One AI system reads 12,000 commercial loan agreements a year – work that used to swallow the equivalent of 173 full-time employees. Error rates fell from 5% to near-zero. Estimated value: $150M+ a year.
Annual manual review time, erased
Estimated annual labor value
Data-extraction error rate
Read, parsed & cross-checked
Commercial loan agreements are dense legal documents – 50 to 200 pages each. Every one needs a careful read for the things that actually matter: interest-rate formulas, covenants, collateral requirements, default triggers, compliance terms.
For years that job fell to junior lawyers and loan officers, extracting and cataloguing the same kinds of clauses by hand, document after document. It added up to roughly 360,000 hours a year – the equivalent of 173 people working full-time, all year, on review alone. A complex agreement could take two weeks. And being human work, it carried a ~5% error rate on data extraction – mistakes that turn into disputes, servicing errors and compliance risk down the line.
COiN – short for Contract Intelligence – uses machine learning and natural-language processing to read an entire agreement, extract 150+ data points, flag unusual clauses, spot missing provisions, and cross-reference everything against the bank's own standards. All in seconds, not weeks.
Illustrative – representative of the extraction COiN performs on each agreement.
hours of manual review erased every year – roughly 173 full-time employees' worth of work, redirected.more than 41 years of nonstop work
estimated annual value once you fold in faster processing, lower error rates and stronger compliance.
a complex agreement that once took two weeks is now read in seconds.order-of-magnitude, not marginal
data-extraction errors driven to near-zero – fewer disputes, fewer servicing mistakes, less risk.
The lawyers freed from review didn't lose their jobs – they moved to the work that actually needs judgment.The pattern behind every good automation
You don't have JPMorgan's tech budget – and you don't need it. Every business has its own version of this: the support tickets, the data entry, the paperwork, the same handful of documents read by hand, week after week.
That's the work we put a machine on. We find where your hours leak, build the system that absorbs it, and run it – and we prove the number on your real data in 30 days, or you pay nothing.
JPMorgan saved $150M from one workflow. Your number is smaller. It's also a lot closer than you think.
A 30-minute call with our senior team. You'll leave knowing what's automatable in your business, the number we'd go and hit, and how the money-back pilot works – whether you hire us or not.
30 minutes of pure value – no slide deck, just your numbersOr reach us directly · Telegram · info@beawhale.io
Figures from public reporting on JPMorgan Chase's COiN (Contract Intelligence) platform, including AIBusiness.vc, The Independent, Futurism and the American Bar Association Journal. The 360,000-hour figure, 12,000 annual agreements, ~150 extracted attributes, reduced error rate and $150M+ estimated value are drawn from these accounts. The extraction window above is illustrative. BeAWhale is not affiliated with JPMorgan Chase – this story is shared as an industry reference of what document-automation AI makes possible.
