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AI & Data Infrastructure

The data foundations and platform your AI projects need — designed once, built properly, ready to scale.

AI & Data Infrastructure is the design and implementation of the technical foundation your AI initiatives run on: data pipelines, storage and retrieval layers, model access, MCP and API connections, hosting, and access control. Solid infrastructure is what separates AI systems that survive production from demos that don't.

Who this is for

Companies whose AI ambitions have outgrown spreadsheets, one-off scripts, and copy-pasting into chatbots.

What you get

  • Data pipelines that deliver clean, current data to your AI systems automatically
  • A retrieval layer that grounds AI answers in your approved documents and sources
  • Secure connections between AI and your business systems via MCPs and APIs
  • Infrastructure your own team can operate, monitor, and extend

What's included

Data pipeline engineering

Ingestion, transformation, and quality checks that keep your data assets fresh and trustworthy — the fuel for every AI use case.

Knowledge and retrieval layer

Vector search and retrieval over approved internal knowledge, with role-aware access and traceable sources.

MCP and API integration

Secure connections between AI models and your real systems — CRM, ERP, document stores — with scoped permissions and audit logs.

Hosting and access control

Deployment on your cloud or Swiss-hosted environments, with authentication, monitoring, and cost controls in place.

How it works

  1. 01

    Map requirements

    We identify the use cases the infrastructure must serve, data sources, security constraints, and hosting preferences.

  2. 02

    Design the architecture

    A pragmatic design sized for your actual needs — no premature platform-building, no dead ends either.

  3. 03

    Build and integrate

    Pipelines, retrieval, and system connections are implemented iteratively, with working checkpoints throughout.

  4. 04

    Harden and hand over

    Monitoring, access control, documentation, and a handover so your team runs the platform with confidence.

Deliverables

  • Running data pipelines with quality checks
  • Retrieval layer over approved knowledge sources
  • MCP/API connections to business systems
  • Architecture documentation and operations runbook

Frequently asked questions

Can our data stay in Switzerland?

Yes. We deploy on Swiss-hosted infrastructure or your existing cloud with Swiss/EU regions, and design data flows so sensitive data never leaves approved boundaries. Model choice follows the same logic — including options that avoid US-based APIs entirely.

What are MCP connections and why do they matter?

MCP (Model Context Protocol) is an open standard that lets AI models use tools and data sources through governed, auditable connections. It turns isolated prompting into AI that can read your approved documents and act in your systems — with permissions you control.

Do we need big-data infrastructure for AI?

Usually not. Most Swiss mid-market AI use cases need clean pipelines and a good retrieval layer, not a data lake. We size infrastructure to the use cases on your shortlist and design for extension rather than speculation.

Ready to scope this with us?

Tell us about your systems, data, and goals. We reply within one Swiss business day with a concrete next step.

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