Solutions for Governed AI

Scale AI adoption without losing enterprise control.

Iterate helps enterprises govern AI usage across teams, agents, applications, and developer tools with observability, policy enforcement, sensitive data protection, provider routing, auditability, and cost controls.
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Centralized LLM gateway
Policy enforcement
DLP & sensitive data protection
Audit trails & observability
Cost controls & routing
The problem

AI adoption creates operating risk when governance comes later.

Teams can adopt models, assistants, agents, and coding tools faster than central teams can govern them. Enterprises need a control layer that supports adoption while reducing unmanaged risk.

Shadow AI

Employees and teams may use AI tools without security, legal, compliance, procurement, or IT visibility.

Inconsistent policies

Access, data handling, provider choices, model usage, and approval rules can vary across teams and tools.

Sensitive data risk

Prompts, files, documents, and code can include confidential, regulated, proprietary, or customer information.

Poor auditability

Leaders need logs, request history, model usage, policy decisions, and usage context to understand what happened.

Spend uncertainty

Token usage, provider selection, budgets, routing, and chargebacks become difficult to manage at scale.
THe Solution

Govern AI through one observable control layer.

Iterate uses AgentWatch with Generate, Interplay, and AgentOne to centralize visibility, policy, routing, auditability, and cost control across business applications, agents, assistants, and developer AI.

Centralize AI visibility

Give leaders a clearer view of how AI is being used across assistants, agents, applications, development tools, teams, and providers.

Apply consistent policies

Enforce rules for data handling, model access, routing, sensitive information, approved providers, and usage boundaries.

Protect sensitive information

Use DLP and policy controls to help prevent confidential, regulated, proprietary, or customer data from being exposed.

Route AI traffic intelligently

Manage provider selection, failover, model usage, latency, performance, cost, and private or hybrid model options.

Create audit-ready records

Capture request history, policy decisions, usage context, model activity, token counts, and operational telemetry.

How Iterate helps enterprises govern AI at scale

01

Map AI usage and risk

Identify where teams are using or planning to use AI across assistants, agents, applications, coding tools, providers, and workflows.

02

Define policies and controls

Establish rules for data handling, access, model routing, provider usage, DLP, approvals, auditability, and cost management.

03

Route traffic through a control layer

Use AgentWatch as the governance layer for visibility, policy enforcement, routing, observability, audit trails, and spend controls.

04

Connect governed AI workflows

Bring Generate, Interplay, and AgentOne usage into the broader governance model across assistants, agents, workflows, and development environments.

05

Monitor, report, and optimize

Track usage, cost, policy events, model performance, routing decisions, and adoption patterns as AI scales.

Where governed AI rollouts create value

Security & compliance

Protect sensitive data, monitor AI usage, enforce policies, and create audit-ready evidence.

IT & platform teams

Centralize AI access, provider routing, API key management, observability, and controls across teams.

AI program leaders

Scale AI adoption with consistent rollout patterns, governance standards, and visibility into usage and outcomes.

Finance & procurement

Track token usage, provider spend, budgets, chargebacks, routing decisions, and cost optimization opportunities.

Engineering teams

Bring coding assistants and developer AI tools into the broader governance model.

Business teams

Adopt assistants, agents, and AI workflows with clear controls, approved providers, and safer data practices.
capabilities

Governed AI rollout capabilities

AI gateway and routing
  • LLM gateway and centralized control
  • Provider routing and failover
  • Governance for public, private, custom, and hybrid model usage
  • Role-based access and API key management
Policy & protection
  • Prompt screening and policy enforcement
  • DLP for sensitive data and secrets
  • Usage policies and guardrails
  • Sensitive data protection across prompts, files, and workflows
Observability & cost control
  • Audit trails and request history
  • Token tracking and budget controls
  • Cost reporting and chargeback support
  • Observability across agents, apps, and coding tools
platform components

The Iterate platform for private knowledge assistants

AgentWatch

AI governance
Anchor product for AI governance, observability, LLM gateway controls, policy enforcement, DLP, routing, auditability, and spend management.

Generate

Private assistants
Provides private assistants and task agents that can operate inside governed enterprise controls.

Interplay

Workflow orchestration
Builds and runs governed workflows and agents that can be observed and controlled.

AgentOne

AI development
Extends governance needs to secure AI-assisted development and coding workflows.
Business value

Outcomes teams can measure

Iterate helps your team measure shadow AI reduction, usage visibility, policy enforcement, sensitive data protection, audit readiness, cost control, and governance maturity across teams, tools, and providers.

  • Reduce shadow AI and unmanaged model usage.
  • Improve visibility across AI applications, agents, and tools.
  • Protect sensitive data and proprietary information.
  • Support auditability and compliance evidence.
  • Control AI costs across teams and providers.
  • Scale AI adoption without losing enterprise control.
Governed AI Rollout Assessment

Build the control plan before AI usage becomes impossible to manage.

Iterate helps your team map current and planned AI usage, identify governance gaps, and define the LLM gateway, policy, observability, and rollout model needed to scale safely.

  • AI usage and stakeholder map
  • Governance, DLP, audit, and cost-control gap analysis
  • LLM gateway architecture recommendation
  • Policy and routing framework
  • 30/60/90-day rollout plan
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Frequently asked questions

Does governance slow down AI adoption?
No. The goal is to make AI adoption safer and easier to scale by creating consistent controls instead of forcing every team to solve governance, security, routing, and auditability on its own.
Can this govern multiple AI providers?
Yes. The solution should support routing, visibility, policy enforcement, and governance across public, private, custom, and hybrid model endpoints.
Does this include developer AI tools?
Yes. AgentOne and AgentWatch together make developer AI usage part of the broader governance story, including visibility, policy controls, proprietary code protection, and auditability.
Can this help reduce shadow AI?
Yes. A governed rollout helps central teams identify AI usage, provide approved paths, apply controls, and reduce unmanaged tools, API keys, and model access.
How does this support auditability?
AgentWatch helps capture request history, policy decisions, model usage, routing activity, token counts, and other context needed for oversight and reporting.
Can governance also help manage AI cost?
Yes. Governed AI rollouts can include token tracking, budget controls, provider routing, reporting, and chargeback support so teams can scale AI with better cost visibility.