RCM
Preventable denials are often driven by incomplete or incorrect data captured in documents.
We use AI to process intake forms, referrals, and clinical documentation, validate data against payer rules, and flag high-risk claims before submission—so errors are fixed upstream.
20–30% fewer preventable denials
Higher first-pass acceptance
Trusted by 15K+ Businesses
Impact:Claims are denied for issues that could have been prevented
- Missing prior authorization in referral or intake documents
- Incomplete or inconsistent clinical documentation
- Eligibility and demographic mismatches
- Coding gaps due to unstructured or unclear notes
Processes Source Documents
Reads intake forms, referral documents, clinical notes, and supporting PDFs
Extracts and Structures Data
Captures patient demographics, payer details, clinical context, and authorization information
Validates Against Payer Rules
Checks completeness, eligibility, and documentation requirements before submission
Flags High-Risk Claims
Identifies claims likely to be denied and surfaces them for correction
Before
Errors identified after submission
Reactive denial management
High rework and delays
After
Errors caught before submission
Cleaner claims entering billing workflows
Teams focus on exceptions, not rework
Faster resolutions, better cash flow, higher recoveries
- Fewer preventable denials
- Higher first-pass yield
- Faster reimbursement cycles
- Reduced cost to collect
This use case connects directly to your core system:
Documents (ERAs, EOBs) → Data (denial reason, claim context) → Workflow (prioritize, route, resolve) → Revenue (cash recovery)
