy?
- We architect for the constraints first — self-hosted models, regional cloud, redaction pipelines, audit logging — then choose the best provider that fits. Privacy is a design input, not an afterthought.
- What if the model gets it wrong?
- Every production AI path needs a fallback. We design for graceful degradation, human-in-the-loop where it matters, and observability that surfaces failure modes before users do.
AI-augmented delivery is the new baseline
Three years in, AI-assisted engineering stopped being a competitive edge and became the default. What separates teams now is what AI cannot do.
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unser CTO Kyrylo Osadchuk wird innerhalb von 24 Stunden antworten.