Solutions
AI Infrastructure
You're building AI, but production infrastructure is unclear.
Build Our AI PlatformThe problem
We design production-ready infrastructure for AI applications, RAG systems, inference workloads and AI-enabled products.
Who it's for
Product and AI teams with a working prototype and no clear answer for latency, cost, evaluation or failure behaviour under real traffic.
Approach
How we work through it.
- 01
Define the serving path: managed API, self-hosted inference, or both with routing between them.
- 02
Build retrieval properly — ingestion, chunking, embedding refresh and index lifecycle as pipelines, not scripts.
- 03
Put evaluation in CI so prompt and model changes are regression-tested like code.
- 04
Control spend with caching, routing, token budgets and per-tenant limits.
- 05
Instrument everything: latency, token cost, retrieval quality, refusal and error rates.
Deliverables
What you end up with.
- AI serving architecture and capacity model
- Retrieval and embedding pipelines
- Evaluation harness wired into CI
- Cost controls and model routing
- AI-specific observability dashboards
Technologies
- Kubernetes
- GPU node pools
- vLLM
- pgvector
- Postgres
- Redis
- LangGraph
- OpenTelemetry
Typical engagement
6–12 weeks from prototype to a production path your team can operate.
Questions
Before you email us.
- Should we self-host models?
- Usually not at first. Self-hosting makes sense when volume, latency or data-residency requirements justify running GPUs. We model the cost both ways and give you the crossover point rather than a preference.
- Can you work with our existing AI code?
- Yes. The application logic is normally fine; what is missing is the infrastructure around it — retrieval lifecycle, evaluation, observability, cost control and deployment.
Start with a two-week Infrastructure Review.
Fixed scope, starting at €3,000, and a 90-day roadmap you keep whether or not we implement it.