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Ema raises $77 million Series B for "AI Employees" in HR, IT and finance

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By TrustList Editorial

Ema, based in Mountain View, California, announced a $77 million Series B on 23 September 2026, led by Creaegis with Accel, S32 and Prosus adding to earlier stakes. It says it has raised $140 million in total for its agentic AI for enterprise operations.

About Ema raises $77 million Series B for "AI Employees" in HR, IT and finance

Ema raises $77 million Series B for "AI Employees" in HR, IT and finance

23 September 2026 — Ema, a company based in Mountain View, California, announced on 23 September 2026 that it has raised $77 million in a Series B round. It says this brings its total funding to $140 million.

The round

  • Amount and stage: $77 million, Series B, as stated in the company's own announcement.
  • Lead investor: Creaegis.
  • Existing investors increasing their stakes: Accel, S32 and Prosus.
  • Use of the money: the chief executive's letter frames it as helping large enterprises move agentic AI from pilots into everyday operations; the announcement gives no itemised split.

What the company does

Ema sells what it calls "AI Employees": AI agents that carry out operational work in human resources, IT support and finance, working inside the systems a company already uses. Its announcement cites Wipro as a reference customer, with an employee assistant serving more than 240,000 staff and handling about 2.9 million queries a year.

Why it matters for buyers

The round is one of several in September 2026 for companies selling AI agents to run back-office processes rather than to answer questions. For HR, IT and finance leaders, the pitch is fewer tickets and faster case handling; the practical questions are how the agents are supervised, which actions they may take on their own, and how their decisions are logged.

What to check

  • HR and IT service teams considering agentic tools should ask for the audit trail of each action, the approval steps that can be required, and how the agent behaves when it is unsure.
  • Procurement should confirm where data is processed, whether customer data is used to improve shared models, and how pricing scales with volume of work rather than seats.
  • Finance teams should test the agent against their own controls, such as segregation of duties and approval limits, before allowing it to post or pay anything.

Sources

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