How Cloudflare’s APAC Team Booked 100 Demos in 2 Weeks With 2 Reps + AI Agents

Cloudflare’s APAC team wanted to generate more qualified pipeline without scaling SDR headcount or relying on generic high volume outbound.

We built an AI powered outbound system that continuously identified companies showing relevant infrastructure signals, researched why each account might need Cloudflare, and generated personalized outreach around that specific trigger.

With just 2 sales reps supported by AI agents, the system helped book 100 demos in 2 weeks.

Traditional outbound made it difficult to know which companies were actually worth contacting.

Sales reps were spending too much time:

• Building account lists
• Researching company infrastructure
• Looking for relevant buying signals
• Understanding what problem Cloudflare could solve
• Writing personalized messages manually

The bigger issue was timing.

A company changing its cloud infrastructure or launching a new website may suddenly have a strong reason to evaluate security, performance, CDN, or networking infrastructure.

But without continuously monitoring those events, reps often reached accounts too early, too late, or with messaging that had no connection to what the company was currently doing.

We wanted to build a system where the signal determined who entered outbound and what message they received.

Instead of building static prospect lists, we created three continuously running lead generation workflows.

We monitored companies for changes in their cloud and infrastructure stack.

The system identified signals such as companies adopting, replacing, or expanding technologies related to:

• Cloud providers
• CDN infrastructure
• Hosting
• Security
• Networking
• Web infrastructure

AI agents then researched the change and determined whether there was a relevant Cloudflare use case.

This gave reps a concrete reason to reach out instead of sending a generic infrastructure pitch.

We continuously tracked newly launched websites and digital properties across target markets.

A new website often creates immediate requirements around:

• Website performance
• CDN
• DDoS protection
• DNS
• Application security
• Infrastructure scalability

The system identified relevant companies, enriched the account and decision makers, and automatically added qualified opportunities into outbound campaigns.

This allowed the APAC team to reach companies while infrastructure decisions were actively being made.

We monitored LinkedIn activity from technical leaders and target accounts for conversations around cloud infrastructure.

The system looked for posts discussing topics such as:

• Cloud migrations
• Infrastructure scaling
• Website performance
• Security incidents
• Cloud costs
• Application delivery
• CDN or networking challenges

Instead of simply treating engagement as a signal, AI agents analyzed the actual content of each post to understand the underlying problem.

Relevant accounts were then enriched and routed into the appropriate outbound workflow.

Finding the signal was only half of the system.

The next step was understanding why the signal mattered for that specific company.

For every qualified account, AI agents researched:

• What changed
• Why the company may have made the change
• Their current infrastructure
• Potential infrastructure limitations
• Relevant Cloudflare products
• The likely business or technical impact

The outreach was then generated around that specific context.

The goal was not to make emails look personalized.

The goal was to make the reason for reaching out genuinely relevant.

The final workflow looked like this:

1. 1Buying signal detected
2. 2Company researched
3. 3Account qualified
4. 4Decision makers identified
5. 5Contact data enriched
6. 6AI analyzes likely problem
7. 7Personalized messaging generated
8. 8Prospect added to outbound
9. 9Rep handles replies

AI agents handled the repetitive research and data work continuously.

The two sales reps focused primarily on conversations, qualification, and moving interested accounts into demos.

Within two weeks, the system helped Cloudflare’s APAC team book 100 demos with just two sales reps.

Instead of asking reps to manually research hundreds of companies, the system continuously surfaced accounts with a reason to buy and gave reps the context needed to start relevant conversations.

The result was a fundamentally different outbound model:

The biggest change was not simply using AI to send more outbound.

It was using AI to decide who to contact, why now, and what problem to talk about.