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AI Platforms AI assistant governance Runtime policy layer

Launch customer-facing AI assistants with control built in

Route AI interactions through a policy-aware gateway for logging, redaction, approvals, escalation, and blocking before risky outputs reach customers.

Runtime EnforcementCustomer-Facing AI GovernanceAudit-Ready Evidenceproduct fit Pre-responsepolicy enforcementMulti-modelassistant coverageShared workflowreview coordination
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AI Model Risk Manager product artwork
Product details

What it does

AI Model Risk Manager gives product, compliance, and security teams a controlled runtime layer for customer-facing AI assistants and approved internal AI tools. Route prompts, responses, tool calls, model metadata, and user identity through one policy-aware gateway for logging, redaction, approval gates, escalation, and blocking. Replace scattered reviews and after-the-fact monitoring with a workflow built for launch readiness, customer trust reviews, and ongoing oversight.

CategoryAI Platforms
Pricing modelUsage-based platform pricing with starter packages for monitored AI interactions and higher tiers for runtime enforcement, advanced workflows, and regulated deployments.
Best forSaaS product teams, compliance leaders, and security teams launching customer-facing AI assistants or managing approved internal AI tools that need runtime controls, evidence trails, and structured review workflows.
Feature set

Key product features

Runtime gateway for prompts, responses, tool calls, model metadata, and user identity

Policy checks that redact, flag, approve, or block risky AI interactions before delivery

Centralized audit logs for prompts, outputs, reviewer actions, and policy decisions

Sensitive-data controls for detecting exposed personal, financial, and confidential information

Approval queues and escalation routing for high-risk prompts, outputs, and tool actions

Workflow workspace with intake, assignments, statuses, reminders, internal notes, and handoff history

Evidence packages for customer security reviews, internal governance updates, and audit preparation

Coverage across multiple models, AI assistants, environments, and approved internal AI workflows

Dashboards for product, compliance, legal, and security stakeholders

Deployment support for customer-facing copilots, support bots, sales assistants, and internal AI tools

Narrow to the highest-value buyer segment

Add risk scoring and evidence automation

Use cases

Where it helps

Launch a customer-facing AI assistant with runtime prompt and output controls

Review sensitive responses before they reach end users in higher-risk workflows

Monitor approved internal AI tools for data handling and policy compliance

Document AI controls for enterprise customer security questionnaires

Create a repeatable approval path for new prompts, tools, and model changes

Track quote-to-contract and change-order assistant interactions with policy checks and evidence capture

Why teams buy now

A focused control layer for customer-facing AI assistants

When an AI assistant touches customers, contracts, support conversations, or sensitive business data, teams need more than passive monitoring. AI Model Risk Manager adds a runtime control point and a daily workflow for review and evidence.

Control responses before delivery Apply policy checks, redaction, approval gates, and blocking in the execution path instead of only reviewing incidents later.

Give reviewers a real operating workflow Manage intake, assignments, statuses, escalations, reminders, notes, and handoffs in one place.

Stay ready for customer and audit questions Keep prompt logs, output history, reviewer actions, and policy decisions organized for trust reviews and internal reporting.

How the platform works

Route every approved AI interaction through a controlled layer

The platform becomes the operational path for production AI assistants and approved internal tools so policy, evidence, and workflow are captured automatically.

Ingest every interaction Collect prompts, responses, tool calls, model details, user identity, and session context in one governed stream.

Apply runtime policy decisions Redact sensitive data, flag risky content, require approval, or block delivery based on your rules and risk thresholds.

Escalate to the right reviewer Send high-risk events to compliance, legal, product, or security with queues, ownership, and response history.

Generate evidence automatically Turn decisions, overrides, reviews, and exceptions into a defensible activity record for audits and customer reviews.

Vertical focus

Built for SaaS teams launching AI assistants buyers will inspect

This product is especially strong for software companies whose AI experiences will face security reviews, customer procurement questions, or internal governance checks before broader rollout.

Support and success assistants Monitor answers, citations, and sensitive account data before responses reach customers.

Sales and contract copilots Track quote, proposal, contract, and change-order assistant activity with approval checkpoints for higher-risk outputs.

Product copilots and in-app AI Govern embedded assistants that trigger tools, summarize data, or guide user actions inside your application.

Proof in practice

What changes when governance sits in the runtime path

Teams move from scattered screenshots and manual spot checks to a repeatable operational system for AI launches and ongoing oversight.

Before launch Map assistant flows, define policies, assign reviewers, and set approval thresholds for high-risk prompts and tool actions.

During production use Continuously log interactions, enforce controls, route exceptions, and keep every decision tied to the original event.

When questions come up Pull a clear evidence trail for leadership reviews, security questionnaires, incident follow-up, and audit preparation.

Pricing

Commercial packaging

Editable pricing cards exported directly in the product catalog JSON.

Starter

$199/mo

A fast path for one production assistant or pilot launch.

  • Up to 50,000 monitored interactions
  • 1 customer-facing AI assistant
  • Runtime logging and policy checks
  • Basic redaction and risk flags
  • Shared review queue
  • Exportable audit history
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Recommended

Team

$899/mo

For growing AI programs that need approvals and cross-team workflows.

  • Up to 250,000 monitored interactions
  • Up to 5 AI assistants or approved tools
  • Approval gates and escalation routing
  • Assignments, statuses, reminders, and notes
  • Sensitive-data controls
  • Evidence packages for security reviews
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Enterprise

Custom

For regulated, high-volume, or multi-business-unit AI operations.

  • Custom interaction volume and deployment scope
  • Advanced runtime enforcement and blocking rules
  • Dedicated environments and implementation guidance
  • Custom reporting and workflow design
  • Priority support and governance onboarding
  • Enterprise integrations and review processes
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FAQ

Buyer questions

Do we need to replace our existing AI models or apps?

No. AI Model Risk Manager is designed to sit in front of existing AI assistants and approved tools as a controlled runtime layer for logging, policy checks, approvals, and evidence capture.

Is this only for regulated industries?

No. It is useful anywhere a customer-facing AI assistant could create trust, security, or operational risk. SaaS teams often use it before enterprise customer reviews become a bottleneck.

Can you block or redact risky responses before users see them?

Yes. The platform can apply rules in the execution path to redact sensitive content, flag interactions for review, require approval, or block delivery based on configured policies.

Who works in the platform day to day?

Product owners, compliance teams, security reviewers, legal stakeholders, and operations managers typically share the workflow depending on the type of assistant and the review rules in place.

How does this help with customer security questionnaires and audits?

It creates a structured evidence trail with prompt and output history, reviewer actions, policy decisions, and exception handling records that are easier to present during trust reviews and internal governance checks.

Next step

See how governed AI delivery can work in your environment

Talk through your customer-facing AI assistant, review workflow, and policy requirements with our team.

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