Enterprise AI Supervision

Enterprise-grade supervision, security, and auditing at Notch

LLMs are powerful, but they are also non-deterministic. That is the core tension every security, compliance, and IT leader feels when evaluating AI for customer-facing and operational workflows: how do you get the upside without inheriting unpredictable behavior, data risk, or audit gaps?

At Notch, we treat the LLM as only one component in a larger, controlled system. To mitigate risks inherent in non-deterministic models, we wrap LLMs in supervisory layers designed to reduce hallucinations, ensure security, prevent abuse, and make every decision traceable. The result is a platform you can deploy in regulated, high-stakes environments with confidence.

Notch powers critical conversations across finance, insurance, healthcare, and other industries where failure is not an option, and where uptime, data integrity, and auditability are non-negotiable. We also work with legal, compliance, and IT teams from day one to align on controls, evidence, and rollout processes across every layer of deployment.

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The core idea: a supervised AI agent

Many AI systems rely on “prompting harder” and hoping the model behaves. We do the opposite. We assume the model can be wrong, overly confident, or socially engineered, and we design the surrounding system accordingly.

In practice, every AI interaction in Notch is governed by three layers working together:

1. Guardrails (deterministic control)
   Deterministic rules govern when and how the AI can respond, what back office actions it may execute, and when it must escalate. This is how you prevent “creative” behavior from turning into business risk.
2. Supervision (real-time oversight and QA workflows)
   Guardrails keep the agent on-topic, while monitoring and QA workflows allow your team to review, correct, and continuously improve behavior over time. For sensitive use cases, we support human-in-the-loop patterns so high-impact actions require review or approval.
3. Audit trail (traceability by default)
   Notch provides granular visibility into each decision made by the AI and why. These events are logged for compliance, investigations, QA, and operational accountability.

You can read more on Notch 5 layers of guardrails here. This is what “enterprise-grade” means to us: the model is never operating alone.

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Guardrails that reflect real business constraints

A safe AI agent is not just “polite” or “accurate.” It must be bounded by the same rules your organization expects from a trained employee or a production system.

Notch guardrails are designed to be:

• Deterministic: Policies are enforced by rules and system controls, not by model “judgment” alone.
• Action-aware: It is not just about what the agent says, but what it can do.
• Escalation-ready: When confidence is low or the action is sensitive, the workflow escalates.

A particularly important dimension here is what we call business limitations: controls that restrict tools and actions based on your policies. For example, you can limit approvals, refunds, or other operational actions based on thresholds, daily caps, customer tier, risk signals, or required approvals. This allows you to adopt AI in real workflows without turning it into an ungoverned automation layer.

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Auditability: every decision is observable and explainable

In regulated industries, “trust me” is not a control. You need evidence.

Notch is built to make AI performance observable, traceable, and accountable:

• Granular audit trails that capture the key decisions the AI made and why.
• Traceable logs that support compliance, incident response, QA, and internal reviews.
• Audit-ready architecture where changes, access, and execution are recorded as part of normal operations.

This is especially important when an agent interacts with customer data, performs back office actions, or is used in player-facing or customer-facing portals. When questions arise, you should be able to answer them quickly and precisely: what happened, what the system knew at the time, what policy was applied, and who changed what.

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Model governance you can control, version, and rollback

AI behavior changes when models change. That is why we treat model configuration like production code: versioned, governed, and reversible.

Versioning and rollback

Model assignments are versioned per module, such as classifier, extractor, and summarizer, and can be pinned to specific model versions. This gives you deterministic control over what runs in production.

We also support safe rollout controls:

• Promote changes deliberately
• Instantly rollback to the last known good configuration

Environment-based control

Notch supports separate model configurations for Dev / Staging / Prod with controlled promotion between environments. This lets you test safely and avoid accidental drift.

We also support tenant-level policy overlays, for example:

• Enterprise-approved models only
• Region-locked inference
• Custom governance requirements per tenant

RBAC and auditability for model governance

Not everyone should be able to change what runs in production. Notch includes role-based access for who can view versus change model configurations, with roles such as admin, operator, and viewer, and auditability for changes.

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Security and privacy that match enterprise expectations

Trust, security, and privacy are at the core of our mission at Notch. Your organization’s data remains confidential, secure, and owned by you.

Certifications and compliance

Notch maintains a strong compliance posture for regulated environments, backed by recognized certifications and frameworks.
Our program includes ISO/IEC 27001 and ISO/IEC 42001, SOC 2, HIPAA, PCI DSS 4.0.1, and CSA STAR, and we support privacy and regulatory requirements including GDPR, CCPA, and the EU AI Act.

Data protection

We implement cryptographic controls when processing and storing data and perform encryption in accordance with industry standards:

• Encryption in transit: all web traffic over the public internet is encrypted using TLS v1.2
• Encryption at rest: data is encrypted using AES-256

We also support:

• PII redaction and policies designed to reduce unnecessary exposure of sensitive fields
• Flexible data retention options, including retention controls for qualifying organizations to help meet regulatory requirements

Network security

Notch production services are hosted on leading cloud infrastructure providers like Amazon AWS. We use:

• Amazon VPC to protect the network perimeter
• Web application firewalls
• Regular vulnerability scanning

Access control

Notch maintains audit logs of all activity, errors, and warnings on production systems, and we enforce application access control through:

• Single sign-on (SSO) and 2-factor authentication
• Least privilege access
• Role-Based Access Control (RBAC) for granular permissions

For enterprises using Okta or similar identity providers: Notch has native SSO support through any OAuth-compliant vendor, including Okta, with SCIM and automatic role assignment according to IdP attributes.

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Data governance for regulated environments

Enterprises often need controls that go beyond basic encryption. We support practical governance requirements that show up in real RFIs and security reviews:

Data residency and region control

For GDPR and legal compliance, we provide options for data and server residency as part of onboarding, including the ability to store customer data in specific regions such as EU, North America, or Asia.

Retention, deletion, and cold storage export

We support native configuration of retention and deletion rules. We also support exporting data into cold storage for regulated clients. While cold storage export may not be fully out-of-the-box for every configuration, it is easily configurable with our technical support.

Masking, obfuscation, and PII access boundaries

Notch includes RBAC as an integral part of the system, enabling granular control over which roles can see and/or edit specific parts of the platform.

In practice:

• PII-sensitive fields and entities are blocked at the backend level according to the current user’s role
• You can define roles that cannot view customer support tickets, analytics, or specific sensitive fields
• These rules are customizable to match your internal data governance policies per use case
• For PCI-sensitive workflows, Notch enforces role-based masking of cardholder fields at the backend and keeps audit-ready access logs, supporting PCI DSS-aligned access control and traceability.

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Operational security: designed for continuous assurance, not one-time checks

Security is not a document. It is an operating posture.

Notch runs a 24/7, 365-day on-call rotation for potential security incidents, with automated alerting and manual investigation processes to address suspicious activity. Our infrastructure undergoes regular audits, including red team and adversarial assessments by independent third parties.

Our program is guided by zero trust and defense-in-depth principles, and our SDLC is designed to build security in from inception. We implement layered controls across endpoints, infrastructure, networks, and applications, and we invest heavily in research and security for next-generation agent technologies.

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What this means for your organization

When you deploy Notch, you get more than “an LLM.” You get a supervised, governed system designed for enterprise operation:

• Encryption at rest and in transit across interactions
• Granular access control with RBAC, plus SSO/SCIM support
• Regular external penetration testing
• Audit-ready architecture with traceable logs
• PII redaction and flexible data retention policies
• Version control and full audit logs for every AI agent update
• Human-in-the-loop workflows for sensitive use cases
• Observable, traceable, accountable AI behavior

If you’re evaluating Notch as part of an RFI or security review, we are happy to engage your legal, compliance, and IT stakeholders early. That is how we ensure the controls you need are not bolted on later, but reflected in the way the system is configured, deployed, and governed from day one.

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