Healthcare runs on documents — most of them scanned, faxed, or formatted differently by every payer. Our document intelligence pipeline reads, structures, and validates this data automatically, so your team spends time acting on information instead of hunting for it.
For most revenue cycle teams, the real bottleneck isn’t a lack of data — it’s that the data arrives in a hundred different shapes. One payer’s EOB is a clean digital PDF; another’s shows up as a blurry fax with hand-stamped annotations. A referral might come through a portal, an email attachment, or a phone call transcribed by hand. Every one of those formats has to be read, understood, and turned into something your billing system can actually use, and doing that manually is where hours disappear every week.
Over time, as our models see more of your specific document mix — your specific payers, your specific referral sources — accuracy on that mix improves further, which is why the first few weeks of an engagement often focus specifically on the document types you process most often, rather than trying to handle every format equally well on day one.
Built for every document your revenue cycle depends on
EOBs & ERAs
EOBs and ERAs from every major payer format, including scanned and faxed documents that standard OCR tools typically fail to read accurately.
Prior Auth Forms
Prior authorization forms and supporting clinical documentation required for payer submission.
Referrals
Referrals and intake paperwork, regardless of whether they arrive through a portal, a fax, or an email attachment.
Faxed Correspondence
Faxed correspondence and other high-volume administrative documents that pile up in shared inboxes.
Patient Charts
Patient charts and clinical notes relevant to billing and reimbursement decisions.
Documents are ingested and processed through OCR and NLP models trained on healthcare-specific formats and terminology. Extracted data is structured, flagged for anomalies, and passed through human validation before it reaches your systems of record. The process runs in three stages.
- 01
Ingest
Documents are ingested from wherever they currently live — a shared drive, a fax server, an email inbox, or a direct system integration — so your team doesn't have to change how documents arrive before we can process them.
- 02
OCR/NLP Extraction
Our OCR and NLP models extract and structure the relevant fields, flagging anything that looks inconsistent, incomplete, or unusual for further review.
- 03
Human Validation
A human validator confirms the flagged items and spot-checks a sample of the rest, so the data that ultimately lands in your system of record has been through both an automated and a human check.
A misread adjustment code or missed field isn’t a minor error — it can delay a claim, misstate patient responsibility, or trigger a downstream denial. We design for precision first, speed second.
This is also why we don’t chase 100% automation for its own sake. A document intelligence pipeline that processes every document without exception sounds impressive, but if it’s silently guessing on the 3% of documents it can’t confidently read, that 3% becomes a hidden source of errors your team won’t discover until weeks later during reconciliation. We’d rather flag that 3% honestly and route it to a human than pretend the model handled it.
We also track accuracy separately by document type and by payer, since a model that performs well on average can still be quietly unreliable on a specific document format your team happens to process a lot. That level of detail is what lets us tell you honestly where the pipeline is strong and where it still needs a closer human check.
A member of our team will reach out within one business day to schedule a short discovery call — no obligation.