Doubled patient revenue bottlenecked manual Excel reconciliations. Now, AI matches complex transactions, freeing accountants for strategy.
A rapidly expanding medical organization tied to a leading educational institution manages complex financial operations across multiple medical centers, recently experiencing doubled patient revenue.
Completely Excel-based reconciliation processes became unmanageable as the organization scaled and transaction volumes multiplied. Accounts...
“When I started, the reconciliation processes were entirely Excel-based, cumbersome, and prone to inefficiencies. It wasn't manageable at scale, especially as our patient revenue doubled and accounting demands expanded.”
Faculty medical group for The Ohio State University Wexner Medical Center.
Accounting workflow automation platform for financial close management.
Related implementations across industries and use cases
A team manually reconciled thousands of transactions, working until 1 a.m. at month-end. Now, an AI matches most transactions, and they finish by noon.
Accountants spent 5 hours on AR reconciliation just to see if a problem existed. Now, AI matches transactions, isolating true issues in minutes.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
A team manually reconciled thousands of transactions, working until 1 a.m. at month-end. Now, an AI matches most transactions, and they finish by noon.
Accountants spent 5 hours on AR reconciliation just to see if a problem existed. Now, AI matches transactions, isolating true issues in minutes.
Compliance risks held back innovation. Engineers now use Claude Code to validate every PR for privacy and write tests 80% faster.
Doctors spent 3 hours nightly reviewing complex records. Claude now synthesizes 300-page referrals into instant patient histories.
Finance teams managed 1.5M transactions via scattered spreadsheets. Now, AI agents extract data and route journal entries in seconds.
Retrieving non-standard data took days of back-office requests. Executives now query AI in plain English for answers in seconds.
Doubled patient revenue bottlenecked manual Excel reconciliations. Now, AI matches complex transactions, freeing accountants for strategy.
A rapidly expanding medical organization tied to a leading educational institution manages complex financial operations across multiple medical centers, recently experiencing doubled patient revenue.
Completely Excel-based reconciliation processes became unmanageable as the organization scaled and transaction volumes multiplied. Accounts...
“When I started, the reconciliation processes were entirely Excel-based, cumbersome, and prone to inefficiencies. It wasn't manageable at scale, especially as our patient revenue doubled and accounting demands expanded.”
Faculty medical group for The Ohio State University Wexner Medical Center.
Accounting workflow automation platform for financial close management.
Related implementations across industries and use cases
A team manually reconciled thousands of transactions, working until 1 a.m. at month-end. Now, an AI matches most transactions, and they finish by noon.
Accountants spent 5 hours on AR reconciliation just to see if a problem existed. Now, AI matches transactions, isolating true issues in minutes.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
A team manually reconciled thousands of transactions, working until 1 a.m. at month-end. Now, an AI matches most transactions, and they finish by noon.
Accountants spent 5 hours on AR reconciliation just to see if a problem existed. Now, AI matches transactions, isolating true issues in minutes.
Compliance risks held back innovation. Engineers now use Claude Code to validate every PR for privacy and write tests 80% faster.
Doctors spent 3 hours nightly reviewing complex records. Claude now synthesizes 300-page referrals into instant patient histories.
Finance teams managed 1.5M transactions via scattered spreadsheets. Now, AI agents extract data and route journal entries in seconds.
Retrieving non-standard data took days of back-office requests. Executives now query AI in plain English for answers in seconds.