Data, Analytics & AI
We integrate AI models and enterprise data into teams and processes, making them manageable, measurable, and impactful.
Service Portfolio
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Agentic AI Implementation
We integrate GenAI and intelligent agents into teams and processes, making their use secure and repeatable.
Our approach: Clear AI prioritization, standardized workflows, structured templates and validation steps, as well as defined roles and approval mechanisms to ensure secure usage.
Teams will be able to: Execute recurring tasks using GenAI within clearly defined processes and deploy selected agents along a structured lifecycle.
You will notice: Shorter cycle times, consistent quality, fewer follow-ups, and a clean transition into operations with clear ownership.
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Tailored Analytics & Reporting
We make impact and progress measurable and controllable, enabling focused and scalable investment decisions.
Our approach: KPI frameworks with baselines and target values, portfolio governance for use cases and agents, and a structured decision cadence.
Teams will be able to: Measure impact, prioritize initiatives, stop or scale them as needed, and report progress transparently to leadership and business units.
You will notice: Clear investment decisions, visible value creation across functions, and a pipeline that reliably scales from pilot to enterprise-wide deployment.
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AI Data Strategy & Enablement
We establish the data foundation that enables AI to deliver reliably—without failing due to limited access or poor data quality.
Our approach: Integration of relevant data sources, cleansing and harmonization, defined quality standards, and clear data ownership and access governance.
Teams will be able to: Find, understand, and use data reliably—without manual exports or continuous alignment loops with IT.
You will notice: Fewer data errors, faster analysis cycles, reliable KPI metrics, and stronger outcomes in AI-supported processes.
Success Stories
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AI Training & Governance (MedTech)
Hybrid AI System | Custom Frontend | Human-in-the-Loop Workflow | Training Framework
Company: Dornier Group
Industry: Infrastructure & Energy Services
Size: approx. 700 employeesChallenge: A service provider in a highly regulated energy sector needed to classify and reorganize large volumes of documentation but risked being overwhelmed by manual effort, while simultaneously preparing its workforce for the AI era.
Solution: We implemented a hybrid AI platform (custom models & GPT) with a tailored frontend for management. In parallel, we established human-in-the-loop processes for continuous model refinement and launched an ongoing upskilling program for teams.
Impact: The result was a highly automated process with continuously improving AI accuracy driven by user feedback, while employees successfully evolved from task executors to confident AI supervisors.
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Automated Contract Extraction & Privacy-Compliant AI Deployment
Secure LLM Pipeline | Custom Validation Frontend | Human-in-the-Loop Workflow | Contract Data Schema
Company: Axel Springer
Industry: Media & Technology
Size: approx. 18,000 employeesChallenge: As part of a system migration, thousands of supplier contracts needed to be transferred into a new data schema. The challenge was to flexibly extract complex information without months of model training — while complying with the strictest data protection requirements.
Solution: We implemented an LLM-based pipeline for classification and extraction (zero-shot approach) that meets rigorous security standards. A custom frontend enabled an efficient human-in-the-loop process for rapid validation of AI-generated results.
Impact: High degree of automation in the migration and structuring of contract data. The combination of AI-driven extraction and targeted manual validation (via frontend) ensured maximum data security and quality.
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AI Governance & Compliance Framework
AI Policy | Risk Assessment Framework | Compliance Audit Routine | Employee Training Curriculum
Industry: Advanced Materials & Manufacturing
Size: approx. 3,500 employeesChallenge: A global market leader in the technology sector was accelerating its digital transformation but faced the challenge of embedding AI innovation into the organization in a legally compliant and ethical manner. A structured framework was missing to efficiently assess risks without slowing innovation velocity.
Solution: We established a multidisciplinary governance model integrating all relevant stakeholders (IT, Legal, HR). Core elements included the development of a global AI policy, a risk classification framework, and accompanying training programs and audit routines.
Impact: Creation of a secure corridor for AI innovation. Teams can now operate with confidence, supported by clear guardrails and processes that ensure compliance and make risks transparent.
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Executive AI Activation
AI Strategy Roadmap | Prioritization List | Governance Backlog
Industry: MedTech
Size: approx. 9,000 employeesChallenge: A global software unit wanted to capture AI potential without getting blocked by unclear decision paths, compliance questions, and missing capabilities across the organization.
Solution: We designed and facilitated a leadership offsite featuring knowledge transfer, live demos, and working sessions. Together, we translated strategic goals into prioritized use cases, defined roles and approvals, and a tangible draft roadmap for execution.
Impact: The leadership team agreed on concrete AI priorities and next steps, and established clarity across governance, compliance, and capability building.