Where AI and data create value
AI is most valuable when it removes repetitive work or improves decisions: answering customer questions, reading invoices, predicting demand, spotting defects on a production line. Behind every good AI solution is clean, accessible data – so data engineering and analytics are part of the same discipline.
We start small with one measurable use case, prove the value with a prototype, then integrate it into your systems. You can see an example on this website: the AI project assistant that interviews visitors and prepares a brief for our team.
AI & data solution segments
AI assistants & chatbots
Answer questions 24/7 on your website, in apps or internally.
- Customer support & lead qualification
- Internal knowledge assistant
- Multilingual (DE/EN and more)
- Hand-over to humans
RAG – AI over your documents
AI that answers from your manuals, contracts and policies.
- Document ingestion & search
- Answers with source citations
- Access rights respected
- Keeps data in your control
Document automation
Read, classify and extract data from documents.
- Invoices, delivery notes, forms
- Email triage and routing
- Validation workflows
- Export to ERP/DATEV
AI agents & workflow automation
AI that performs multi-step tasks with your tools.
- Research & report drafting
- CRM updates
- Approval gates for safety
- Audit logs
Forecasting & prediction
Machine learning on your historical data.
- Demand & sales forecasting
- Churn prediction
- Predictive maintenance
- Pricing support
Computer vision
Understand images and video.
- Quality inspection
- Object counting & detection
- OCR on photos
- Satellite & drone imagery
Data engineering
Reliable pipelines from source systems to analytics.
- ETL/ELT pipelines
- Data warehouse / lakehouse
- Data quality checks
- Scheduling & monitoring
BI & dashboards
One version of the truth for management.
- KPI dashboards
- Self-service reporting
- Automated reports
- Embedded analytics
How we deliver AI & data projects
- Use-case workshopIdentify processes with high volume and clear success criteria; estimate value and feasibility.Deliverables: prioritised use-case list
- Data assessmentCheck availability, quality, volume and legal basis (GDPR) of the data.Deliverables: data report, risks
- Proof of conceptQuick prototype on real data to measure accuracy and usefulness.Deliverables: working prototype, evaluation results
- Evaluation & safetyTest sets, quality metrics, failure cases, guardrails and human-in-the-loop design.Deliverables: evaluation report
- IntegrationConnect to your website, apps, ERP, CRM or document storage; build the user interface.Deliverables: integrated solution
- Rollout & trainingPilot users, feedback, training and documentation.Deliverables: production go-live
- Monitoring & improvementTrack quality, costs and usage; improve prompts, models and data over time.Deliverables: monthly quality report
Example use cases by industry
Technology stack
| AI models & APIs | Claude (Anthropic)Open-source LLMsEmbeddingsVision models |
| Frameworks | PythonPyTorchscikit-learnLangGraphFastAPI |
| Data | PostgreSQL / pgvectordbtAirflowDuckDBBigQuery |
| BI | Power BIMetabaseApache SupersetLooker Studio |
Frequently asked questions
Is it safe to use AI with our company data?
Yes, with the right setup: business API terms that exclude training on your data, EU hosting where needed, access control and logging. We document data flows for your GDPR records.
Will an AI chatbot give wrong answers?
Any AI can make mistakes. We reduce this with retrieval from your verified documents, citations, clear limits on what the bot answers, and hand-over to a human.
How quickly can we start?
A proof of concept is often ready in 2–4 weeks, so you can judge value before investing more.