Ember Copilot
AI revenue integrity platform for specialty physician practices, surgery centers, and health systems, working both sides of the denial problem. To prevent denials it reviews every encounter against coding standards, payer policy, and the practice's own contracts, checking CPT, ICD-10, HCPCS, modifiers, NCCI edits, and documentation completeness, returning suggested corrections that carry the underlying rule and its source from CMS, NCCI, or payer policy rather than an unexplained flag. To recover them it identifies root cause, retrieves records, references payer policy and contract terms, drafts the appeal packet with clinical evidence, and tracks it through adjudication. Also provides ambient scribing across dozens of specialties, benchmarks payer rates to surface underpayments, and tracks payer policy changes. Runs on US based cloud infrastructure stated as HIPAA and SOC 2 compliant. Reports 55 to 57 percent fewer denials and 98 percent coding accuracy for customers. Co founded by a former healthcare AI product manager at Google and a CTO with explainable AI research background at MIT CSAIL; $4.3 million seed in November 2025 led by Nexus Venture Partners with Y Combinator.
Capability Axes
Models perform the review, the root cause analysis, and the appeal drafting. Coding checks against CPT, ICD-10, HCPCS, modifiers, and NCCI edits, plus payer policy and contract terms, are model outputs, and ambient scribing runs on the same platform.
The design answer to the problem this index has flagged repeatedly in revenue cycle AI. Where Waystar autonomously generates appeal letters with no published review step, Ember returns suggested corrections that carry the underlying rule and cite its source, from CMS, NCCI, or payer policy. A coder can verify why a change was proposed rather than accepting an unexplained flag, which makes the output auditable and the reviewer accountable. Citing the rule is the correct pattern for coding assistance.
Specific outcome claims are published, 55 to 57 percent fewer denials and 98 percent coding accuracy, but they are vendor reported without methodology, baseline, or customer references, and the company is early with a $4.3 million seed in November 2025. Coding accuracy in particular is meaningless without stating the audit standard it was measured against.
Sits on top of existing systems, pulling clinical notes, codes, and charge files from Epic, Oracle Health, athenahealth, and ModMed. Read breadth across four major systems is solid for a seed stage company; write back depth into discrete fields was not documented.
No rate card, but the evaluation path is unusually concrete and low friction: providers can submit a few hundred cases for initial analysis with results in about three days, and a free ICD-10 code search tool is offered publicly. A prospect can test the product against their own data before committing, which is a more useful disclosure than a price for a product whose value depends entirely on the buyer's case mix.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
| Entry Price | Pricing Basis | BAA Tier | Implementation | Source |
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Demo led enterprise sales; case sample analysis available before commitment | — | — | Vendor Published |
No public rate card, but the evaluation path is concrete: providers can submit a few hundred cases for initial analysis with results in roughly three days, and onboarding is stated in days. A free ICD-10 code search tool is offered publicly. For a product whose value depends on the buyer's specific case mix and payer contracts, a paid pilot against real data is more informative than a list price.