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

Map AI usage and risk

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

Define policies and controls

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

Route traffic through a control layer

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

Connect governed AI workflows

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

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

Woman interacting with transparent digital screen displaying data and charts in a tech environment.

Security & compliance

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

People working focused at computers in an office with shelves and documents in the background.

IT & platform teams

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

Woman with glasses explaining coding concepts while holding a tablet amid floating code graphics.

AI program leaders

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

Businesswoman presenting financial data on a large screen in a conference room.

Finance & procurement

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

Five coworkers collaborate and work on computers in a bright modern office with large windows.

Engineering teams

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

Two professionals collaborating and smiling while working on a laptop in a modern office.

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

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

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. AgentWatch supports 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.