Choosing the right approach to AI depends on your process complexity and data types. This comparison helps leaders evaluate the options within our AI strategy framework.
Approaches Compared
| Cognitive Automation | Traditional RPA | Pure Consulting | Process Mining | |
|---|---|---|---|---|
| Unstructured Data | Documents, emails, images | Structured fields only | Analysis only | Log data only |
| AI Models | Proprietary technology + LLMs | Rule-based | Recommendations | Detection, no action |
| Autonomous Agents | Multi-agent systems | Simple bots | ||
| Process Understanding | End-to-end | Single steps | Strategic | Process analysis |
| Implementation | In-house team builds & operates | License partner | Handoff to IT | Dashboard |
| Time to Value | Pilot in 4-8 weeks | Months | Months to years | Weeks (analysis only) |
| Data Sovereignty | Germany/EU, ISO 27001 | Cloud-dependent | Not applicable | Cloud-dependent |
When to Choose Which Approach
Cognitive Automation
Best for: Organizations with knowledge-intensive processes where decisions depend on unstructured data. Cognitive automation handles documents, emails, and images that rule-based tools cannot parse. It learns from your domain-specific data and improves over time, making it the right choice when your processes involve ambiguity, variation, or professional judgement.
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Traditional RPA
Best for: Simple, rule-based processes with structured inputs. RPA works well when the data arrives in consistent formats and the process follows predictable logic. However, it breaks down when inputs vary, which is why most enterprises hit an automation ceiling at 20-30% of their workflows. If your documents come in different layouts or languages, RPA alone will not scale.
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Pure Consulting
Best for: Strategic planning and change management without proprietary technology. Consulting firms deliver frameworks, organizational assessments, and executive alignment. The limitation is execution: recommendations require a separate implementation partner, which introduces handoff risk, timeline delays, and accountability gaps between strategy and delivery.
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Process Mining
Best for: Analyzing existing processes based on system log data. Process mining tools visualize how work actually flows through your systems, often revealing bottlenecks that management did not know existed. The constraint is that process mining diagnoses but does not treat. It tells you where the inefficiencies are, but you still need an implementation partner to automate them.
What Sets Us Apart
Own Technology
Purpose-built AI systems with multimodal LLMs and multi-agent architectures. No reselling, no license partnerships →. We build and maintain the technology stack ourselves, which means we can adapt it to your specific requirements without waiting on a third-party vendor's roadmap. When an edge case appears in production, our engineering team fixes it directly.
Sovereign Infrastructure
Hosted in Germany, ISO 27001 certified, GDPR compliant. No US cloud dependency. See all AI strategy → capabilities. For regulated industries, data residency is not optional. Our infrastructure runs in German data centres under EU jurisdiction, which means your data never leaves a legal framework you control. This matters especially for financial services, healthcare, and public sector clients subject to BaFin, gematik, or BSI oversight.
Since 2016
Founded before the AI boom. 462+ expert articles, 50+ enterprise projects, profitable without external funding →. While most AI consultancies launched after ChatGPT, we have been deploying production AI systems for nearly a decade. That history means we have already encountered, and solved, the integration challenges, data quality issues, and organizational resistance patterns that newer firms are still learning about.