Operating Document-Intelligence Systems with Governance
by Sebastian Aschwanden, Co-Founder & Chief AI Officer
Document-intelligence systems are often evaluated only by model accuracy. In production, long-term success depends on operational reliability and governance.
1. Build for data variability from the start
Scanned documents vary in quality, structure, and language. Pipelines should include robust preprocessing, fallback handling, and clear confidence signaling for downstream workflows.
2. Separate extraction from business interpretation
Keep OCR/CV extraction layers modular and map outputs into business-specific schemas in a separate step. This makes systems easier to maintain as document formats evolve.
3. Define human-in-the-loop controls
Operational teams need clear paths to review low-confidence outputs and correct records. Human oversight should be designed as part of the system, not treated as exception handling.
4. Operationalize with measurable controls
Production readiness requires monitoring for throughput, error classes, and data-quality drift. Governance teams should be able to review these metrics without reverse-engineering the entire stack.
Document intelligence creates the most value when engineering quality and operational governance are treated as one system.