From concept to production. Real solutions for real problems. Strategy, implementation, operations — in one hand. Sometimes with AI at the core, sometimes as an amplifier, sometimes not at all — depending on what the task actually needs.
Get in touchThe market for AI consulting is well-served. What's often missing is the bridge from concept to a running system — and that is exactly where my focus lies.
Strategic clarity is the starting point: I analyse, prioritise and design the architecture with you — and then implement it productively, with measurable impact in day-to-day operations.
Two levers make Applied AI economically effective in mid-sized businesses: inside-out (streamline processes, free up capacity) and outside-in (growth, sales, marketing). We begin where the value contribution is clearest — and scale from a robust foundation.
Repetitive tasks tie up valuable time. Knowledge is scattered across many systems. Workflows have grown over years without being rethought. Applied AI starts here — with measurable effects on cycle time and capacity.
Inbound/outbound invoices, expenses, reports. From inbox through OCR + classification straight to bookkeeping.
Semantic search over SharePoint, Confluence, local docs. Answers in seconds instead of hours.
Autonomous task chains. Market analysis from 20+ sources, automatically summarized and fact-checked.
OCR + LLM for complex unstructured inputs (contracts, applications, files). AI extracts, humans decide.
Automated checking against internal policies, regulatory updates, AI Act compliance.
More leads. Faster qualification. More personal customer communication at scale. This is where Applied AI becomes a revenue driver.
Extract, qualify and route leads from email, forms, external sources to the right consultant.
From lead to first contact in minutes, not hours. Personalized outreach. Meeting booking without back-and-forth.
Content briefings, research, drafts, image generation, A/B test setups. Output scales without team growth.
Smart customer portals that give real answers. With escalation to humans when needed.
Continuous monitoring of competitors, industry, regulation. Weekly briefings that support decisions.
Not every problem is an AI problem. Sometimes the right answer is a clean custom software solution without an LLM, agent or vector database. I'll say so transparently when that's the case.
Tailored applications for specific business processes. Where standard software fits 30%.
Connect existing tools. ERP, CRM, ticketing, time tracking — should work together.
Decision-relevant KPIs from sources you already have. No mega data lake projects.
Replace three SaaS tools that don't cooperate with one custom-built solution. Often cheaper.
Every implementation rests on a clear understanding of context, objectives and constraints. Strategic advisory is the foundation for any viable AI solution — and remains a constant companion throughout the build.
Honest position check. Where does AI pay off now, where later, where not at all?
6-to-12-month plan with ROI model, quick wins, team setup.
Clear rules for AI use in your company. GDPR + AI Act compliant.
Mandatory since February 2025 (EU AI Act, Art. 4). Not a compliance exercise but real enablement of your team.
First call + use-case identification
Assessment + architecture draft, clickable prototype
MVP build, first production users
Iteration based on real usage, scaling, new use cases
Principle: Iterative and outcome-oriented. Three smaller productive systems create more value than a single large project with an uncertain outcome.
Yes — provided the use case is well-scoped. For mid-sized businesses, applied-AI projects pay off particularly where recurring, rule-based tasks consume capacity (invoice processing, customer inquiries, internal knowledge search, lead qualification). First productive systems are typically in place within four to eight weeks, with measurable ROI within 3–6 months. The precondition: a realistic use case instead of a generic "AI strategy" project.
A first productive MVP for mid-market companies usually sits in the five-figure range, depending on scope and integration depth. Investment scales along value-contribution validation: small focused first system → measurement → scale only after proven benefit. Concrete numbers emerge from the first-call assessment.
No. The architecture decision follows the use case and compliance requirements — not vendor lock-in. Options: EU cloud (e.g. Mistral, Anthropic via AWS Frankfurt), on-premise (open-source models like Llama, DeepSeek), or hybrid. Sensitive data can be processed entirely on German or Austrian infrastructure.
Since 2 February 2025, Article 4 of the EU AI Regulation obligates every company deploying AI systems to maintain their staff at a sufficient level of AI literacy. That covers training on how AI works, its limits, its risks (hallucination, bias, data exfiltration) and correct application in each role. The obligation applies regardless of the AI system's risk class.
A ChatGPT or Copilot subscription is a tool for individuals — useful for drafting, research, code. Applied AI, by contrast, means integrated systems that operate within business processes (classify documents automatically, route inquiries, generate reports, monitor supply-chain signals). The two complement, but do not replace, each other.
Typical MVP timeline: assessment in week 1–2, architecture and prototype in week 2–3, MVP build in week 3–6, first productive users from week 6–8. Operations can continue to be run by MH-Services, or the system can be handed over to your internal IT team — with documentation and training. Strategy, implementation and operations come from one source.
This is exactly what the Machine + Human methodology addresses: AI handles what scales and what repeats; decisions with real consequences stay with human experts. Implemented via confidence scores, automatic escalation workflows, four-eyes principle on critical actions, and full audit trails. No AI operates uncontrolled.
