Linkbound: $25,000 ARR within the first 5 days

About the company

Linkbound is a tool designed for warm outreach on LinkedIn - born from the collaboration between Jasmin Alić, a LinkedIn strategy expert, and Senad Šantić, ZenDev CEO.

To understand Linkbound's value, it's important to distinguish between cold and warm outreach. 

Cold outreach means contacting people who have no prior connection to you or your content - essentially strangers. 

Warm outreach, on the other hand, focuses on people who have already shown interest in your content by engaging with your posts through likes, comments, or shares. 

These people are already familiar with you and have demonstrated some level of interest.

Linkbound helps sales professionals identify and connect with these warm prospects who have already engaged with their LinkedIn content. 

Linkbound is not a tool for those who want to “spam” LinkedIn - it is designed for professionals who wish to build genuine relationships.


 
 

The challenges

The business challenge

LinkedIn has evolved from a simple job search platform into a powerful tool for business networking and personal branding.

But, many users still don't leverage its full potential. 

The platform captures valuable engagement data - who's liking and commenting on your posts.

However, it does not systematically organize these interactions or identify high-potential leads.

Creators put out content, get tons of reactions, and then… what?

Many struggle to turn that visibility into real business opportunities. 

Without a clear way to track and prioritize interactions, they miss out on potential clients and leave money on the table.

Here's what Jasmin and Senad identified:
 

• LinkedIn users were sitting on a goldmine of warm leads hidden in their post engagements
• There was no practical way to filter these warm connections by company size, position, industry, or other key parameters
• Sales professionals struggled to maintain consistent relationships with high-quality leads over time
• LinkedIn had closed its API, making it difficult to access this valuable data systematically
• Organizations lacked visibility into how social selling efforts translated to actual business results

Jasmin noted, "LinkedIn is now an essential tool for business networking and personal branding, but many users still don't understand how to monetize their effort." 

The market needed a solution that could bridge this gap.

The technical challenge

Building Linkbound presented several complex technical hurdles:

• With LinkedIn's API closed, we needed to find another approach to accessing engagement data without violating platform terms or risking user accounts
   
• Security and compliance were non-negotiable.
   
• The system needed to continuously process and organize engagement data across potentially thousands of users and millions of interactions while maintaining performance.
   
• Sales teams required filtering capabilities based on multiple parameters to identify relevant prospects efficiently.
   
• We needed to design a scalable architecture that could grow with user adoption without performance degradation.
   
• Balancing Chrome extension functionality with web application features presented unique UX and development challenges.
   

The solution

The Build-measure-learn approach

Rather than diving straight into building a full-featured product, we applied our lean startup methodology. 

Instead of asking, "What do we want to build?", we asked, "What hypothesis do we need to test?"

Our core hypothesis

Sales professionals could achieve higher response rates by focusing outreach on people already engaged with their LinkedIn content.

Minimum viable product (MVP) development

We started with a minimal but powerful feature set.

We listed and filtered the LinkedIn profiles of those who engaged with the content. 

That way, we could quickly test without overcommitting resources. 

After confirming initial traction, we expanded based on direct feedback from the sales team. 

The system evolved to include: 

1. engagement analytics
2. advanced filtering capabilities
3. saved dynamic lists
4. interaction tracking
5. performance metrics
6. smart follow-up alerts

This iterative approach meant we built what users needed, not what we thought they might want.

Top network

Systematically collecting and organizing who engaged with your content

Filtering and tagging

Ability to filter by company name, size, industry, location, position title, and more

Custom lists

Saved filters that automatically update as new engagement data comes in

Interactions

CRM-like functionality to log conversations and track relationship development

Statistics

Metrics showing outreach effectiveness and conversion rates

Smart alerts

Reminders to maintain relationships with qualified leads

AI-powered messaging

Based on engagement, users get personalized message recommendations.

Technical implementation

The technical solution leverages multiple technologies:
 

Frontend:

• We combined React, Vite, and Tailwind to create a fast and polished Chrome extension that integrates naturally with Chrome's workflow.
• Web dashboard using the same tech stack for consistency and maintenance efficiency
• Mobile app developed with React Native for on-the-go access
   

Backend:

• Laravel API handles communication between databases and user interfaces
• Go workers for high-performance data processing, especially under load
• Laravel Filament for rapid internal admin panel development

Databases & infrastructure:

• High-availability Kubernetes cluster (SLA) ensuring 99.99% uptime
• Helm Charts for simplified deployment management
• Comprehensive monitoring via Prometheus and Grafana
• PostgreSQL with PgBouncer for connection pooling and read replicas
• Redis for caching frequently accessed data
• MongoDB for application audit trails

Monitoring:

• Fluentbit & Fluent Operator: To manage logs and improve observability, we deployed Fluentbit as the log collector and Fluent
• Operator to manage Fluentbit configurations in Kubernetes. 
• Fluentbit aggregates logs from all containers within the cluster and forwards them to our logging backend, where they can be indexed and analyzed. 
• This setup enables us to monitor logs in real time and troubleshoot issues quickly.

Security:

• The tool operates independently from the user’s LinkedIn account, with no direct API integration or connection to the profile, ensuring no risk of the accounts being penalized or banned by LinkedIn.
• Secure credential management with Infisical
• Sentry for proactive and comprehensive error handling - ensuring better user experience
• SonarQube for security hotspot in code and static code analysis
• DepentaBot for security upgrades of the packages
   

Team formation

We knew from the start that team composition would be critical for success.  Our technical team structure was intentionally lean and efficient:

• Technical lead - oversaw architecture and ensured code quality
• Backend developer - focused on data processing and API development
• Frontend developer - created the Chrome extension and web interfaces
• UX/UI Designer - developed the visual language and user experience
• QA specialist - conducted both manual and automated testing

Daily meetings focused on discussing the "single source of truth" - aligning feedback from Jasmin and Senad about user preferences with technical feasibility and design direction. 

The team maintained a culture of open feedback and investigation through weekly demos where everyone showcased completed work. 
 

The results

When we tested our hypothesis with ZenDev's own sales efforts, the results were remarkable:

• 93% response rate to messages sent to qualified leads (compared to typical cold outreach rates of 1-3%)
• 87% of responses led to booked meetings
• Generated actual converted business, confirming these were genuinely warm prospects

After launch, Linkbound achieved:

• $25,000 ARR within the first week
• 500 users installing the Chrome extension over a single weekend
• All this without launching major marketing campaigns

These results validated our build-measure-learn approach and confirmed that our MVP delivered on its core promise.

What's particularly noteworthy is that we achieved these results with an incredibly efficient investment of resources. 

By focusing on building only what was necessary to test our hypothesis and then iterating based on actual user feedback, we created a product that delivered immediate value without wasting time on unnecessary features.

As Senad puts it: 

"The biggest lesson we learned from this instance of SaaS building is that if you're solving your problem. 

The first level of 'Product market fit' should be attained when you, the creators, use the product."

This project exemplifies our approach to product development. We always prioritize understanding the business problem deeply: 
 

1. Create a minimal solution to test core hypotheses.

2. Measure real-world results

3. Iterate based on evidence rather than assumptions. 

The result is a product that delivers genuine business value with minimal waste.