Scroll through every stage of an autonomous run — triage, parallel research, synthesis, adversarial red-team review, and submission-packet prep — then the post-run learning loop that fires when the payer responds.
Llama 3.3 70B extracts CPT and ICD-10 codes and drafts a medical-necessity statement. Before any agent runs, the triage checks persisted payer personas for this payer + CPT and surfaces predictive warnings learned from prior denials.
TinyFish browser agents run one-per-payer through a Swift TaskGroup, hitting api.search.tinyfish.ai with domain-aware ranking and api.fetch.tinyfish.ai in markdown mode to parse coverage criteria, exclusions, and required documentation.
The LLM composes the Evidence Binder and appeal letter against a strict JSON schema, with a repair fallback that round-trips malformed output back to the model before giving up.
An adversarial "medical director" model critiques the draft. If it fails, both documents are auto-revised. When evidence is genuinely missing, a Clinical Addendum Request is generated to the physician rather than hallucinating the gap.
A payer-specific packet is assembled — portal URL, attachments list, pre-upload checklist, case summary — ready for a human submitter. The app deliberately does not auto-submit; a clinician stays in the loop for every outbound transmission.
When the payer responds, paste the EOB into the run and the LLM extracts {denialReason, missingEvidence, recommendedAction, learnedRule}. The learned rule upserts a PayerPersonaRule that fires as a predictive warning on the next triage for the same payer + CPT. The product literally sharpens with use.
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