EachRun is an enterprise AI execution and governance platform. It gives every AI run started by an employee, agent or business system an identity, boundary, record and result.

Enterprise AI execution and governance

Every AI run,accounted for.

Every execution has an identity, a boundary, a record and a result.

Discuss a pilot See the interface

EachRun employee workbench showing projects, running workspaces and AI quota — Employees start work in one place. Each execution remains attached to its project and owner.

AI has no shortage of starts. It needs a certain finish.

A prompt, workflow or agent call can start in seconds. Enterprise work also needs to answer who started it, what it could use, what happened and what it delivered.

That is the promise behind EachRun: not one successful run, but every run brought to a result the business can manage, reuse and stand behind.

Identity: Who or what started the run
Boundary: What data, tools and models it may use
Record: What happened during execution
Result: What was delivered and accepted

One platform for every run

Employees do the work in cloud workspaces. Administrators govern the same execution from the other side.

WorkbenchProjects, requests and open tasks
WorkspaceA cloud machine for the project
DiscoverAvailable models, agents and tools
Share centerTemplates the team already proved
AI quotaWhat was used and what is left
Admin consoleAccess, review and model providers

Workbench

Four steps to start, no training required

A new employee does not install an environment, configure keys or hunt for documentation. The workbench walks them through requesting a workspace, and they start working once it is approved.

Request a workspace, an administrator approves it
Projects, running workspaces and remaining quota on one screen
Switch to the expert view when finer control is needed

EachRun workbench with the four-step getting started guide — Getting started: request a workspace, enter it, run the task, review the result.

Workspace

A cloud machine per project, so the work stays when people move

The workspace is the project's own environment. Files, context, tools and execution records live inside it, and a colleague can be added to the same workspace and continue.

A standard environment that does not depend on one person's laptop
Project files, context and execution records kept together
Several people in one workspace for collaboration and handover

EachRun project workspace interface — Project workspace: project files, tools and company-approved AI in one place.

Discover

When a task stalls, switch the model or the agent

Different tasks suit different models and agents. Discover lists what the company has connected and what this employee is allowed to use, so switching takes seconds.

The company connects models and agents once, not per employee
Availability follows role and project
Project context is kept when switching

EachRun workspace showing available AI skills and models — Available capabilities stay inside the project context, ready to use without moving files elsewhere.

AI quota

What AI cost this month, and who it was spent on

Quota is allocated and reported by person, project, team and model. Employees see what they have left, and management sees which work the money went into.

Set limits in advance instead of discovering spend later
Usage by person, project, team and model
Cost attributable to a project for internal allocation

EachRun workbench showing projects, active workspaces and remaining AI quota — Employees see remaining quota beside the projects and workspaces where it is used.

Admin console

Simple for employees, visible for administrators

Workspace resources, organization directory, access control, model providers, approval requests, scheduled jobs and login logs sit in one console.

Access control and roles decide who may use what
Model providers and keys held centrally, not scattered
Approvals, login logs and scheduled jobs in one place

EachRun administrator console overview with workspaces, agents, model access and tasks — Admin console: workspace resources, capabilities and models, tenants, projects and access.

Security and boundaries

Set the boundary first, then let AI in

Data boundaries, retention periods and integration scope are confirmed item by item in the deployment plan.

One identity Employees, business systems and agents each hold an identity in the directory instead of sharing accounts.
Layered access Models, tools, files and permitted actions are controlled by role, project and workspace.
Central key custody Model provider keys are held and rotated by the platform, not spread across people and systems.
Execution records Who started a task, what it used, how it ran and how it ended stays available for later review.
Requests and approval Workspaces, resources and capabilities are granted through an approval flow that leaves a record.
Deployment choice Where data may not leave the organization, private or hybrid deployment defines the boundary.

Choose where it runs and how far it reaches

Start with one team, or plan for the whole company. It depends on your stage and your data requirements.

SaaS service

Hosted by us and ready to use, for teams validating the value first.

No environment to build
Per user or per team
Extend to more teams later

Ask about access

Enterprise team edition

For a department already rolling out, with directory, project spaces and usage reporting.

Organization and role access
Projects and workspaces
Usage and quota reporting

Ask about scope

Enterprise governance edition

For companies managing AI use across the organization, with the full governance set.

Access control and approval flows
Execution and login records
Budgets across teams and projects

Ask about scope

Private deployment

Runs inside your own environment, with data boundaries and model access defined by you.

Own data centre or private cloud
Data-stays-inside setup
Implementation and operations support

Ask about deployment

Hybrid deployment

Sensitive data stays inside, general capability uses external models, split by scenario.

Internal and external models by case
Routing by data sensitivity
One view of usage and records

Ask about boundaries

Business system integration

Let CRM, ERP, OA and ticketing systems use AI through one entry point.

System identity and access
Usage and cost attribution
Scope confirmed during the pilot

Ask about integration

What you can check

Everything listed here can be verified and written into an agreement.

Company

Legal entityChengdu March Orange Intelligent Information Technology Co., Ltd
OfficeRoom 1307, Block B, Junxin Building, No. 288 Shuyue East Road, Jinniu District, Chengdu
Phone400-030-8696
Email[email protected]

What the pilot delivers

EnvironmentConfigured projects, workspaces, access and model connections
RecordsTask baseline, execution records, usage and cost detail
ConclusionBefore-and-after comparison with a recommendation
QuoteA formal quote based on actual usage and scope

How acceptance works

BaselineTime, rework and cost recorded before the pilot starts
ScopeOne team, one or two recurring tasks, agreed in writing
ComparisonSame measures applied again after four to six weeks
DecisionYou decide whether to stop, adjust or expand

What sets the price

ScaleNumber of users and teams
DeploymentSaaS, private or hybrid
ResourcesModel and execution resource usage
IntegrationWhich business systems must be connected

Prove one real task first

No company-wide plan needed up front. Pick work a team does every week, run it for four to six weeks, and decide from the numbers.

Discuss a pilot

Scope: One team and one or two recurring tasks
Period: Normally four to six weeks
Review: Compare the outcome with the baseline

What the pilot leaves ready for review

Configured projects, access, models and execution resources
Task baseline, acceptance rules, usage and execution records
A before-and-after comparison to stop, adjust or expand with evidence

Pricing depends on team size, deployment, AI resources and integration scope.

Common questions.

What is EachRun?

EachRun is an enterprise AI execution and governance platform. Every run started by an employee, agent or business system has an identity, boundary, record and result.

How is this different from personal AI tools such as ChatGPT or Cursor?

Personal AI tools help one person finish a task, and the environment, account and records stay with that person. EachRun provides a shared company workspace, so each AI task has an identity, access rules, a quota, project context, execution records and a result, and colleagues can enter the same workspace to collaborate or take over.

Do we have to replace our existing AI tools and business systems?

Usually not. Existing models, knowledge bases, repositories and business systems can be connected within the agreed delivery scope, which is confirmed during the pilot.

Is private deployment supported?

Yes. Alongside the SaaS service, enterprise, private and hybrid deployments are available so data boundaries and model access can match your requirements.

Is it hard for employees to start?

Employees do not install an environment or configure keys. On first sign-in the workbench guides them to request a workspace, and they begin once an administrator approves it.

How are the pilot and pricing determined?

A pilot normally uses one engineering or IT team and one or two recurring tasks for four to six weeks. Time, rework, cost and results are recorded before the pilot and compared afterwards. Pricing depends on team size, deployment, model and execution resources, governance requirements and integration scope.

Start with one real AI task.

Tell us which teams use which AI tools, and what is hardest to manage across cost, access, handoffs or execution records.

Phone400-030-8696 Email[email protected]

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