Now in Beta

Noise in.

Talon processes the chaos of payer requirements and clinical documentation into clean, structured intelligence. Gap analysis, readiness checks, formulary coverage, and appeal generation — all via REST API.

120+
Payer rules
<400ms
Avg response
99.9%
Uptime

// built for

EHR Vendors
Billing Platforms
RCM Companies
Telehealth Apps

// endpoints

Four APIs. One integration.

Each endpoint is a signal processor — raw healthcare data goes in, actionable intelligence comes out.

Payer Rules Engine

Know exactly what every payer requires before you submit. Documentation requirements, blocking rules, approval rates, and denial patterns in one call.

GET /v1/rules

Appeal Generator

Generate winning appeals from denial patterns. AI-powered letters backed by real outcomes data.

POST /v1/appeals/generate

Gap Analysis Engine

Send a clinical note, get back exactly what's missing. AI-powered extraction maps documentation to payer-specific requirements and highlights gaps.

POST /v1/analyze-gaps

Formulary Intelligence

Check drug coverage, prior auth requirements, step therapy status, and quantity limits across payers. Know if a medication is ready to prescribe.

GET /v1/formulary
HL7 Da Vinci FHIR Research

The documentation gap is real.

We analyzed the HL7 Da Vinci CDS-Library — the reference implementation for FHIR-based prior authorization. The data confirms what every PA coordinator already knows.

The Problem

The majority of prior authorization data-entry items require manual clinician entry — they can't be auto-populated from structured EHR data.

The Gap

Critical documentation like exam findings, treatment history, and functional assessments lives in unstructured clinical notes that FHIR auto-population can't reach.

The Fix

Talon's clinical NLP extracts the data that matters from physician notes and maps it to payer-specific requirements — closing the automation gap.

“It is likely that at least some answers will not be able to be gleaned from the EHR, due to missing data, data that is not computable, or data that is not represented in a standardized way.”
— HL7 Da Vinci DTR Implementation Guide, specification.html

Where Talon fits

FHIR auto-population handles demographics and lab values. But the data that actually determines approval or denial — conservative treatment history, clinical justifications, functional assessments — lives in unstructured clinical notes. Talon's clinical NLP reads those notes and maps the findings to payer-specific requirements, closing the gap that CQL was never designed to fill.

4 payers3 specialties120+ validated rulesReadiness scoringHIPAA BAA compliant

// quick start

Try it now

Hit the health endpoint — no API key required.

GET/api/v1/health
$ curl https://talonapi.dev/api/v1/health

{
  "status": "ok",
  "version": "1.0.0",
  "timestamp": "2026-02-28T12:00:00.000Z"
}
GET/api/v1/rules
$ curl -H "X-API-Key: tln_test_abc123..." \
    "https://talonapi.dev/api/v1/rules?payer=aetna&cpt=27447"

{
  "payer": "Aetna",
  "cpt_code": "27447",
  "description": "Total Knee Replacement",
  "approval_rate": 0.84,
  "required_docs": ["Clinical notes", "Conservative treatment history", ...],
  "blocking_requirements": ["6+ weeks documented conservative treatment", ...],
  ...
}