Your data. Your scenarios.
Generate a company from a seed, choosing its industry, size, tool mix and scenarios. For your own dataset, we scope the mapping from your S3 bucket or existing pipeline, including relationships and usage rights.
Enterprise RL environments
Train agents for real enterprise work.
Simulated companies with connected business data, realistic tools and enforceable policies. Tasks and outcome verifiers included.
Enterprise worlds. A complete learning loop.
A real workflow.
A result worth learning.
Connected company data. Sensitive decisions.
Repeat the workflow from a clean start.
One company. Connected histories.
Generate a company from a seed with your chosen industry, size, tool mix and scenarios, or scope your own S3 data integration.
Simulated company dataConnected sample records
CustomerAcme Co.
EmployeeJamie
ProjectBilling review
Salesforce
Zendesk
Slack
Jira
GitHub
Google Drive
Gmail
WorkdayA task-specific illustration. Supported actions and checks are agreed for your pilot.
Evidence you can inspect
Know why an
agent passed.
Trace tool actions, approval decisions and committed business changes. Debug failed attempts and inspect the evidence behind each verifier result.
- 01Share the billing summary#billing · Acme Co.Committed
- 02Request a status changeCase #740 · Open → EscalatedApproval required
- 03Approval decisionSimulated approver · scenario ruleDenied
- 04Escalate to the support team#support-escalations · Case #740Committed
Model traces cover traffic routed through Record. Evidence visibility follows your access permissions.
One company. Across your tools.
Simulated tool environments share the same customers, employees and projects across CRM records, tickets, documents and conversations.
Choose your company’s tool mix. Availability and verification coverage are confirmed for your pilot.
AWSSalesforce
Zoom
Snowflake
Google Calendar
GitLab
Box
Amazon S3GitHub
SQL
Stripe
Cloudflare
SAP
PostgreSQLZendeskGoogle Drive
Linear
Gong
PagerDuty
BitbucketGmail
Okta
Notion
AsanaJira
Confluence
SharePointWorkday
Dropbox
Google Cloud
Intercom
Figma
Microsoft TeamsSlack
Datadog
Microsoft Entra
Azure
HubSpotVary the world.
Keep your stack.
Change the company, scenario and tool mix while keeping the agent, model and training loop your team already uses.
Your agent. Your runtime.
Keep your model and training loop. Connect through your existing MCP client, or use a supported Record-managed sandbox with kernel-level enforcement for supported file, process and network policies.
Start with one
enterprise workflow.
Tell us the capability you want to improve. We’ll shape a company world, tasks and verifiers around it, then help your team inspect the results with your own agent.
Talk through your workflowBuilt from firsthand experience at Google and ServiceNow, working with Fortune 500 enterprises.
A few practical questions.
What does Record provide?
A simulated company with connected data, MCP servers for its enterprise tools, realistic workflow tasks, outcome verifiers and rewards. Your team can inspect the runs, business changes and verifier evidence.
Can we use our own agent, model and runtime?
Yes. Connect your agent to Record’s simulated MCP servers or APIs using its existing client. Run in Record’s managed runtime or your own, with your chosen model. Record provides the environment and grading; your training system uses the rewards to update the model.
Can we use our own company data?
Generate a company from a seed, choosing its size, industry and scenarios. The same customers, employees and projects connect across its tools, with a shared history. For your own data, we can scope an integration with your S3 bucket or existing pipeline, agreeing the format, relationships and usage rights first.
Who defines the tasks and training objective?
We build tasks and tests with your team around the capability you want to improve. Each includes a natural business request, starting company state, hidden success checks and a budget. Company and scenario variations create different instances. You choose the training objective and own the model-training loop.
What results and rewards do we receive?
Each attempt produces per-check outcomes, an overall result and supporting evidence. Record’s default terminal mapping awards 1 for a qualified, fully assessed success and 0 for a fully assessed failure. Incomplete or unqualified results receive no reward, with a reason. Your training loop can use this mapping or derive its own signal from the structured check results.
How do we start?
Start with one enterprise workflow. We shape the simulated company, tasks and checks around it, then help your team evaluate the attempts and evidence with your own agent.
How do you verify outcomes and resist reward hacking?
Verifiers run separately from the agent and check actual business changes and policy behavior. We test the checks against successful, failed and adversarial runs. Success criteria are explicit for each task. A prohibited business change fails the run even when other checks pass. When required evidence is missing, we explain the gap and withhold the reward.
What should your agents get better at?
Let’s scope the world, tasks and verifiers for your next pilot.
Discuss a pilotfounder@tryrecord.com