AWS Document Processing Evaluation
Comparative evaluation of AWS document intelligence services for extraction accuracy, cost, and integration fit.
- Role
- Cloud & AI Engineer
- Timeline
- 2024
Overview
Project summary and scope.
A structured evaluation project comparing AWS document processing options for a production-bound workflow. The work benchmarks extraction quality, operational characteristics, and integration complexity to produce actionable architecture recommendations.
My Contributions
Key areas of ownership and delivery.
- Defined benchmark criteria for accuracy, latency, cost, and operability
- Built sample document test set and extraction comparison workflow
- Implemented evaluation scripts and output normalization for analysis
- Summarized Textract vs alternative approach tradeoffs for stakeholders
- Documented recommended integration pattern for downstream pipelines
Core Features
Primary capabilities delivered in this project.
- Benchmark harness for document extraction services
- Normalized output comparison across providers/methods
- Cost and latency measurement placeholders
- Sample extracted output gallery for review
- Recommendation summary for architecture decisions
Architecture
How the system is structured at a high level.
Documents are stored in S3 and processed via Lambda-triggered extraction jobs. Outputs are normalized into a common schema for comparison. Metrics feed into a summary layer that supports stakeholder review and downstream pipeline design.
Impact
Outcomes and value delivered.
- Provided evidence-backed recommendations for document processing architecture
- Reduced uncertainty in service selection through structured benchmarks
- Created reusable evaluation methodology for future document workflows
Challenges
Constraints and difficulties encountered during delivery.
- Normalizing outputs from different extraction approaches for fair comparison
- Selecting representative documents without exposing sensitive content
- Balancing benchmark depth with delivery timeline constraints
Tradeoffs
Key decisions and the reasoning behind them.
- Focused on operationally relevant samples rather than exhaustive document coverage
- Used placeholder cost modeling where live billing data was unavailable
- Prioritized integration clarity over maximum extraction accuracy in early tests
Future Improvements
Next steps that would strengthen or extend this work.
- Expand benchmark suite with domain-specific document types
- Automate regression testing when service models or APIs change
- Add human review scoring for extraction quality validation
- Integrate chosen approach into production ingestion pipeline
Problem and Solution
Problem
The team needed to select a document processing approach for incoming unstructured files but lacked evidence-backed comparisons across accuracy, latency, cost, and integration effort.
Solution
Ran a controlled evaluation across AWS document intelligence services using representative sample documents, captured structured comparison results, and delivered a recommendation summary with integration guidance.
Tech Stack
Technologies used across this project.
- AWS Textract
- AWS Lambda
- S3
- Python
- CloudWatch