How We Built a Time Tracker with AI and Replaced Clockify
06.07.2026
CEO
We are a fast-growing product development team. Every year, we add 20% more people. And every year, our SaaS costs grow with them.
We used a popular time tracking tool. It worked well for a while, but over time it stopped fitting our needs. Customization was limited, and the features we actually needed weren't there. When we asked for improvements, the vendor didn't listen. Our data lived on their servers, not ours.
We were locked in. We had two choices: keep paying for a tool that no longer worked for us, or build our own. We chose the second path.
As a result, we built a fully functional time tracker in just 80 hours: that's 4 weeks with the team working part-time. It paid for itself in 6 months. The ROI? 728%.
We used a popular time tracking tool. It worked well for a while, but over time it stopped fitting our needs. Customization was limited, and the features we actually needed weren't there. When we asked for improvements, the vendor didn't listen. Our data lived on their servers, not ours.
We were locked in. We had two choices: keep paying for a tool that no longer worked for us, or build our own. We chose the second path.
As a result, we built a fully functional time tracker in just 80 hours: that's 4 weeks with the team working part-time. It paid for itself in 6 months. The ROI? 728%.
Build vs Buy: Why We Chose Custom Development
For a long time, the time tracking tool we used did its job. But as our team crossed certain thresholds, cracks started to show.
The pricing model punished growth - every new team member meant a bigger monthly bill. We couldn't adapt the tool to our internal workflows because customization options were too basic.
Meanwhile, the features we desperately needed simply weren't coming. We felt like we were paying for a tool that was built for someone else's team, not ours.
The final straw was realizing how dependent we'd become. All our time tracking data was locked inside their system. Switching to another vendor would mean starting from scratch or dealing with a painful migration. We were trapped.
The pricing model punished growth - every new team member meant a bigger monthly bill. We couldn't adapt the tool to our internal workflows because customization options were too basic.
Meanwhile, the features we desperately needed simply weren't coming. We felt like we were paying for a tool that was built for someone else's team, not ours.
The final straw was realizing how dependent we'd become. All our time tracking data was locked inside their system. Switching to another vendor would mean starting from scratch or dealing with a painful migration. We were trapped.
Build the Best Tool for Our Team
We didn't want to build just another time tracker. We wanted to build the time tracker - the one that actually works the way our team works.
Not a clone. Not a copy. A tool that fits our processes perfectly, not the other way around.
We gave it a name: SYNCHRON.TEAM.
Not a clone. Not a copy. A tool that fits our processes perfectly, not the other way around.
We gave it a name: SYNCHRON.TEAM.
How We Built It: 80 Hours from Idea to Product
Stage 1 “Kickoff & Scope”
We didn't overcomplicate things. We pulled together a small team: a Project Manager to keep things moving, a Business Analyst to figure out exactly what we needed, and a Senior Java Developer to build it.
Over 3-4 sessions with the BA, we defined the core features for the first version. We broke everything down into clear user stories and technical tasks. No fluff. Just what we actually needed to start using it.
The team estimated 4-5 weeks with part-time effort - everyone still had their main projects to handle.
We had everything we needed to begin.
Over 3-4 sessions with the BA, we defined the core features for the first version. We broke everything down into clear user stories and technical tasks. No fluff. Just what we actually needed to start using it.
The team estimated 4-5 weeks with part-time effort - everyone still had their main projects to handle.
We had everything we needed to begin.
Stage 2 “Data Migration from Clockify”
Starting from scratch wasn't an option. We had years of data inside the old system - projects, clients, time entries, reports, and all the relationships between them. We couldn't afford to lose that history.
So we exported everything. Projects, clients, time entries, reports, and the links between them. We moved it all into our new system.
This step turned out to be critical. Having real business data from day one made everything easier - better context for the AI, faster requirements, and higher accuracy during implementation. We weren't guessing what the data should look like. We just brought it with us.
So we exported everything. Projects, clients, time entries, reports, and the links between them. We moved it all into our new system.
This step turned out to be critical. Having real business data from day one made everything easier - better context for the AI, faster requirements, and higher accuracy during implementation. We weren't guessing what the data should look like. We just brought it with us.
Stage 3 “Define & Design with AI”
The biggest productivity boost came from planning, not coding. AI helped us structure requirements, design architecture, and map out user scenarios before we wrote a single line of code.
The result was the same quality of planning that used to take weeks - compressed into hours. And because AI worked from our real data, the requirements actually reflected what our team needed.
The result was the same quality of planning that used to take weeks - compressed into hours. And because AI worked from our real data, the requirements actually reflected what our team needed.
Stage 4 “Build with AI (Codex)”
The key insight? It's not AI instead of engineers. It's AI plus experienced engineers.
We used AI to handle repetitive work like boilerplate code, API integrations, tests, documentation. The heavy lifting that usually eats up time and attention. Our senior engineers focused on what actually matters: architecture, complex business logic, security, and code review.
AI made us faster. Experience made us right.
We used AI to handle repetitive work like boilerplate code, API integrations, tests, documentation. The heavy lifting that usually eats up time and attention. Our senior engineers focused on what actually matters: architecture, complex business logic, security, and code review.
AI made us faster. Experience made us right.
Stage 5 “Merge & Deploy”
Eighty hours from idea to production. A working MVP with core functionality - no extra features, no over-engineering. Just a tool that did what we needed.
The best part? If tomorrow we need a new feature, we don't ask a vendor. We build it ourselves.
The best part? If tomorrow we need a new feature, we don't ask a vendor. We build it ourselves.
What We Built
We didn't build everything at once. We built what we needed, when we needed it, and we're still expanding based on how our team actually works.
Stage 1 (MVP)
Time Tracking: track hours
Reports/Analytics: insights and data
Stage 2
Clients/Billing: client management and invoicing
Projects/Tasks: project and task management
Stage 3
Resource Planning: team capacity planning
Stage 4
Payroll/Calculations: salary and compensation calculations
Stage 1 (MVP)
Time Tracking: track hours
Reports/Analytics: insights and data
Stage 2
Clients/Billing: client management and invoicing
Projects/Tasks: project and task management
Stage 3
Resource Planning: team capacity planning
Stage 4
Payroll/Calculations: salary and compensation calculations
The Results
Key Takeaways & Insights
Here's what we learned from building our own tool in 80 hours:
1. AI changes the game. We built in 80 hours what would have taken months before. The difference wasn't working harder. It was working smarter with AI.
2. AI plus experience beats AI alone. Without experienced engineers, AI just generates code. With experienced engineers, AI generates the right code. The winning formula is AI + senior engineers, not AI instead of senior engineers.
3. Planning with AI is the real breakthrough. The biggest productivity boost wasn't in writing code. It was in requirements, architecture, and design. AI helped us get those right before we wrote a single line of code.
4. Real data matters. Migrating our old tool data gave AI the context it needed to generate accurate requirements and code. We didn't guess - we used real data from day one.
5. Building your own tools means freedom. No vendor dependency. Full control over your data. Features when you need them. If tomorrow we need a new feature, we don't ask a vendor. We build it ourselves.
1. AI changes the game. We built in 80 hours what would have taken months before. The difference wasn't working harder. It was working smarter with AI.
2. AI plus experience beats AI alone. Without experienced engineers, AI just generates code. With experienced engineers, AI generates the right code. The winning formula is AI + senior engineers, not AI instead of senior engineers.
3. Planning with AI is the real breakthrough. The biggest productivity boost wasn't in writing code. It was in requirements, architecture, and design. AI helped us get those right before we wrote a single line of code.
4. Real data matters. Migrating our old tool data gave AI the context it needed to generate accurate requirements and code. We didn't guess - we used real data from day one.
5. Building your own tools means freedom. No vendor dependency. Full control over your data. Features when you need them. If tomorrow we need a new feature, we don't ask a vendor. We build it ourselves.
Let's Build the Future Together
Two years ago, we were in the same situation as many companies. Growth, rising costs, vendor dependency.
We invested thousands of euros and months of team work.
Today, we can test hypotheses, launch new products, and bring even the most ambitious ideas to life - 100 times faster and cheaper.
Now we're building the future. Together.
Want to replicate this path for your business?
Talk to our team
We invested thousands of euros and months of team work.
Today, we can test hypotheses, launch new products, and bring even the most ambitious ideas to life - 100 times faster and cheaper.
Now we're building the future. Together.
Want to replicate this path for your business?
Talk to our team
Frequently Asked Questions
80 hours over 4 weeks with part-time team load. Everyone on the team had their main projects, so we worked on this in parallel.
728% ROI. The tool paid for itself in 6 months through SaaS savings alone.
Rising costs, limited customization, vendor lock-in, missing features, and data that belonged to the vendor, not to us.
AI accelerated requirements, design, code generation, testing, and deployment. The biggest impact was in planning - not just coding.
Yes. The same approach works for any internal tool or custom SaaS replacement. Start with a clear scope, use real data, and combine AI with experienced engineers.
Backend and frontend built with AI-assisted development using Codex.
Yes. Senior-level engineers with 11+ years of experience. The combination of AI + experienced engineers was the key.
Yes. We're expanding functionality based on team needs. Stage 3 (Resource Planning) and Stage 4 (Payroll) are in progress.