Underwriting, pricing, and policyholder agents on the carrier side; claims, prior-auth, denials, and member service on the payer side. GenGuardX is the shared environment where operations, actuarial, risk, and compliance teams validate and monitor any AI agent, whoever builds it, with the evidence to approve before launch and the monitoring to trust it after.
It's rarely one bot. It's a roadmap of carrier-side and payer-side agents, from underwriting and pricing to claims and prior-auth, and every one clears the same gate. Operations, actuarial, and growth leaders need confidence the AI cuts cost and cycle time accurately. Risk, compliance, and legal need evidence it stays fair, explainable, and within regulation. Most pilots stall because neither side has a shared, defensible way to test, decide, and document.
Operations, underwriting, and growth leaders own throughput and customer experience, but are often sidelined during technical testing. GGX lets your adjusters, underwriters, and SMEs interact with the AI, validate it against real policies and claims, and build evidence-based confidence to sign off.
Before insurance or payer GenAI goes live, risk and compliance teams need more than a demo, they need evidence. GGX stress-tests models for unfair outcomes and unsafe behavior, tracks how gaps close, and builds an audit trail that holds up to regulators.
"In a regulated insurer or payer, confidence can't come from a demo, it has to come from evidence."
The agents on your roadmap span both sides of the business: carrier-side underwriting, pricing, and servicing, and payer-side claims, authorization, and member care. For each one, GGX defines the risks that matter, runs standardized evaluations, and produces the evidence to approve and monitor.
Auto-adjudicate clean claims against benefits and policy, and route edge cases to adjusters, reducing leakage and cycle time without mispaying.
Speed authorization decisions by checking requests against medical policy and benefits, approving clear cases and escalating the rest safely.
Draft clear, defensible denial rationales and appeal responses that cite policy, improving consistency and standing up to scrutiny.
Surface FWA signals across claims and providers with explainable evidence, prioritizing investigator time on the highest-value cases.
Match members to in-network providers by need, coverage, and access, improving steerage while respecting network and benefit rules.
Recommend the next best action for members and care teams, such as outreach, programs, or interventions, grounded in eligibility and policy.
Support pricing and quoting workflows with explainable, policy-consistent recommendations that stay within actuarial and regulatory bounds.
Assist underwriting and risk review with grounded summaries and recommendations, with traceable justification for every decision.
Answer policyholder and agent questions on coverage, billing, and endorsements, grounded in the policy and staying on-script for what is and isn't covered.
Read and structure submissions and first-notice-of-loss, extract the fields underwriters and adjusters need, and route by appetite and severity, with every value traceable to the source.
The same four steps clear the gate for any agent, on the carrier side or the payer side. Identify the risks that matter, measure them with standardized evaluations, mitigate the gaps, and monitor after launch, with evidence at every step.
Select use-case risks: policy and coverage adherence, fair outcomes and bias, hallucinated benefits or terms, PHI/PII leakage, decision explainability, prompt injection.
Repeatable tests against curated claims and policy datasets, expected outcomes, policy documents, and thresholds, not one-off scripts.
Apply guardrails, escalation logic, retrieval grounding to benefits and policy, or prompt changes, then prove the gap is actually closed.
Turn production traces into alerts, evidence, and new test cases as policies, models, and claim or book mixes change after go-live.
Each flag, rating, and annotation from a claims, underwriting, or clinical SME becomes reusable, structured ground truth, supporting objective measurement, faster iteration, monitoring, and future evaluation sets. SME time is scarce; GGX captures it once and reuses it across the whole AI lifecycle.
Pre-built, insurance- and payer-specific evaluation categories, plus your own custom risks, thresholds, and datasets.
Decisions stay faithful to policy, benefits, and the evidence, not invented.
Detect exposure of protected health and personal information and privacy violations.
Catch fabricated coverage, codes, and policy detail before they reach a decision.
Test for disparate denials, declines, and pricing across populations and groups.
Verify every adverse decision is justified, traceable, and defensible.
Stress-test against manipulation, data exfiltration, and out-of-scope use.
The same modules your operations, actuarial, risk, and compliance teams use to test, approve, and monitor any AI agent (carrier-side or payer-side), connected to your systems and your evidence.
Compose multi-agent systems: an orchestrator routing to specialized sub-agents, each with its own model, tools, and grounding. Then compare whole configurations to find the safest setup for each use case.
Claims SMEs, adjusters, and underwriters run real claims and policy scenarios against the AI, flag issues, and rate responses: human judgment combined with automated scale and a closed feedback loop.
Starter insurance and payer tests and customizable reports for policy adherence, fair outcomes, PHI/PII, and explainability: versioned, reusable, and ready for audit.
A single, defensible readiness measure, rolled up from objective sub-metrics and tracked version over version, so progress and approval are provable.
Role-based access, lineage, versioning, and approval workflows so operations, risk, and compliance teams work from one shared, auditable record.
Out-of-the-box LLM connectors plus API hooks into your claims and policy systems, grounded to the record, for pre- and post-production monitoring.
Two representative workflows taken to production with evidence rather than guesswork, illustrative of how GGX engagements run.
The challenge: a high-volume assistant that auto-approves clean claims and routes edge cases to adjusters. What GGX did: ran standardized policy-adherence and accuracy evaluations, captured adjuster feedback in one place, and produced the approval evidence compliance needed to launch.
The challenge: an authorization assistant that checks requests against medical policy, approves clear cases, and escalates the rest. What GGX did: drove fairness, explainability, and safe-escalation testing with iterative SME validation until every threshold held up to review.
Insurance and payer AI doesn't ship until your CISO, privacy, and legal teams sign off too. GenGuardX is built to pass that review: your data stays protected, your deployment stays in your control, and every action is logged and auditable.
We never train models on your data. Everything is encrypted in transit and at rest, with configurable retention and de-identification, and your PHI and PII never leave your control.
SOC 2 Type II and HIPAA-aligned, with a Business Associate Agreement available on request.
Secure SaaS, your own VPC, or fully on-prem, with data-residency options.
OpenAI, Google, Anthropic, or your own private and self-hosted LLMs.
SSO/SAML, role-based access, and immutable audit logs across every test, approval, and version.
Built on Google Cloud and AWS, with the isolation and reliability enterprise carriers and plans expect.