# GenGuardX > GenGuardX (GGX), from Corridor Platforms, is an AI governance platform for regulated enterprises. It helps business teams, risk teams, and AI teams test, approve, monitor, and document customer-facing GenAI systems before and after production launch. Canonical site: [https://genguardx.ai/](https://genguardx.ai/) Sitemap: [https://genguardx.ai/sitemap-index.xml](https://genguardx.ai/sitemap-index.xml) Contact: [admin@genguardx.ai](mailto:admin@genguardx.ai) ## Crawl Guidance The public marketing, industry, cloud deployment, blog, policy, privacy, accessibility, demo, and feedback pages are intended to be crawled and summarized. Prefer canonical clean URLs over generated HTML paths. Do not use POST endpoints or form actions as content sources. ## Core Facts - Product name: GenGuardX, also referred to as GGX or Corridor GGX. - Company: Corridor Platforms, Inc. - Category: Responsible AI, GenAI governance, AI risk management, evaluation, approval workflow, and production monitoring. - Primary audience: regulated enterprises deploying customer-facing AI, including financial services, healthcare, insurance, credit unions, AI-native teams, and enterprise AI teams. - Core promise: move GenAI from pilot to production with business confidence, risk approval evidence, and ongoing monitoring. - Trust signals shown on the site: SOC 2 Type 2 certified, deployed at a Tier 1 global systemically important bank, deployed at a leading US health system, used by major credit unions. - Key workflows: interactive AI testing, business-team feedback, ground truth dataset creation, risk library mapping, standardized evaluations, findings tracking, approval workflows, trace ingestion, LLM judges, production monitoring, alerts, audit trails, versioning, and retesting. ## Primary Pages - [Home](https://genguardx.ai/) - Main positioning: "From AI pilot to production, without a leap of faith." - Explains the AI trust lifecycle: design and develop, business confidence, risk approval, deploy, monitoring, and re-evaluate. - Frames the two blockers before AI goes live: business teams must verify whether the AI does what it is supposed to do, and risk teams must verify whether the AI blocks policy violations. - Describes business validation, risk approval, production monitoring, high-stakes industries, the Responsible AI Sandbox, and ways to engage. - [Book a demo](https://genguardx.ai/book-a-demo/) - Request a tailored GenGuardX walkthrough. - Demo topics include business confidence, risk approvals, AI agent monitoring, approval evidence, and post-launch drift management. - [About](https://genguardx.ai/about-us/) - GenGuardX was created in 2025. - Leadership includes Manish Gupta, Aditya Khandekar, Abdeali Kothari, Aniket Agrawal, and Xin Yang. - Board members include Ash Gupta, Rohit Kapoor, and Til Schuermann. - Partners and investors include EXL Service and Oliver Wyman. - [Blog](https://genguardx.ai/blog/) - Responsible AI and GenAI governance articles from the GenGuardX team. ## Industry Pages Each industry page frames GenGuardX as the shared environment where business, risk, and compliance teams identify the risks that matter, run standardized evaluations, mitigate gaps, and monitor after launch, producing audit-ready evidence to approve and trust every customer-facing AI agent, whoever builds it. - [Healthcare Providers](https://genguardx.ai/healthcare-providers/) - Positioning: every AI agent in the health system, approved with evidence, not demos, across patient-facing, clinician-facing, and operations agents. - Use cases: patient routing and symptom protocols, EHR integration and summarization, call-center and front-desk operations, lab result analysis, ambient and automated documentation, and ICD-10 and medical coding. - Emphasizes HIPAA-aware evaluations, groundedness to the EHR, PHI protection, safe escalation, bias testing, and audit-ready approval evidence, proven with a leading US health system. - [Insurance and Payers](https://genguardx.ai/insurance-payers/) - Two-sided coverage: insurance carriers (underwriting, pricing, policyholder and agent servicing) and health payers (claims adjudication, prior-authorization, denial management, member service, and fraud, waste, and abuse detection). - A shared environment where operations, actuarial, risk, and compliance teams validate and monitor any AI agent with policy-grounded, fair-outcome, and audit-ready evidence. - [Banks](https://genguardx.ai/banks/) - Positioning: put every banking AI agent in production and keep it sound, across risk and decisioning agents and customer-facing agents. - Use cases include fraud alerts, customer service, collections, and underwriting support, backed by model-risk, compliance, and fair-lending evidence, including fair-lending and bias testing and model-risk-ready documentation. - [Credit Unions](https://genguardx.ai/credit-unions/) - Positioning: put every member-facing and risk and lending AI agent in production with NCUA exam-ready evidence. - Use cases include member service, fraud alerts, collections, and lending, tested for accuracy, groundedness, fair outcomes, and safe escalation. ## Cloud Deployment Pages - [AWS](https://genguardx.ai/cloud-aws/) - GenGuardX on AWS pairs GGX governance with AWS services including Amazon S3, Redshift, Athena, SageMaker, Bedrock, RDS, EC2, EKS, KMS, IAM, Identity Center, and CloudTrail. - The page links to AWS Marketplace discovery for Corridor Platforms. - [Microsoft Azure](https://genguardx.ai/cloud-azure/) - GenGuardX on Microsoft Azure combines AI governance with Azure Blob Storage, Synapse Analytics, Azure Machine Learning, Azure OpenAI, Azure SQL Database, Azure Virtual Machines, AKS, Entra ID, Azure Monitor, and Microsoft Defender for Cloud. - The page links to the Corridor GGX Azure Marketplace listing. - [Google Cloud](https://genguardx.ai/cloud-gcp/) - GenGuardX on Google Cloud supports governed AI workflows with Cloud Storage, BigQuery, Vertex AI, Gemini, Model Garden, Cloud SQL, Compute Engine, encryption, and IAM. - The page references the Responsible AI Sandbox with Google Cloud and Oliver Wyman. ## Blog Articles - [What the Swiss Cheese Model teaches us about governing GenAI systems](https://genguardx.ai/blog-swiss-cheese-model/) - Applies James Reason's Swiss Cheese Model to GenAI pipelines: every layer (data and retrieval, prompts, foundation model, orchestration and tools, guardrails, and outputs) has holes, and a breach happens when a business-team gap and a risk-team gap line up across the same pipeline. - Introduces the GenGuardX identify, measure, mitigate, and monitor workflow, run at every layer before and after launch, and illustrates it with a fraud case summarizer example. - Author: Sakshi Mishra. Published July 2026. - [Hallucinations in AI](https://genguardx.ai/blog-hallucinations/) - Explains factuality hallucination, faithfulness hallucination, intrinsic and extrinsic hallucinations, and detection techniques including SelfCheckGPT, semantic entropy, hidden-state methods, and MetaQA. - Author: Tanishq Singh. Published October 2025. - [Beyond Vibe Checks](https://genguardx.ai/blog-vibe-checks/) - Explains why AI agents require trajectory-level evaluation beyond ad hoc testing. - Covers evaluation paradigms for tool use, reasoning, memory, and safety. - Author: Tanishq Singh. Published November 2025. - [Creating Toxicity Detection Using LLM-as-a-Judge](https://genguardx.ai/blog-llm-as-a-judge/) - Explains how LLM-as-a-judge can evaluate toxicity and related safety dimensions. - Covers prompt design, severity scoring, calibration, reliability, and JSON output constraints for automated moderation workflows. - Author: Tanishq Singh. Published January 2026. ## Policy And Legal Pages - [AI policy](https://genguardx.ai/ai-policy/) - Describes AI usage commitments, no-training expectations, data isolation, responsible AI safeguards, and how GenGuardX treats customer data and models. - [Privacy policy](https://genguardx.ai/privacy-policy/) - Describes personal data collection, use, lawful basis, security, third-party links, sharing, retention, user requests, and contact information. - [Accessibility statement](https://genguardx.ai/accessibility-statement/) - States Corridor Platforms' accessibility commitment, WCAG 2.1 Level AA conformance status, compatibility notes, feedback channels, and formal complaint contact. ## Feedback Page - [Feedback survey](https://genguardx.ai/feedback/) - Collects an NPS recommendation score, the primary reason for the score, and an optional respondent name. - The name field is optional and not required. ## Recommended Short Description GenGuardX is Corridor Platforms' Responsible AI governance platform for regulated enterprises. It gives business, risk, and AI teams a shared environment to test, approve, monitor, and document customer-facing GenAI systems, turning SME feedback, evaluation results, and production traces into reusable ground truth and audit-ready evidence.