Gain control over fragmented, outdated, or incomplete data — and automatically improve it with AI.
Sound familiar?
- "We know our data is unreliable, but cleaning it up is too much work."
- "Everyone builds their own dashboards — but we have no idea if the data can be trusted."
- "Our customer data is chaos: duplicates, outdated, missing."
- "Integrating data? That takes us months of manual puzzle work."
- "AI? We can’t apply it — our data isn’t good enough."
What is AI4DI™?
AI4DI™ is Caronne’s methodology to measure, repair, and structure your data quality using AI. Instead of endless manual checks, we deploy intelligent models that automatically detect inconsistencies, remove duplicates, and enrich records.
When should you use AI4DI™?
- Your dashboards are unreliable due to inconsistent or incomplete data
- You work with many systems but lack a unified customer, product, or project structure
- Your data teams spend too much time manually integrating sources
- Your analysts are stuck fixing errors instead of generating insights
- You want to apply AI — but know the foundation must be fixed first
What does it deliver?
- Data quality insight: Reports on errors, duplicates, gaps, inaccuracies
- Automated cleaning & suggestions: AI algorithms propose corrections and enrichment
- Stronger integrations: Customer, project, and financial data seamlessly connected
- Trustworthy dashboards: Better control of margins, planning, costs, and performance
- Ready for AI adoption: A solid foundation for forecasting, text recognition, or process optimization
How does it work?
- Assessment: Together we identify your biggest data challenges
- Quick Scan™: Using our AI tools, we analyze your data
- Action plan: You receive a concrete roadmap with quick wins and long-term steps
- Implementation: We support with rollout and training
Who is AI4DI™ for?
- CFOs & controllers seeking more reliable numbers
- IT & data managers aiming to solve structural data issues
- Teams that want to use data smartly but currently can’t trust it
- Organizations striving for AI but still living in Excel
Example cases
- Government agency: 22% fewer customer file errors, faster reporting
- Infrastructure company: Unified project code structure across 4 ERPs
- Financial services provider: AI models now run on cleaned, reliable data sources
Interested?
Schedule an introduction or request a sample report:
