Agentic Workflow Design
Multi-step AI workflows where each process step hands off to the next automatically — and escalates only when it genuinely needs a human.
AI Process Automation
Less Friction, More Flow
Repetitive workflows absorb capacity that should go to real work. We automate them end-to-end — with AI that decides, hands off, and pulls people in only where judgment matters.
The process isn't broken. It's just manual.
Most operational bottlenecks aren't a staffing problem. They're a process structure problem — built for a world without AI. Classification tasks that take hours. Email chains that generate data nobody captures. Knowledge buried in individual inboxes.
We build systems where AI handles the repeatable work and your team handles what's actually complex. Every decision is transparent and auditable. Every automated step has a fallback. Humans stay in control — they just spend their time differently.
What We Do
Document & Data Extraction
Emails, PDFs, spreadsheets, forms: parsed and turned into structured data without manual intervention.
Intelligent Routing & Matching
The system determines the right next step — which partner to contact, which category applies, which case to escalate — based on extracted data and historical patterns.
Knowledge Base Integration
Every automated interaction builds the knowledge base. Decisions, alternatives, and outcomes accumulate in structured form, searchable by the whole team.
Human-in-the-Loop Architecture
Every AI decision comes with confidence scores and fallback paths. Edge cases surface to the right person, with full context.
LLM Cost & Quality Control
Every model call tracked by use case and volume. Fallback logic between providers. Automation stays economically viable as volume scales.
Here's how we work
AI Process Automation works when it's built for production from day one — not retrofitted after a pilot runs out of steam.
That means starting with a discovery phase: mapping which process steps are worth automating, validating the technical approach with real data before writing production code, and identifying the places where a human needs to stay in the loop. We use AI-assisted prototyping in this phase to test classification logic, email parsing accuracy, and data extraction quality — before any of it goes live.
The systems we build follow Domain-Driven Design with clear bounded contexts. Each process domain can evolve independently. When a model update breaks an output format, the fix stays localized. When a new workflow step gets added six months later, it slots in without re-architecture.
We use multiple LLM providers — typically OpenAI, Anthropic and Google — with fallback logic between them. Prompts are optimized per use case. Cost is tracked. Everything is auditable.
Case Study: AI Process Automation at Amec
AI Tech Stack
AI projects rarely falter because of the technology, but because they never truly make it to production. We build for production-readiness from day one: fallback mechanisms between models, LLM cost controls, human-in-the-loop. That's how AI integration actually matures.
Matthias Gronwald
Co-CTO
Turbine Kreuzberg
Process issues?
Tell us what's slowing your team down. We'll tell you what's worth automating and how.
- Timothy Becker
- Director Business Development
- timothy.becker@turbinekreuzberg.com
- +4915110633496