AI Advisor

AI Advisor for Tech & Manufacturing | Identified $5.6M in Hidden Inefficiencies | ROI-Driven AI Systems

• agentic AI
• Business Consultant
• IT-Strategieberatung
• Langchain

Most Tech & Manufacturing companies i meet are sitting on at least 200K$–800K$ in operational waste. They just don’t know where to look.

The AI they bought to fix it is making things worse, not better. It's not because AI is bad. The issue is that we haven't asked the right question first: Where will AI actually save us money, and what system is required to capture it? That's the question i help clients answer.

⸻
⦿ Here is how I help you fix that:

► Identify high-impact AI opportunities across your operations
► Quantify ROI (cost savings, time reduction, efficiency gains)
► Design a clear implementation roadmap
► Support execution from audit → automation → measurable results


⦿ What this means for your business:

✔ Less manual work 20-40%
✔ Faster approval, reporting, and processing cycles x2-4
✔ Lower operational costs 15-25%
✔ Better, data-driven decisions

► Typically, within 30–90 days

⸻

I’ve done this with 20+ Tech & Manufacturing teams across Europe and mapped 100+ AI use cases across 10 departments.

Where I Create Impact

? AI Enablement & Transformation
Identify and prioritize high-impact AI use cases tied to business KPIs—not hype.
Design transformation roadmaps that move from idea → pilot → production.
Align processes, teams, and operating models to support sustainable AI adoption.

? Generative AI, LLMs & AI Agents
Build secure, production-grade GenAI systems—not just demos.
Develop LLM-powered applications including:

RAG systems for internal knowledge retrieval
AI assistants for support, finance, and operations
Autonomous and multi-agent workflows

Focus on reliability, scalability, and real-world usability through LLMOps/MLOps.

? Architecture & Delivery
Design and deploy end-to-end AI systems across modern stacks:
Python, FastAPI, Next.js, PostgreSQL
Cloud: Azure AI, Vercel, Render
Practices: CI/CD, containerization, scalable infrastructure

What This Looks Like in Practice

→ Turning manual workflows into automated AI-driven processes
→ Reducing time spent on knowledge retrieval and decision support
→ Enabling teams to operate faster without increasing headcount

Technologies
LangChain, LlamaIndex, LangGraph, vLLM, OpenAI API, Python, FastAPI, Next.js, PostgreSQL, Microsoft Azure AI, PyTorch, Pinecone, ChromaDB, Milvus, Docker, Kubernetes, Terraform, CI/CD