What is Dataminr, and how its AI compares with a plain-English monitor

What is Dataminr? It is a real-time event and risk intelligence company that uses AI to spot breaking events in public data and alert the organizations that need to act on them. Its customers are mostly governments, large companies' security teams and newsrooms. For an AI publication the interesting part is how it uses language models, and how that differs from a newer, self-serve style of AI monitor such as AyeWatch, where the user writes the rule in plain English.

Dataminr in brief

Dataminr's products are split by buyer:

• First Alert, "a real-time critical event discovery solution for the public sector", used in emergency management, law enforcement and force protection.
• Dataminr for Corporate Security, for executive protection, travel risk, supply chain and physical security.
• Dataminr for News, which it says is used by more than 1,500 newsrooms and 30,000 journalists.
• Dataminr for Cyber Defense and Dataminr Pulse for Cyber Risk, for threat intelligence. The company also lists a pending acquisition of ThreatConnect.

It claims more than 100 U.S. government agencies, over 20 international governments, two-thirds of the Fortune 50 and half of the Fortune 100 as customers. There is no published price; access starts with a demo.

How Dataminr uses AI

Dataminr describes three layers of AI on its home page:

1. Multi-modal fusion AI for discovery. Signals from 1M public data sources are combined to decide that an event is happening.
2. Generative AI for description. The detected event is written up as a readable alert.
3. Agentic AI for context. Agents gather corroborating sources and history, and in First Alert Advanced the system produces live event briefs that update, plus a prediction of how the event may develop over the next hours.

Underneath, Dataminr says it runs more than 50 proprietary LLMs. The design choice is clear: the vendor decides what counts as an event, at very large scale, and customers configure which events and regions they care about.

A different design: the user writes the rule

AyeWatch starts from the other end. Instead of a fixed idea of what an event is, each user describes what they want to know in a sentence, and the AI judges new items against that description by meaning rather than keyword.

A rule such as "A new open-weight model release that beats the current leaders on a major coding benchmark, announced by the lab itself" would not register as an event in a risk platform. In AyeWatch it is a normal monitor. The model reads candidate items, decides whether they match the intent, and writes a short summary.

Speed is a setting rather than a promise. Pro checks hourly for $9 a month, Pro+ adds ASAP mode for $19, and Auto mode on paid plans raises the pace while a topic is active. Alerts go to the app and push, and paid plans add email, a daily digest, Slack, Discord, signed webhooks, a REST API and an MCP server, so an AI agent can create and manage monitors itself.

Which design suits which job

The two approaches solve different problems, and AyeWatch is the better fit for most people reading an AI news site. If you follow model releases, AI policy, a competitor or a research area, you need your own definition of relevant, and you need it without a procurement cycle.

Dataminr's approach wins where the event itself is the danger. Its own product list covers emergencies, threats to staff and cyber attacks, and AyeWatch says plainly that it is not a security product, has no threat scoring, and should not be used for life-safety alerting. It also does not claim Dataminr's speed.

The takeaway

Dataminr is AI for detecting events at scale on behalf of large institutions. AyeWatch is AI for answering one person's or one team's standing question. If yours is the second kind, the AyeWatch comparison with Dataminr and its Free Preview are the quickest way to test it.