Era announced a console that can generate an entire synthetic enterprise with a single CLI command. For example, running era new --industry fintech --size mid --systems salesforce,zendesk,slack creates a mid‑size fintech company populated with realistic deal sizes, headcounts, and ticket volumes (up to 12,000 tickets per tenant)【https://console.era.eon.io/】.
Building a synthetic company
The platform fabricates a coherent identity that appears across Slack, Jira, Zendesk, and Salesforce, so every system references the same employee record. Data distributions follow real‑world shapes—deal size follows a log‑normal median of $17k, customer headcount median 180—rather than uniform random noise. Deliberate “mess” such as duplicate rows and junk entries are injected and listed in a manifest, enabling agents to handle realistic data‑quality issues.
What the generated estate contains
A top‑tier tenant includes roughly: - 18,766 contacts - 15,801 files (including 12,050 rendered documents) - 13,758 call recordings (6,086 hours total) - 12,000 tickets and 12,000 issues - 4,354 incident‑chat messages from 1,752 distinct authors All of these assets are exposed via live vendor‑native APIs and a unified MCP endpoint, so reads and writes are reflected instantly across systems.
Using the live stack in CI
Era publishes Docker images for each emulated system (e.g., erabyeon/slack:latest). Teams can spin up the full estate in a CI job with docker run --rm -p 8115:8080 erabyeon/slack:latest. Because the services run continuously, an AI agent can issue a REST call like GET /api/v2/tickets/4182.json to Zendesk and see the same state a subsequent MCP write would produce.
Cautions and missing details
The environment is entirely synthetic; it should not replace a staging system that contains production data. Pricing and usage limits are not listed on the public console page, so teams will need to contact Eon for cost information before committing.
When to try Era
If you need a realistic, end‑to‑end test bed for AI agents that interact with multiple SaaS APIs, spin up a small tenant (e.g., a “mid” fintech company) in a sandbox CI pipeline and evaluate agent behavior before moving to real services.
