AI automation: processes that handle themselves
The most valuable automations in a business hide in unstructured work: reading email, checking documents, writing quotes, moving data between systems. That's exactly what AI does reliably today - when it's integrated properly. Olio is an AI automation agency: we find the processes with the biggest leverage, ship a production pilot in a few weeks, and scale what works. GDPR-compliant, on EU servers, built by senior engineers. Boutique agency from Mönchengladbach, Germany, working remotely across the EU.
Which processes we automate with AI
The most common cases: understanding, classifying, and answering or routing incoming email. Reading invoices, delivery notes, and contracts and posting them into ERP or accounting. Generating quotes and standard documents from CRM data. Pre-qualifying tenders and inquiries. Summarizing reports across multiple systems. Answering internal knowledge questions from company documents. They all share the same shape: unstructured input, clear rules, lots of hours - ideal candidates for AI.
AI automation or classic RPA?
Classic automation and RPA work as long as data is structured and workflows never change - the first free-form email breaks them. LLM-based automation understands language and documents and stays robust when input varies. We combine both: deterministic workflows as the backbone (with n8n or custom code), AI steps where understanding is required, and guardrails with human approval where mistakes are expensive.
GDPR-compliant: AI on EU servers
AI automation doesn't mean dumping customer data into US clouds. We run the workflows on servers in Germany or the EU, use EU endpoints of the model providers or self-hosted models for sensitive data, sign data processing agreements, and build deletion concepts in from the start. You get the productivity of AI without putting your data protection officer on the barricades - ours is at the table from day one.
How we work: audit, pilot, rollout
First: a compact process audit. We look at your workflows and prioritize by hours saved per week versus implementation effort. Second: a pilot in 2 to 4 weeks - one process, in production, with a measurable result. Third: rollout to further processes, with monitoring, error handling, and training. After the pilot you see in plain numbers how many hours per week the automation saves - and decide about scaling on that basis.
Why Olio
AI automation rarely fails at the model - it fails at the integration: brittle connections, no error handling, hallucinations without guardrails. We are software engineers, not prompt tinkerers: workflows are versioned and tested, AI steps have validation and escalation paths, and everything runs on infrastructure we can operate ourselves. As a boutique agency we deliver with senior engineers - and we'll tell you honestly which process is not worth automating.
What you get with us
- Measurable time savings: a pilot in production within 2 to 4 weeks, impact in hours per week
- GDPR-compliant AI: EU servers, EU endpoints or self-hosted models, clean data processing agreements
- Robust integration instead of a prompt demo: validation, guardrails, human approvals
- Connected to your systems: ERP, CRM, email, accounting, industry solutions
- Honest prioritization: we automate what pays off - and tell you what doesn't
- Operations, monitoring, and evolution by us - or documented handover to your team
Core Technologies
Let's find the process with your biggest leverage
Frequently asked questions
What does AI automation cost?
A pilot for one process runs €8,000 to €20,000; rolling out across several processes lands between €20,000 and €75,000, depending on your system landscape and guardrail requirements. Add running costs for operations and model usage, which we calculate transparently up front. After the process audit you get a reliable estimate.
Which processes are a good fit for AI automation?
Processes with unstructured input and clear rules: email handling, document and invoice processing, quote generation, data transfer between systems, internal knowledge questions. Rule of thumb: if a task costs several hours a week and follows a recurring pattern, it's worth evaluating.
How quickly do we see results?
The pilot is in production within 2 to 4 weeks and produces measurable numbers from day one: cases handled, hours saved, error rate. You decide about the rollout based on real results, not a demo.
Can this be done GDPR-compliantly?
Yes. Workflows run on servers in Germany or the EU, models are used through EU endpoints or self-hosted - sensitive data can be pseudonymized before the AI step. Data processing agreements, role and deletion concepts are part of the project scope, not a wish list.
What about hallucinations - can we trust the AI?
Unchecked AI output has no place in a business process. We build validation against your data, define confidence thresholds, and route uncertain cases to humans. In practice the system automates the clear 80 percent and queues the unclear 20 percent for approval - and that ratio improves with every month of production data.
AI automation or classic RPA - which do we need?
RPA replays rigid user interfaces and breaks when anything changes. LLM-based automation understands content and tolerates variation. The answer is usually a combination: deterministic workflows as the backbone, AI where language and documents need to be understood. The audit shows which share is which in your case.
Which tools do you work with - n8n, Make, or custom code?
Tool-neutral: n8n for orchestrated workflows with data sovereignty, custom code (Python, TypeScript) for complex logic and high volume, Make or Zapier only when they genuinely fit. Models come from OpenAI or Anthropic via EU endpoints - or run self-hosted when your data requires it.
Do our employees need training?
The experience is deliberately simple: the automation works in the background, and unclear cases show up as approval tasks in the tools you already use - email, Teams, Slack, or your CRM. For operations and adjustments we train the people it concerns and hand over documentation instead of tribal knowledge.
Does the automation replace employees?
In practice it shifts work: away from retyping, sorting, and searching, toward the cases that need judgment. The companies we work with use the reclaimed time for more throughput with the same team - especially in the Mittelstand, where open positions are hard to fill anyway.
Who operates the system after the rollout?
Either works: we operate the automation with monitoring, alerts, and agreed response times - or your team takes over based on the documentation and training. Many clients start with us operating and take over themselves after a few months.
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