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Production AI · Document Intelligence

Production AI Reliability and Document Intelligence

A method-focused public case study on evaluating document-intelligence reliability beyond OCR accuracy alone.

Method-only public case study · sensitive operational details omitted

Synthetic invoice

ProviderExample Vet
Invoice IDINV-204
Total$184.90
Tax$16.81
Line items04

Extraction trace

invoice_total0.97validated
provider_name0.74review
line_item_tax0.42mismatch

Synthetic method illustration

This fictional invoice trace demonstrates the evaluation method without exposing client data.

Scope

Role and problem

My role: AI/ML Research and Development Intern

Document intelligence can fail before, during, or after OCR. Reliable evaluation must inspect the full path from ingestion to extraction, transformation, validation, and review without exposing private records or proprietary implementation details.

Architecture

System flow

01

Document intake

02

OCR configuration

03

Structured extraction

04

Transformation rules

05

Validation dependencies

06

Error-code review

07

Operational recommendation

Evidence

Measured signals

OCR

Configuration comparison

Compared extraction behaviour across OCR configurations and transformation choices.

FAR / FRR

Operational trade-off framing

Reviewed false-acceptance and false-rejection implications in adversarial evaluation workflows.

E2E

System reliability

Shifted evaluation from model-only accuracy toward traceability, reproducibility, latency, cost, and validation dependencies.

Public scope: Public visuals must use synthetic or anonymised data only. Do not publish client invoices, internal screenshots, detector logic, or confidential metrics.

Contribution

  • Built a repeatable evaluation workflow across OCR configurations, field mappings, confidence scores, and error codes.
  • Reviewed reruns and edge cases while preserving traceability and review boundaries.
  • Translated findings into system-level recommendations rather than treating OCR quality as the entire problem.

Lessons

  • Reliability is a pipeline property, not a single model metric.
  • Traceability changes debugging from anecdotal investigation into a repeatable engineering process.
  • Synthetic public demonstrations can explain a method without violating confidentiality.

Limitations

  • Client data, internal detector logic, and confidential operational metrics are intentionally omitted.
  • The synthetic public visual demonstrates method structure, not production performance.
  • A public benchmark is inappropriate unless a publishable dataset and protocol are available.

Stack

  • Python
  • pandas
  • AWS S3
  • boto3
  • Azure Document Intelligence
  • JSON
  • OCR Evaluation