KPI Extraction from Reports - structured and efficient
Not a generic chatbot – a tool built for exactly this task.
Many more features await you.
Instead of investing hundreds of work hours in manual analysis, we automate exactly the steps that can be cleanly and traceably automated.
Background Story
A rating agency faced a massive challenge: analysts had to fight their way through documents with over 500 pages daily to extract quantitative and qualitative information for risk assessments. Every detail counted, every error could be costly.
The manual process not only tied up significant resources, but also increasingly became a bottleneck for business-critical decisions. The central question was not whether automation is possible, but how it can work in such a sensitive area where errors are not an option.
Our Solution
At Klartext AI we know: In finance, "the AI says" is not enough. That's why we developed a solution that not only extracts, but also explains.
- Automated extraction of quantitative and qualitative data from complex documents
- Intelligent verification: Each extraction is provided with a direct reference location in the original document
- Reasoning engine: The AI delivers not only values, but also the reasoning behind them
- Seamless integration: Direct feeding into existing systems without media disruption
- Human-in-the-loop: Analysts can verify each extraction time-efficiently
What distinguishes us: We didn't just unleash a generic LLM on the problem. Instead, we applied Domain-Driven Design and tailored the solution to the specific requirements of the banking sector – including regulatory compliance and audit trails.
The Results
Instead of 4 days of manual extraction – now done in 15 minutes, optimized for human-in-the-loop
Full traceability: every extraction with direct reference to the location in the document
Higher quality through consistency – no more overlooked details
Scalability without additional effort – volume can increase without problems
Complete traceability – every extraction with source reference
What We Learned
Successful AI implementation in the financial sector requires three things:
- Trust through transparency: Every AI decision must be traceable
- Domain expertise: Generic solutions don't work in regulated industries
- Evaluation-Driven Development: Systematic measurements from the start, not as an afterthought
The solution is running in production today and has changed the way our client works with documents. Not because the AI is perfect – but because it is transparent, reliable, and tailored to real business processes.
