Invoice processing AI
You get invoices captured at header and line-item level and checked before anything posts. Line items must sum to the stated subtotal, tax must match the rate that applies to the vendor, and each invoice must pass a three-way match against an open purchase order and goods receipt. Vendor name, tax ID and remit-to bank details are checked against your vendor master, so a changed bank account is flagged before payment. Duplicates are caught by vendor, amount and date, including resubmissions carrying a new invoice number, and credit notes are matched to the invoice they reverse. Your ERP receives a posting-ready record, and every exception lands in your accounts payable queue with its reason attached.
Claims document AI
You get a claim file that arrives sorted. A mixed packet is split into individual documents and each page is classified: first report of injury, CMS-1500 and UB-04 bills, medical narratives, correspondence and policy documents. Pages that match no known type go to a reviewer marked unclassified instead of being forced into the wrong schema. ICD-10 diagnosis codes and CPT procedure codes are extracted and format-checked, and billed line amounts are totaled against the claim total. Medical narratives, the free-text part of the file, are structured into the facts an adjuster needs, which is the work ClaimClarity runs in production. The handler opens one record with every value linked to its source page.
Contract data extraction
You get the terms that carry risk as fields: parties, effective and expiry dates, renewal terms and notice periods, payment terms, liability caps, indemnities and governing law. Amendments are linked to the master agreement they modify, so your register shows the terms in force today instead of the terms first signed. Each clause is compared against your own playbook, and a missing clause, a non-standard cap or an unexpected governing law goes to legal review before the contract is filed. The result is a register of obligations across every contract you hold, with each field linked to the clause and page it came from.
OCR pipelines for scans and handwriting
You get an OCR layer chosen for your documents, not by habit. Pages are deskewed, denoised and split before recognition, because recognition quality depends more on the input image than on the engine. The engine can differ by document type: a clean digital PDF needs text extraction and no OCR at all, while a faxed form needs full recognition. Handwritten entries carry their own confidence score, and low-confidence words route to review, never to a guess. The full layer list is in section 12.
