AI Architecture & Build Sprint
For concrete AI ideas whose technical route is still unclear.
Outcome / focus
Architecture decision, make/buy, MVP scope and a concrete build plan.
Services
AI Services
From a sound architecture decision through pilots and integration to production readiness and ongoing technical support.
Ways to work together
Four ways from a question to a working system.
The right starting point depends on where your project stands.
AI Pilot & Product Build
For teams testing a real workflow with real data and users.
Outcome / focus
A working pilot, a clear learning question and production-oriented implementation.
AI Production Care
For AI systems already in production.
Outcome / focus
Evaluation, regression, RAG and agent quality, provider changes, cost and technical health checks.
Fractional AI Technical Lead
For companies where AI matters strategically but a senior in-house AI team would be premature.
Outcome / focus
Technical leadership, architecture decisions and hands-on guidance.
Capabilities
Specialist capabilities and existing offers
Depending on your project, architecture, integration, evaluation or a particular operating model may take priority.
AI Architecture & Integration
Data flows, APIs, system integration and make-or-buy decisions.
Explore →RAG, Agents & Automation
Knowledge systems, agents and automation with real data and permissions.
Explore →Private / Sovereign AI
EU hosting, open source and on-premise where data sovereignty calls for it.
Explore →Production Readiness & Evaluation
Quality, regression, guardrails and reliable release criteria.
Explore →Security & Permissions
Permissions and safe data flows for production use.
Explore →Workshops & Discovery
Clarify use cases, constraints and initial architecture decisions.
Explore →Meeting AI
Local meeting processing with traceable results.
Explore →AI Incident Response
Practical steps for a specific AI data privacy incident.
Explore →AI incident response
Sensitive or confidential data has reached an AI system? Scope the incident technically, check active data paths, preserve evidence and establish the next owners.
Offerings
The right starting point depends on your maturity.
Some teams first need orientation. Others already have use cases and need architecture, sparring or implementation depth. The services are therefore deliberately modular.
Sovereign AI
A private AI workplace for your team
GDPR-compliant AI that belongs to your company: EU-hosted or on-premise, without US cloud lock-in and with a first automated workflow.
Suited to mid-sized companies that want to bring Shadow AI under control and introduce productive AI with full data sovereignty.
- Sovereignty check as a solid starting point
- Private AI workplace productive in about 4 weeks
- One automated business process with measurable time savings
- Setup and configuration remain your property
Workshops
The structured entry point for management, IT and business units
When clarity comes first: a workshop instead of jumping straight to tooling. Sort out potential, risks, conditions and next steps together.
Suited to companies that want to start in a structured way and need a sound basis for decisions.
- Prioritise relevant AI use cases cleanly
- Assess cloud, local or hybrid realistically
- Clarify risks and governance
- Sound next steps for pilot or roadmap
Consulting
Strategic and technical guidance for sound decisions
When the direction is clearer: guidance on architecture, stack selection, target picture and implementation preparation – with a technical reality check.
Suited to companies that want to bring AI into processes and systems in a controlled way.
- AI strategy and target architecture
- RAG, knowledge systems and secure AI assistance
- Data flows, integrations and technical guardrails
- Sparring for pilot and implementation planning
Implementation & support
Pragmatic support from pilot to operations
When decisions should turn into solid solutions: technical support, prototypes, integrations and a controlled handover into everyday work.
Suited to companies that want to evaluate AI not only conceptually, but bring it cleanly into existing processes.
- Prototypes and pilots with a clear learning goal
- Integration into existing data and system landscapes
- Technical reviews on architecture, security and operations
- Handover with traceable decisions and next steps
AI production readiness
Evaluate AI systems and make them production-ready
An independent 2–4 week sprint for existing RAG and agentic AI systems: realistic test sets, failure analysis, guardrails and a prioritised go-live plan.
Suited to teams with an existing pilot or system where quality, agent behaviour, security or operability are not yet established reliably.
- Measurable quality and release criteria
- Reusable evaluation set and baseline
- Prioritised failure map instead of vague symptoms
- Concrete fix, guardrail and go-live plan
Fields of application
Where AI often becomes valuable in a company.
Make company knowledge usable faster
Contracts, policies, documents, PDFs — made findable and usable in context faster via RAG, internal search systems, or secure knowledge assistants.
Ease knowledge work
Research, preliminary analysis, summaries, documentation — noticeably accelerated once quality and control are clarified.
A secure alternative to public-cloud AI
Where data, know-how, or internal information must stay protected: sovereign systems with clear roles and data flows.
Technical clarity instead of stack chaos
Not more tools, but better decisions: which models, which infrastructure, which hosting variant make sense in the long run.
A concrete offer
Meeting AI: one field of application you can test right away.
“Ease knowledge work” turned into a ready-made offer: minutes and tasks automatically, processed locally on your own hardware, every critical statement backed by a quote and timecode. With an open ROI calculator and a 14-day Fast-Track that measures it against your own meetings.
A typical process in 4 steps
Each step builds logically on the previous one.
- 01
Workshop
Clarify goals, priorities and use cases.
- 02
Architecture
Define data flows, roles and technical guardrails.
- 03
Development
Build and test a solid prototype.
- 04
Pilot
Try the solution in everyday work, gather learnings for rollout.