New AI Agent for Healthcare Claim Benchmarking and Cost Containment

Today we're announcing a new agentic AI capability for healthcare claim benchmarking and cost-containment workflows. The agent is designed to help payment integrity organizations, litigation support teams, stop-loss carriers, and other healthcare cost-containment teams investigate high-cost or unusual medical claims faster by benchmarking them against commercial rates published under federal healthcare price transparency requirements.

The capability takes available claim details, such as billing code, provider or hospital, payer, geography, and payment amount, then evaluates those facts against our Healthcare Price Intelligence data. Instead of forcing analysts to manually search through dozens of files or predetermine every possible query, the agent can identify relevant comparison sets, apply customer-defined rules, and return a structured benchmark for review.

Why claim benchmarking needs more than exact lookup

Price transparency rules have made a tremendous amount of commercial healthcare pricing data public. But using that data to investigate an individual claim is rarely as simple as looking up one code.

An exact provider, payer, service, and geography match may produce multiple prices, or none at all. Even when an exact rate exists, reviewers often need broader market context to determine whether that rate is typical, unusually high, or worth deeper investigation.

Our AI agent is built for that analytical step. When sufficient exact matches are not available, it can reason across comparable providers or facilities, approved commercial payer sets, local and expanded geographies, sample sufficiency, customer-defined fallback rules, and geographically adjusted Medicare reimbursement.

"Price transparency rules have made a tremendous amount of commercial healthcare pricing data public, but finding the right rate isn't always as simple as looking up a code," said Jason Hines, co-founder and CEO of Gigasheet. "When an unusual high-cost claim lands on an analyst's desk, the real question is often: what are the most defensible comparisons given what we know? AI gives us a way to make this data dramatically more accessible and useful for cost containment."

From rate lookup to intelligent claim research

For straightforward claims where the code, provider, payer, and geography are known and well represented in the data, teams can use direct Gigasheet API queries to retrieve matching commercial rates, distributions, and Medicare benchmarks at scale.

More complex claims can be routed to the AI-assisted endpoint for deeper analysis. The agent can evaluate available data, test comparison sets, apply thresholds and fallbacks, customize the analysis according to an organization's rules, and return a structured result that includes the selected comparison set, benchmark statistics, and supporting rate records.

Depending on the methodology, the agent can consider the same billing code or service, the same provider or facility, similar providers or peer facilities, local or regional markets, minimum sample sizes, commercial rate distributions, percentiles, Medicare reference points, exclusions, weighting, and fallback rules.

"Our agentic capability is a level beyond anything else on the market," said Garth Griffin, co-founder and CTO of Gigasheet. "Our product has always offered an intuitive interface for finding data, then we added AI-friendly integration points for users that had their own AI tools, and now we have gone even further to provide the AI agent itself, with baked-in expertise in healthcare price transparency."

Adding commercial market evidence to cost containment

Healthcare cost-containment teams already rely on contract terms, claims history, coding rules, clinical review, Medicare reimbursement, and proprietary benchmark datasets. Price transparency adds another source of evidence: the prices healthcare providers and commercial payers have negotiated with one another.

We process more than 15 trillion published negotiated rates and combine payer and hospital price transparency data with provider intelligence, Medicare benchmarks, normalization, enrichment, and proprietary analytical methods. That intelligence can help teams benchmark high-dollar claims, identify provider or facility pricing anomalies, compare allowed amounts with commercial market rates and Medicare, prioritize claims for deeper investigation, and support medical bill review, disputes, repricing research, stop-loss workflows, and litigation support.

Commercial rate benchmarks do not determine that a claim is fraudulent, incorrectly coded, medically unnecessary, or contractually overpaid. They provide an independent pricing signal that complements the contractual, clinical, coding, eligibility, and adjudication information already used by cost-containment teams.

Supporting reimbursement disputes and litigation research

Litigation support teams and reimbursement dispute specialists often need to explain whether a challenged claim is consistent with the broader commercial market. That can require more than a single benchmark or internal claims history. It may require a defensible comparison set, clear methodology, and supporting rate records that show how similar services are priced across relevant providers, payers, and geographies.

Gigasheet's AI-assisted workflow can help organize that research by identifying available commercial rate evidence, testing reasonable comparables, documenting fallback logic, and surfacing the underlying records used in the benchmark. The output can support expert review, case preparation, negotiation strategy, medical bill review, and reimbursement dispute analysis.

The benchmark is not a legal conclusion and does not determine whether a claim should or should not be paid. It gives litigation and dispute teams a structured commercial pricing signal they can evaluate alongside contracts, plan documents, clinical facts, coding review, and other case-specific evidence.

Built for existing workflows

The new capability is designed to integrate into existing claims and payment integrity systems rather than replace them. Organizations can use direct API calls for high-volume deterministic benchmarking and invoke agentic AI for the smaller subset of claims that require judgment or deeper research.

Results can feed existing risk scores, analyst queues, case-management systems, investigation workspaces, or customer-facing applications. We can also provide normalized price transparency data through bulk data delivery for organizations performing large-scale retrospective analysis or building their own models.

See the AI agent in action

We'll demonstrate the new capability during a live webinar on September 24, 2026 at 1:00 PM ET. Register here to see how AI-assisted claim benchmarking can help cost-containment teams move from manual research to structured, defensible market intelligence.

To learn more about the broader workflow, visit our claims cost-containment page.