Funding
Charta Health Raises $55M Series B to Expand AI Chart Review
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Charta Health announced on November 10, 2026, that it had raised a $55 million Series B to expand its AI chart-review technology. Insight Partners led the financing, which also drew QuantumLight and returning investors Madrona, Bain Capital Ventures and South Park Commons.
Charta will put the capital toward further AI development for provider customers, entry into additional specialties and its goal of establishing one trusted interpretation of a chart before billing. Alongside the financing, it broadened Charta for Payers with a suite initially aimed at Medicare Advantage and Affordable Care Act Marketplace plans.
Pre-Billing Review for Providers
Charta says its provider software reviews every chart before billing against coding guidelines, payer rules and each customer’s clinical protocols. Findings appear for human approval inside the electronic health record, leaving staff to validate the issues flagged by the system.
The software applies large language models to unstructured material such as free-text notes, scanned orders, laboratory results and images. Its task is to establish what care was delivered and whether the documentation supports the billing, moving accuracy checks ahead of the denials and appeals that can follow a claim.
Compliance and medical leaders can also examine documentation deficiencies, error patterns and provider performance across patient encounters. American Family Care, Grow Therapy and Pediatric Associates Family of Companies are among the healthcare enterprises using the technology. Charta reports consistent double-digit reductions in denial rates for fee-for-service billing.
Payer Suite Checks Coding in Both Directions
The payer products look for documented conditions that were not coded and for coded diagnoses that lack support in the chart. Their six product areas span retrospective diagnosis coding, automated second-pass coding, a smart coding workspace, prospective risk-adjustment intelligence, diagnosis validation and audit workflow, and quality care-gap detection. Charta says it designed the suite with one of the nation’s largest health insurers, without naming the partner.
The payer platform extracts structured clinical and encounter intelligence from records that plans already retrieve, allowing the same evidence to support retrospective coding, second-pass quality assurance, audit work and prospective workflows. Charta says its automated second-pass product applies AI to 100% of coded charts, while the smart coding workspace presents supporting evidence, citations, and additions and deletions for validation in one review.
For retrospective risk adjustment, AI presents clinical evidence to human coders, who use customer-specific guidelines to validate additional diagnoses and flag unsupported codes for deletion. Smart Scan pre-screens the chart population to identify records with enough potential value to warrant full review. Charta advertises 98% accuracy for diagnosis coding in this workflow, based on the combination of AI evidence recall and human validation.
Investor Thesis and Funding History
Insight Partners co-founder and managing director Jeff Horing pointed to Charta’s pairing of AI and customer-specific coding expertise across providers and payers. “As payers and providers both adopt AI, we believe the opportunity lies in getting the underlying record, or the ‘ground truth,’ right before a claim is submitted,” he said.
The new round follows Charta’s $22 million Series A, announced on July 22, 2025 and led by Bain Capital Ventures, with Madrona, SV Angel, Refract Ventures and South Park Commons participating. Charta said its total funding exceeded $30 million at that time. Where the earlier announcement focused on automating provider chart review, this one pairs continued provider development with a broader payer product rollout.
The expansion makes a shared chart record the central test of Charta’s model. The same evidence can help a provider submit a better-supported claim and help a payer identify an unsupported diagnosis; the commercial challenge is making that review dependable enough for both sides while humans remain responsible for the coding decisions.
