Koala in the Field: How GreyNoise Turns Docs into Pipeline

GreyNoise helps security teams cut through the noise—literally. The Series A security company delivers real-time, verifiable threat intelligence through the largest and most sophisticated internet sensor network, trusted by over 40,000 users around the world. By filtering out noisy, low-priority alerts, GreyNoise empowers defenders to focus on the most urgent and critical threats. So when the team set out to bring the same clarity and precision to their go-to-market strategy, they turned to Koala.

The problem: Finding prospects that want to find them

GreyNoise wanted to focus their GTM team’s efforts on opportunities most likely to convert—timing outreach with the exact moment a buyer within their ICP showed real intent. Firmographic data was useful for identifying target accounts, but the team needed more nuanced information about current priorities to identify surging intent.

The Solution: Engage when the signal is strongest

The Play GreyNoise built in Koala is simple, but powerful:

1. Find when target accounts were researching the problems they solve
2. Combine with historical engagement data from GreyNoise's CRM
3. Augment with AI-research to find each company’s security posture and tooling
4. Tune outreach from AI insights, and triggering event signals

This surgical approach is especially useful in enterprise security deals where buying cycles are long, and often involve multiple stakeholders. Rather than chasing generic intent, GreyNoise now zeroes in on specific buying behaviors exhibited by every persona involved in the deal.

Building the Play: Target account engagement

The team set up a Play to alert sellers when target accounts engaged with high-converting content, following the below 4-step process:

1. Define Play goals: Direct Koala Coach to execute on a particular Play, including researching target accounts that show high intent.

2. Define trigger criteria or events: Set event thresholds including visits to high-converting pages on their website or specific activity within their free product.

3. Find additional personas: Sometimes, the person triggering the alert may not be the best persona to evaluate GreyNoise. AI-Auto-prospecting brings in an adjustment number of other target titles from the same account.

4. Run AI research agents: Define agents that search for nuanced context about recent events, top tools, and existing processes at the target account. Examples might include publicly available information about existing security infrastructure or standards followed.

A Live Look: Timing outreach to a company researching PAN-OS

When one of GreyNoise’s target accounts engaged with blog content about a specific vulnerability (a high-intent moment for GreyNoise) Koala was ready. The GreyNoise team received an alert about the activity, along with additional context about the account so the rep assigned would know exactly what happened, and what to do next.

This is when they began to really see the Koala Coach at work: Referencing the Play strategy to gather additional information about the account, combining with GreyNoise value props, and crafting a hyper-contextual message to exactly the right person, at exactly the right time.

How you can run the same Play

To build a similar Play, you’ll need a strong understanding of your target audience’s pains, and what they stand to gain with a solution like yours.

1. Define your high-fit accounts (ICP + stage + role)

2. Track content consumption and web behavior using Koala’s signal engine

3. Enrich with AI research to get beyond firmographics

4. Pull in CRM data to understand past activity and relationship status

5. Let Koala generate outreach that actually reflects what the buyer cares about

This approach replaces generic outreach with strategically timed, intelligence-driven engagement that addresses immediate needs with remarkable precision—transforming how enterprise sales teams connect with prospects at exactly the right moment.

Want help building a version of this play for your team?

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