AI for AI® · DSAIL® Verification Engine
Use AI agents for insurance decisions you can prove
AI compliance for insurers now turns on one question: can you show that every AI output was checked against your filed forms and the rules that govern it before it reached a policyholder, a producer, or a state DOI examiner? Jaxon is the policy-enforcement layer for AI-assisted insurance workflows — the deterministic verification engine that checks every underwriting recommendation, AI-generated claims decision, regulatory filing, and member communication against your codified policy language, your filed forms, and the regulations governing your lines of business, then returns a PASS, FAIL, or UNKNOWN verdict with an audit trail.
Built for the rules that govern AI in insurance
The rules your DOI examiner will ask about. Jaxon encodes every one.
By the numbers
Four facts that frame every conversation about AI in insurance.
The problem
AI is already inside underwriting, claims, and pricing. The regulator wants to know if you can answer for it.
Nothing to log into. Jaxon is an API embedded into the systems you already run.
The integration. Embedded into your underwriting workbenches, claims platforms, and policy administration systems.
The rules. Underwriting, Claims, Compliance, Counsel, and the Medical Director author the policy, version-controlled.
Replaces ad-hoc prompt engineering with a version-controlled compliance layer.
Prove everything
How DSAIL® verifies AI outputs instead of letting one model grade another
Most AI controls ask a second model to grade the first. That is still a probability, and a probability cannot be produced in a market conduct exam. Jaxon splits the work. The LLM does what LLMs are good at: it reads the document and extracts atomic facts (does this claim involve water damage? yes, no, or unknown). Deterministic code, your rules compiled by DSAIL® (Domain-Specific AI Language), takes those facts and computes the verdict. The policy logic never lives inside a prompt.
The probability path
One model grades another
The proof path
LLM extracts facts → deterministic rules → a verdict
A proof. Produced in the exam, the lawsuit, the attestation.
Defensible like human review, and fast like the LLM it verifies. Audit-grade output at AI speed, with no human-review bottleneck.
What Jaxon delivers to your compliance operation
Underwriting Officer · Claims Officer · Compliance · Medical Director · Counsel · Chief Actuary
Every determination applies the same encoded rules, with no variance between examiners.
Every output is verified against your codified policy language before it leaves your hands.
Jaxon clears the routine cases; your judgment goes to the ones that need it.
The verification record is the documentation regulators increasingly expect.
What Jaxon delivers to your engineering team
CTO · CIO · Head of Data and Analytics · Engineering Lead
Jaxon is an API, not a platform migration.
Works with GPT, Claude, Gemini, or proprietary models. Rules authored once, applied at runtime, updated when you re-file a form or a state issues new guidance, with no retraining.
Deployable wherever your regulated data must stay, up to HITRUST-aligned for PHI. Jaxon never sees your data at rest.
LLM behavior stays cleanly separated from policy enforcement, so your team owns neither problem.
Use cases · Insurers & Carriers
Six AI compliance use cases for insurers
AI-Assisted Underwriting & Pricing Decisions
Underwriting and pricing is the most heavily scrutinized AI use case in insurance. Carriers using AI systems and external consumer data and information sources (ECDIS) in underwriting and pricing operate under a converging regulatory stack: the NAIC Model Bulletin (adopted in 24+ states) and other state regulations specifically governing AI use in underwriting and pricing with its quantitative testing and annual attestation requirements. Jaxon encodes your underwriting guidelines, your filed rating plan, your tier-assignment logic, and the state-specific anti-discrimination and proxy-test obligations governing each line as formal DSAIL® rules. ECDIS variables flagged as potential proxies for protected classes are surfaced for governance review, not silently consumed.
- ▪Filed rating plan encoded as enforceable rules — every AI-recommended rate verified against the filed plan.
- ▪ECDIS variable governance — external data sources flagged when used outside documented underwriting purpose.
- ▪NY DFS proxy-test logic encoded — model inputs and outputs checked against Article 26 protected classes.
- ▪Colorado SB21-169 attestation evidence generated continuously — not reconstructed at filing time.
- ▪Tier-assignment determination — same risk profile, same tier, same state, appropriate rate, every time.
- ▪Underwriting decision chain of custody — rule fired, variables consumed, governance review, examiner sign-off.
Claims Adjudication Governance & Policy Enforcement
Carriers increasingly deploy AI to assist in claims review, coverage determinations, medical-necessity assessments, and reimbursement analysis — across health, P&C, and life. The risk is not simply incorrect output. It is policy misapplication: approving or denying a claim in ways that deviate from your filed policy language, your state-required claims settlement standards, CMS guidance (health), or your documented internal criteria. Jaxon embeds a deterministic policy-enforcement layer between your AI workflow and the claim decision, encoding your filed forms, coverage exclusions, and medical-necessity thresholds as formal DSAIL® logic. If an AI model recommends a denial that does not match the filed form, or approves a service outside plan parameters, Jaxon flags or blocks the output before adjudication is finalized.
- ▪Filed policy forms encoded as enforceable rules — every AI-generated coverage decision checked against the form on file.
- ▪Coverage exclusions and conditions encoded element-by-element with field-level traceability.
- ▪State-specific unfair-claims-settlement obligations (prompt-pay windows, written explanation requirements, appeal rights) verified per state of insured.
- ▪Medical-necessity criteria (health) encoded per line of business — commercial, Medicare Advantage, Medicaid managed care.
- ▪Pessimistic completion posture — ambiguous claim facts route to a human examiner, never auto-denied.
- ▪Per-decision chain of custody — rule fired, evidence extracted, examiner touch, timestamp — defensible in market conduct exam and litigation.
Coverage Determinations & Authorization Consistency
Coverage determination and authorization workflows — health prior authorization, P&C coverage triggers, life policy condition verification — are a focal point of regulatory attention. Errors in interpretation of clinical guidelines, misalignment with documented medical policies, or inconsistency with filed coverage triggers expose carriers to fines, corrective action plans, federal CMS scrutiny (Medicare Advantage), and reputational harm. Jaxon operates as a governance layer that enforces adherence to your documented clinical criteria, evidence-based treatment pathways, internal medical-policy documentation, and filed coverage terms when AI systems generate authorization recommendations. The emphasis is not speed — it is defensibility in a workflow under continuous scrutiny.
- ▪Documented medical policies and evidence-based pathways encoded per line of business.
- ▪Required escalation triggers (physician review, peer-to-peer, medical director sign-off) enforced as rules, not relied on as procedure.
- ▪Filed coverage triggers (P&C) and policy conditions (life) encoded with field-level traceability.
- ▪Pessimistic posture — when an authorization request lacks sufficient information, the rule fires UNKNOWN rather than COMPLIANT; the carrier decides whether to escalate, never auto-approve.
- ▪Same patient profile, same diagnosis, same plan, same answer — disparate-treatment risk eliminated by determinism.
- ▪Appeal-and-grievance audit trail per determination — rule fired, criteria cited, timestamped.
Member & Policyholder Communications Guardrails
Carriers deploy AI-powered chatbots, content-generation systems, and member-services automation to communicate with members and policyholders about benefits, coverage eligibility, network providers, cost-sharing, claim status, and policy changes. These communications must strictly adhere to filed plan documents, state insurance regulations, mandated disclosures, and — for health carriers — HIPAA protected health information rules. Jaxon operates as a conduct-compliance enforcement layer that encodes your benefit language, your copay structures, your deductible terms, your required disclosure language, and state-specific consumer-protection requirements into deterministic constraints governing AI-generated responses. If an AI-generated response misstates coverage eligibility, omits a required disclosure, or includes PHI outside the minimum-necessary standard, Jaxon intervenes before delivery.
- ▪Filed plan documents encoded as enforceable rules — every AI-generated member response checked against the filed document.
- ▪Required disclosure language enforced per channel, per state, per product line.
- ▪HIPAA minimum-necessary standard enforced as a rule (health) — PHI emission outside policy blocked before transmission.
- ▪Copay, deductible, OOP-max, and benefit-tier logic encoded — no AI hallucination of cost-sharing terms.
- ▪Pre-clearance chain of custody — every AI-generated communication carries a verification token.
- ▪Cross-channel consistency — chatbot, member portal, IVR, email — same encoded rules, same answers.
Regulatory Reporting & Risk Adjustment Documentation Integrity
Carriers produce complex regulatory filings — NAIC annual financial statements, ORSA reports, state Market Conduct Annual Statements (MCAS), risk adjustment submissions to CMS (health), rate filings, and form filings — and increasingly use AI to reconcile data, summarize metrics, and draft explanatory narratives. Jaxon functions as a cross-document verification layer, ensuring AI-generated reporting content aligns with underlying datasets, actuarial models, and regulatory thresholds. For health carriers under CMS Risk Adjustment Data Validation (RADV) scrutiny, coding-narrative consistency is verified deterministically. For all carriers, your regulator receives a filing whose narrative and numbers cannot diverge.
- ▪AI-generated regulatory narrative checked against submitted data — discrepancies flagged before filing.
- ▪RADV coding-narrative consistency verified deterministically (health).
- ▪ORSA, MCAS, and statutory-statement claims cross-referenced against underlying actuarial and operational data.
- ▪Cross-filing consistency — same metric, same value, every form, every state.
- ▪Rate filing and form filing narratives checked against the rate plan and policy forms they reference.
- ▪Filing-ready audit trail — every claim in every narrative tied back to a source record and a verified rule.
Enterprise AI Governance Across Business Lines
Carriers operate across multiple lines — commercial, individual, Medicare Advantage, Medicaid managed care, group life, P&C personal, P&C commercial — and each is subject to distinct regulatory regimes and policy requirements. As AI tools embed across underwriting, care management, claims, actuarial modeling, customer service, and fraud, carriers need centralized oversight to ensure AI usage aligns with documented policies and the NAIC Model Bulletin’s expectations on governance, risk management, and third-party AI vendor oversight. Jaxon functions as the enterprise AI governance layer — encoding business-line-specific constraints, escalation protocols, vendor-AI usage boundaries, and regulatory obligations into enforceable policy controls.
- ▪Business-line-specific AI usage policies encoded as enforceable rules — same model, different policy controls per line.
- ▪Third-party AI vendor outputs verified against your governance framework before consumption.
- ▪NAIC Model Bulletin alignment — written AI Program evidence generated continuously, not assembled at exam time.
- ▪Cross-line consistency oversight — AI decisions across commercial, individual, Medicare Advantage, and managed Medicaid checked against the same encoded fairness and accuracy standards.
- ▪Board and audit committee reporting — verified record of AI usage, exceptions, and remediations.
- ▪Whole-enterprise policy versioning — when state guidance changes, rules are updated centrally, not chased model-by-model.
Integration and API flow
How Jaxon verifies an AI output: POST, check, verdict, log
Any LLM: an underwriting decision, a claim determination, a fraud flag.
Formal check against your encoded, version-controlled rulebook.
Deployment · your environment, your security model
SaaS / Managed Cloud
Fastest time to production. Isolated, encrypted execution. Suitable for non-PHI, non-PII workflows.
Private Cloud (VPC)
Deployed inside your own AWS, Azure, or GCP VPC. Your data never leaves your cloud. Recommended for regulated insurance data.
On-Premises
Deployed on your own infrastructure. No data leaves your perimeter. No external API dependencies.
HITRUST-Aligned
Architecture mapped to HITRUST CSF control families for PHI handling. Required for health-carrier deployments touching PHI under HIPAA.
Get in contact
Ready to embed verified AI into your insurance compliance workflows?
Jaxon's engineering team works directly with your underwriting, claims, compliance, and CAIO functions to encode your rules and stand up verification inside your environment.
Your rules. Your environment. A verdict you can produce.
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Start the conversation
Or write directly: info@jaxon.ai
FAQ
Questions insurance and compliance teams ask first.
No. Jaxon verifies AI outputs against the rules you encode and returns PASS, FAIL, or UNKNOWN with an audit trail. Compliance remains your determination. Jaxon gives you the defensible record behind it.
The bulletin requires a written AI Systems (AIS) Program covering governance, risk management, and validation, and it reminds insurers that AI decisions must comply with existing unfair-discrimination law. It does not prescribe a single testing method, but it expects you to document how you test for and mitigate proxy discrimination and to produce that documentation on request. Jaxon encodes those tests as rules and generates the evidence continuously.
Validation has to be independent, documented, and done before deployment, and the obligation is not transferred by using a vendor model. Jaxon checks each AI-recommended rate against the filed rating plan, flags ECDIS variables used outside their documented purpose, and produces a per-decision chain of custody you can hand to an examiner.
The regulatory direction is toward human accountability for adverse decisions, with emphasis on disclosure, auditability, and a qualified human standing behind any AI-driven denial. Jaxon supports that posture: ambiguous facts return UNKNOWN rather than a forced pass or fail, and a recommended denial that does not match the filed form returns FAIL before it is finalized.
An AI Systems (AIS) Program is the written program the NAIC Model Bulletin expects every insurer to maintain for the responsible use of AI in decisions affecting regulated insurance products. It covers governance, risk management, validation, and vendor oversight. Jaxon is the technical substrate that makes the program documentable: every verification is logged, versioned, and reproducible.
SaaS managed cloud for non-PHI workflows, your own VPC, fully on-premises, or HITRUST-aligned for PHI under HIPAA. The LLM never sees your policy logic, and Jaxon never holds your data at rest.