Extract Structured Lease Terms for a Commercial Real-Estate Platform
Overview
What this challenge is about.
Extract Structured Lease Terms for a Commercial Real-Estate Platform. Advanced challenge in code. Writing production code that solves real engineering proble...
The Brief
What you'll do, and what you'll demonstrate.
Automate per-field lease extraction at 95% accuracy with a human-review fallback for low-confidence rows.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Build a hybrid rule + ML extraction pipeline on real PDF data
- Calibrate per-field confidence to route to human review
- Evaluate IE accuracy per field, not just overall
- Translate extraction performance into deployment recommendations
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Information Extraction
Apply information extraction to solve real industry problems and demonstrate production-level capability.
- Pdf Parsing
Apply pdf parsing to solve real industry problems and demonstrate production-level capability.
- Named Entity Recognition
Apply named entity recognition to solve real industry problems and demonstrate production-level capability.
- Human In The Loop
Apply human in the loop to solve real industry problems and demonstrate production-level capability.
- Confidence Calibration
Apply confidence calibration to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
NLP Engineer
Owning an IE pipeline on real PDFs with calibrated confidence is the day-one work of NLP engineers at any vertical-document AI startup.
This challenge sharpens
- information-extraction
- named-entity-recognition
- pdf-parsing
AI Engineer
Wiring the human-in-the-loop fallback plus the rollout plan is core AI-engineer work at vertical-AI vendors.
This challenge sharpens
- human-in-the-loop
- confidence-calibration
- pdf-parsing
Data Engineer
Designing the extraction pipeline and gold-set evaluation is the data-engineering backbone of any IE product.
This challenge sharpens
- information-extraction
- evaluation
- pdf-parsing