Two months after closing a ten million dollar seed round led by a16z Speedrun, ZeroDrift is opening its compliance enforcement platform to developers directly. The company just launched Command, a self-service console where developers can get an API key, activate regulatory rule packs, and start checking their AI agents' output for compliance violations before those agents ever talk to a customer. New signups get 1,000 dollars in free credits to test it against their own systems first.
ZeroDrift's pitch is straightforward: AI agents are now writing customer communications at scale, and most companies have no real way to stop an agent from saying something that breaks FINRA, SEC, HIPAA, GDPR, or their own internal policy. ZeroDrift sits between the agent and the end user, checks every message before it goes out, and either rewrites it to be compliant or blocks it outright.
"Whatever an AI says, we want to control what an AI can say by enforcing compliance and policies on that," said Kumesh Aroomoogan, founder and CEO of ZeroDrift. He spent 13 years building Accern, an early NLP platform for financial services that he sold last year, before starting ZeroDrift. "There was no preventative compliance because LLMs didn't exist back then. You couldn't catch these things on the fly and rewrite them to be fully compliant."
Why Regex and General LLMs Fall Short
Before building ZeroDrift, Aroomoogan spent time with two large banks that had already tried solving this themselves. Their first approach was regex and keyword filtering, stripping out flagged phrases before an agent's message went out. It generated so many false positives that customers using the beta chatbots got frustrated with the constant mistakes.
Their second approach was routing every message through a general-purpose LLM with a compliance prompt attached. That worked better, but it introduced two new problems: cost and latency. Checking hundreds of millions of messages through GPT-class models roughly doubled the banks' existing AI spend, and each check added five to ten seconds of delay.
ZeroDrift's answer was to build a small language model trained specifically for compliance, rather than leaning on a general-purpose model for the job. Aroomoogan said that's what let the company hit the accuracy the banks needed at a fraction of the cost and latency of routing checks through a frontier model.
What's in Command
Command is where developers actually work with ZeroDrift. After signing up, a developer gets an API key and can activate whichever pre-built regulatory rule packs apply to them, along with uploading their own internal policies, such as HR, legal, security, or brand and pricing guidelines. Those policies can then be assigned per-agent, so a customer support bot and a sales prospecting agent can each be held to different rules.
The platform also keeps a running activity log of every decision it makes: which messages it blocked, which it rewrote, and which it let through clean. Aroomoogan described it as a system of record that gives compliance teams an audit trail they didn't have before.
Before pushing anything to production, developers can test messages in a live sandbox inside Command Center, pasting in sample emails or chat messages to see how ZeroDrift would handle them.
What the $1,000 in Credits Actually Buys
ZeroDrift prices by token, similar to OpenAI and Anthropic. Aroomoogan said five dollars covers roughly a million input tokens, which works out to about 40,000 documents or close to a million Slack messages run through compliance checks. That means $1,000 in credits gives a developer enough room to backtest a substantial chunk of historical communication and see what ZeroDrift would have caught, before ever connecting it to a live agent.
Notably, ZeroDrift only charges for input tokens. Rewrites, the output tokens generated when a message needs to be fixed, are free. Aroomoogan said internal testing suggests roughly 5% of a typical company's AI communication ends up non-compliant, and covering that rewrite cost is meant to keep pricing simple and predictable.
A Concrete Catch
Aroomoogan pointed to a real example from one of ZeroDrift's customers, a startup building AI wealth management agents. A customer asked the agent whether their money was safe at the bank. The agent's draft response was going to say the money was "100% safe" because of FDIC insurance, without stating the actual coverage limit. ZeroDrift caught the draft and rewrote it to specify that FDIC coverage protects up to $250,000, adding the disclaimer the original response was missing.
That's the kind of gap ZeroDrift is built to catch: technically well-intentioned, but missing the specific regulatory detail that turns a helpful answer into a violation.
Who It's Actually For
Aroomoogan said roughly half of ZeroDrift's customers are enterprises, split about 60/40 between financial services and healthcare, with the other half startups and scale-ups building products for regulated industries. He said the startup segment converts fast because those teams don't want to build a compliance model from scratch, while enterprise deals move on a longer sales cycle.
There's also a regulatory tailwind pushing in ZeroDrift's direction. FINRA has started writing AI-generated communication into its rules directly, treating a violation from a chatbot or AI agent the same as one from an individual broker, fines and legal exposure included. Aroomoogan sees that as validation that enforcement infrastructure for AI agents is becoming a requirement, not an option, as more regulated firms move to deploy AI at scale.
ZeroDrift plans to expand rule pack coverage into additional regulated verticals, including manufacturing and government, which Aroomoogan said mostly requires encoding new regulations rather than rearchitecting the platform itself.
Developers who want to see what ZeroDrift catches in their own systems can sign up for Command and use the $1,000 in credits to test it against historical communication before deciding whether to run it live.