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How Ambee is Redefining Advertising During Natural Disasters

August 25, 2025
2 min read
How Ambee is Redefining Advertising During Natural Disasters
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At Ambee, we believe physical risk intelligence must evolve as fast as the world around us. That means continuously advancing our datasets, adapting to real-world complexities, and ensuring our partners can deliver experiences that are not only effective but also compliant.  With natural disasters and severe weather increasing in frequency, duration, and severity, the issue for businesses is how to effectively mitigate or capitalize on this increased risk while at the same time ensuring compliance.

Recently, we partnered with a U.S. leader in telemarketing and customer outreach. Their challenge was clear: deliver contextual, compliant engagement in a landscape where natural disasters and severe weather events are increasingly impacting and shaping customer behavior within regulatory frameworks.

The challenge: Natural disaster and physical risk data are anything but simple

Government agencies and reporting bodies rarely provide disaster data in developer-ready formats. Critical information such as impacted regions, severity, or duration often exists only in narrative text. For example:

A cyclone with peak wind speeds of 80 mph is expected to impact coastal counties over the next 12 hours.

For an enterprise engagement system, this type of report raises several questions: Which counties exactly? When does the event end? What are the probability and confidence levels?  Is this a red alert or a low-level advisory? Without clear parameters, campaigns risk either being paused unnecessarily or, worse, continuing in places where they are non-compliant or tone-deaf.

On top of this, many states in the U.S. prohibit promotional calls or messages when FEMA has declared an emergency. In 2024, FEMA issued a total of 90 major disaster declarations, which is nearly double the annual average and was reported by CNN and the International Institute for Environment and Development (IIED), and this trend is only expected to increase. For instance, New York’s Nuisance Call Act prohibits unsolicited sales calls during emergencies. Outreach campaigns therefore risked being non-compliant without a real-time regulatory layer.

Our solution: Enhancing Ambee’s natural disaster data

To meet these challenges, we enhanced our natural disasters dataset with a set of powerful, ML and AI-driven capabilities tailored for enterprise-scale engagement.

From raw reports to structured intelligence

Raw disaster reports are rarely designed for integration. Agencies and reporting bodies publish information in varying formats, often burying critical details inside long narrative descriptions. One source may describe a wildfire in acres, another in hectares, and another in square kilometers. Key attributes such as affected area or event end date are often missing altogether.

Ambee’s products address these inconsistencies by using advanced ML and AI models to extract the essential details, normalize units, and fill in gaps with intelligent estimates. The result is a consistent, analytics-grade structure that enterprises can rely on for decision-making and compliance.

Before:

“An earthquake of magnitude 5.2 struck 30 km north of City X at 10 km depth.”

“Heavy rainfall is expected in eastern districts, potentially causing flash floods.”

After (Ambee):

{
  "event": "earthquake",
  "magnitude": 5.2,
  "location": "City X, 30 km north",
  "depth_km": 10,
  "alert_level": "moderate",
  "status": "active",
  "estimated_end": "2025-08-21T18:00:00Z"
}

Key enhancements delivered

  • AI-driven detail extraction: Our system parses unstructured text to surface actionable information such as identifying magnitude, depth, wind speed, number of people affected, and more.
  • Unit standardization: Measurements such as burned area are converted into a single, global standard (e.g., all wildfire data expressed in square kilometers).
  • Impact-adjusted alert levels: Ambee provides green, orange, and red risk categories not only based on event physics (e.g., magnitude or wind speed) but also on real-world impact, like the number of people displaced or injured.
  • Estimated missing values: Where no end dates are provided, Ambee applies semantic rules to infer duration. Where no map exists, estimated polygons are generated to intelligently approximate the extent of impacted areas.
  • Regulatory integration: FEMA datasets are dynamically embedded with minimal lag, ensuring that campaigns automatically pause in FEMA-declared emergency zones, keeping businesses and advertisers compliant.
Ambee’s API unifies global disaster signals through proprietary intelligence algorithms, providing consistent and actionable data you can build risk and predictive insights on.
- Chandrashekar D, VP of Engineering

The outcome: responsible, contextual engagement

For our enterprise partner, this meant campaigns could now adapt dynamically:

  • Avoiding outreach in disaster-affected zones
  • Complying automatically with FEMA guidelines
  • Reducing wasted spend on low-response regions
  • Improving conversions by engaging audiences at the right time

The result is a new standard in responsible engagement, a system that understands the world as it is and adjusts accordingly.

Toward a future of responsible engagement

This collaboration reflects Ambee’s core philosophy: impact, compliance and risk mitigation is our bottom line. By tailoring our natural disaster offering to enterprise needs, we have not only advanced our dataset but also created a model for how climate intelligence can integrate seamlessly into everyday business systems, empowering organizations to act with precision, responsibility, and foresight.

Keep an eye out for our upcoming technical deep dive, where we will share the engineering story behind this work, including the semantic rules and standardization processes that make Ambee’s disaster intelligence truly enterprise-ready.

Interested in exploring how Ambee’s disaster intelligence can power your business? Get in touch with us.

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