Client Profile
Real-time API monitoring & debugging platform
Engineering teams at companies with 10+ APIs
$500-5,000/month based on API calls
300,000+ companies with production APIs
Developers hate sales emails and ignore vendor outreach
The Problem: Developers Delete Everything
Traditional outreach to developers:
"Hi John, I see you're a Senior Engineer at TechCo. Our API monitoring platform can reduce downtime by 50%. Want to see a demo?"
Developer reaction: "Another vendor pitch." Block sender.
Our Approach: Speak Developer, Not Sales
Layer 1: Characteristic-Based Qualification
We built a comprehensive list based on company characteristics:
- Companies with 50+ engineers (need API architecture)
- SaaS/tech companies with $10M+ revenue
- Job postings for backend/API/DevOps roles
- Companies using cloud infrastructure (AWS/GCP/Azure)
- Businesses with mobile apps (always have APIs)
Simple qualification criteria: Do they likely have 10+ production APIs? Yes = qualified.
This gave us 315,000 companies with production APIs → 195,000 after removing enterprises with existing solutions.
Layer 2: Inference at Email Creation
For each qualified company, our AI analyzed available data to infer their situation:
- Company with 50+ microservices repos = likely debugging challenges
- Recent transition from monolith (based on repo history) = experiencing complexity
- Hiring SREs/DevOps = scaling challenges
- Multiple status page incidents = current pain
The key: We email everyone who fits criteria, but each email reflects their specific inferred situation. Some have visible pain signals, others just get intelligent assumptions based on their architecture.
Layer 3: Technical Credibility in Every Email
Our AI wrote emails that showed we understand their actual architecture:
To an engineer whose team manages 50+ microservices:
Hey Marcus,
Saw your team manages 50+ microservices across 3 Kubernetes clusters. Betting someone's SSHing into pods at 3am trying to correlate logs.
Spotify's team was doing the same dance. Now they trace requests across all services in one view. P99 latency dropped 40%.
We built this specifically for k8s architectures. Want to see how it works with your setup?
To a startup that just went from monolith to microservices:
Sarah,
Noticed FinanceAPI repo split into 8 services last month. Guessing you're hitting your first "which service caused the timeout?" mystery.
Every team discovers this fun: Request works fine in monolith. Breaks randomly in microservices. No idea why.
Stripe burned 2 months debugging this manually. There's a better way.
10 minutes to show you distributed tracing that actually works?
To a team actively dealing with incidents:
David,
Your status page shows 3 API incidents this week. With Black Friday coming, that's terrifying.
The "elevated error rates" from Tuesday looked exactly like what killed Shopify's checkout in 2019. Same cascade pattern.
We can spot these patterns before customers do. Free trial on your actual traffic?
Results
Campaign Performance
- Qualified companies:195,000
- Emails sent:78,000
- Response rate:2.4% (1,872)
- Meetings booked:56
- Show rate:93% (52 attended)
Conversion Metrics
- Started free trials:38
- Converted to paid:22
- Average deal:$2,100/month
- Total MRR:$46,200
- Annual revenue:$554,400
Client Testimonial
"We tried everything to reach developers. Your emails were the first that didn't sound like marketing. One senior engineer forwarded it to his team saying 'finally, a vendor who gets our architecture.' That never happens."
The Math of Scale:
- Traditional approach: Hunt for 5,000 companies with visible problems → 50 emails/day → 2 customers
- Our approach: Email 78,000 qualified companies → 2,500 emails/day → 22 customers
- Result: More emails = more pipeline, but each one still personally relevant
Why This Worked
❌ What Everyone Sends
"Our platform reduces downtime!"
✅ What We Sent
"Debugging microservices with kubectl logs at 3am?"
The Difference:
Found real architecture patterns from GitHub
Acknowledged their actual pain (3am debugging)
Spotify, Stripe (companies developers respect)
Straight technical talk, no marketing speak
The Breakthrough Insight
We didn't hunt for companies with problems. We emailed every company that likely had 10+ APIs, then made each email technically relevant based on what we could infer about their architecture.
78,000 emails. Each one different. Each one technically credible.
"This is the first vendor email I've ever forwarded to my team as actually useful."
— CTO response
"Your email described our exact problem better than our internal docs."
— Senior Engineer response
The Real Innovation:
Characteristic-based qualification for scale, combined with technical inference for relevance. Not hunting for needles in haystacks - emailing the entire haystack intelligently.
Ready for Technical Credibility at Scale?
See how our technical intelligence approach can help you reach engineering teams with authentic, architecture-specific messaging.
Free • 30 minutes • Technical approach consultation