AI for Energy & Real Estate: Turning Documents and Land Data into Revenue
Deeds, leases and land records hold your revenue — locked in handwriting, PDFs and county formats. Here's how document AI and GIS extract and track ownership at machine speed.
In energy, land and real estate, a surprising amount of revenue sits trapped in documents no software can read at scale — handwritten deeds, scanned leases, county records in a hundred formats. Someone reads each one by hand, extracts the terms, and types them into a system. It’s slow, error-prone, and it hides opportunities that are invisible simply because the data is unreadable in bulk.
What AI changes
Document intelligence. LLMs, fine-tuned on your document types, extract royalty percentages, parties and terms from handwritten and scanned records — with a confidence score on every extraction and a human reviewing the low-confidence ones.
GIS and mapping automation. Automated mapping of metes-and-bounds descriptions, satellite imagery processing and parcel-level intelligence.
Ownership-chain tracking. Keep title chains current and flag conflicts before they become legal problems.
Deal-flow intelligence. Turn processed records into a searchable, alertable pipeline — surfacing opportunities the manual process would never reach.
The proof
We built a system that processes deeds 90% faster with zero critical errors and surfaced $8.5M in new leases in a single quarter — data that was previously trapped on paper. (See the Glacier Analytics story.)
The accuracy question
These documents move money, so accuracy isn’t optional. We build the evaluation suite first, measure against ground truth, and route anything below a confidence threshold to a human. The result is machine speed with the accuracy the stakes demand.
Where to start
A two-week Sprint on a sample of your real documents shows you extraction accuracy on your data before you commit. See how we build AI for energy & real estate, or get a free AI roadmap.