Applied AI for the work your EMR leaves behind.

• The fax machine is load-bearing

  Prior auths, referrals, and orders arrive as PDFs and faxes. Someone reads them, retypes them into the EMR, and chases the payer. Highest volume, lowest judgment work in the building.

• Every integration is a person

  The EMR doesn’t talk to the payer portal, which doesn’t talk to scheduling. Staff hold it together across browser tabs, and the handoffs are where the errors and denials come from.

• Denials get worked, not prevented

  By the time a claim comes back denied, the documentation gap is weeks old. Nobody checked eligibility and medical necessity before the service, because checking by hand doesn’t scale.

• The pilot stalls at compliance

  A demo works on sample data. Then it has to handle PHI, write back to the EMR, and leave an audit trail, and that’s where most healthcare AI quietly stops.

• Prior authorization

  Reads the inbound fax or portal request, checks it against payer policy, submits, and tracks status to a decision. Escalates only when the policy is genuinely ambiguous.

  Fax gateway → payer API → EMR

• Referral intake and routing

  Turns an unstructured referral into a structured record, matches it to the right specialty and site, and books or queues it without a coordinator retyping anything.

  Intake → EMR → scheduling

• Eligibility and necessity checks before service

  Scores reimbursement likelihood against the Medicare and Medicaid rules that govern the service, and flags missing documentation while it can still be collected.

  EMR → policy layer → eligibility API

• Clinical documentation reconciliation

  Compares the note, the order, and the claim, finds where they disagree, and writes the corrections back with the source of each change attached.

  EMR → document store → claims

• Denial root-cause and appeal drafting

  Groups denials by actual cause instead of payer code, drafts the appeal with the supporting record cited, and reports the pattern worth fixing upstream.

  Claims → EMR → appeal packet

• Reasoning systems

  Payer policy and medical necessity checked before the service, with the rule behind every decision cited.

  Models work through policy, rules, and edge cases, and show how they reached the answer.

• Multimodal AI

  Faxes, PDFs, and scanned referrals become structured records without anyone retyping them.

  Documents, images, and forms become structured data without anyone keying them in.

• Agentic workflows

  Prior auth runs from intake to decision; staff work the exceptions instead of every request.

  Multi-step processes run end to end. People approve the exceptions instead of doing every step.

• Computer use

  Payer portals with no API stop being the step a person has to do by hand.

  AI operates software that has no API, so a legacy system stops being the reason you can’t automate.

• Voice agents

  Scheduling and intake calls answered and acted on while the patient is still on the line.

  The phone becomes a channel AI can answer, qualify, and act on while the caller is still on the line.

1. 01Advise

   Count the touches

   We sit with the people doing prior auth, intake, and documentation and count every touch. You get workflows ranked by volume, judgment required, and denial exposure, including the ones we tell you not to automate.

2. 02Engineer

   Inside the EMR, not beside it

   We write back through the interfaces you already own: HL7, FHIR, and the fax gateway. Payer policy and the Medicare and Medicaid rules that govern your services become the knowledge the system reasons over.

3. 03Operate

   We run it until your team owns it

   Monitoring on every run, accuracy tracked over time, and an engineer on call. Handover happens when your team is ready, not when the engagement ends.

Other industries

• Financial Services
• Field Service
• Ed Tech
• Private Equity