Intelligent automation
Less time spent processing documents and entering data
A scenario where staff read similar information from different files and forms and enter it manually into a system.
This page presents a conceptual scenario, not a real customer project. It illustrates problem analysis, scope definition, and the choice of evaluation criteria. None of the improvements described are measured results from a real project.
What problems does the current situation create?
Repeated data entry is tiring, and errors can create follow-up costs later. Document formats also vary, and some information requires human judgment.
Users and stakeholders
Data entry staff, quality reviewers, operations managers, security staff, and users of the final data.
Proposed approach
How does the solution take shape step by step?
The system identifies document types and fields, shows suggested information with its source and confidence level, and holds uncertain cases for human review. Corrections are recorded to monitor quality.
Real data samples, processes, and constraints must be reviewed before choosing technology. This scenario illustrates only one possible approach.
Conceptual scope
Areas explored in this scenario
This list is not a contractual scope or quotation. First-version features are defined after understanding users and dependencies.
Suggested metrics
How would we measure success in a real project?
Extraction accuracy
Measure each field's accuracy on a separate evaluation set.
Processing time
Record system time and human review time separately.
Referral rate
Measure the percentage of cases requiring human review.
Final error rate
Monitor errors remaining after quality checks.
These metrics are not achieved results. In a real project, they must be defined before implementation and then measured.
Assumptions and risks
What needs investigation before implementation?
- Poor scan quality
- Changing document formats
- Personal data
- Overreliance on the model
- Missing evaluation data
- Processing costs
- Audit requirements
AI and data analysis for real problems
AI feasibility assessment with quality criteria, human oversight, and confidentiality.
