Documents that read themselves
a commercial real estate investment firm
extraction accuracy
less manual keying
to process a full lease
Analysts keyed lease and loan terms from thousands of PDFs by hand, each formatted differently. The work was slow, error-prone, and impossible to scale at deal pace. A single missed clause could reprice a transaction. Leadership wanted reliable extraction they could audit, not a black box that guessed.
How we approached it
Cataloged document types and the exact fields that drive underwriting decisions.
Built an extraction pipeline that reads varied layouts and returns structured, validated data.
Added confidence scoring and human review queues for low-certainty fields.
Benchmarked against analyst-verified ground truth before trusting any field end to end.
“What took an analyst a day now takes minutes, and we can audit every field. We trust the numbers because we can see where they came from.”
Extractions are benchmarked against analyst-verified ground truth, and low-confidence fields are routed to a human review queue rather than passed through silently.
Yes. The pipeline is built to read varied layouts across lease and loan documents and normalize them into a consistent structured output.
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