Member service, fraud and disputes, collections, loan decision support, and next best offer, across member-facing and risk and lending agents. GenGuardX is the shared environment where member-experience teams and risk, compliance, and NCUA-exam 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 member-facing and risk and lending agents, from service and collections to fraud and loan decisions, and every one clears the same gate. Member-experience and growth leaders need confidence the AI serves members better, accurately and on-brand. Risk, compliance, and examiners 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.
Member-service, lending, and marketing leaders own the member relationship, but are often sidelined during technical testing. GGX lets your member reps and SMEs interact with the AI, validate it against real scenarios and policy, and build evidence-based confidence to sign off.
Before member-facing 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 NCUA examiners.
"For a member-owned institution, confidence can't come from a demo, it has to come from evidence."
The agents on your roadmap span the member journey, from service to lending to recovery, across member-facing and risk and lending work. For each one, GGX defines the risks that matter, runs standardized evaluations, and produces the evidence to approve and monitor.
Answer member questions across chat, web, and the mobile app (balances, transfers, products, and policies) accurately and on-policy, with clean handoff to reps.
Automate voice and IVR interactions for routing, account servicing, and FAQs, deflecting call volume while keeping answers accurate and on-brand.
Surface fraud signals and handle disputes and chargebacks with explainable evidence, protecting members without over-blocking legitimate activity.
Automate compassionate, compliant collections conversations that follow policy and disclosure rules, escalating sensitive situations to member reps.
Assist lending review with grounded summaries and recommendations, with traceable, fair-lending-aware justification for every decision.
Recommend relevant, fair products grounded in eligibility and suitability, deepening member relationships without unfair targeting or invented terms.
The same four steps clear the gate for any agent, member-facing or risk and lending. 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 adherence, fair lending and bias, hallucinated terms and rates, PII leakage, adverse-action explainability, prompt injection.
Repeatable tests against curated member-service datasets, expected outcomes, policy documents, and thresholds, not one-off scripts or vibes.
Apply guardrails, escalation logic, retrieval grounding to 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 member behavior change after go-live.
Each flag, rating, and annotation from a member-service, lending, or compliance 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, credit-union-specific evaluation categories, plus your own custom risks, thresholds, and datasets.
Answers stay faithful to policy, product terms, and the evidence, not invented.
Detect exposure of personal and account information and privacy violations.
Catch fabricated rates, fees, and product detail before they reach a member.
Test for disparate outcomes and performance across member populations.
Verify every adverse decision is justified, traceable, and defensible.
Stress-test against manipulation, data exfiltration, and out-of-scope use.
The same modules your member-experience, risk, and compliance teams use to test, approve, and monitor any AI agent, connected to your core 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 member use case.
Member-service, lending, and compliance SMEs run real member scenarios against the AI, flag issues, and rate responses: human judgment combined with automated scale and a closed feedback loop.
Starter credit union tests and customizable reports for policy adherence, fair lending, 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 member-experience, risk, and compliance teams work from one shared, auditable record.
Out-of-the-box LLM connectors plus API hooks into core banking and contact-center platforms for pre- and post-production monitoring.
Member-facing AI, taken to production with evidence rather than guesswork. Details anonymized at the customers' request.
The challenge: a member-facing assistant for servicing questions and account help, with accuracy, tone, and policy stakes. What GGX did: ran standardized accuracy, tone, and policy evaluations, captured member-rep feedback, and produced the examiner-ready evidence risk and compliance needed to launch.
The challenge: fraud, disputes, and collections initiatives, each stuck without a shared, defensible way to test and prove readiness. What GGX did: gave member-experience teams a way to test and validate AI and gave risk and compliance a consistent, audit-ready evidence trail, replacing ad-hoc testing with a repeatable workflow.
Member-facing AI doesn't ship until your CISO, risk, and compliance teams sign off too, and it has to hold up in an NCUA exam. 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 your member and account data never leaves your control.
SOC 2 Type II, with examiner-ready documentation and evidence packs built for NCUA review.
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 credit unions expect.