Managing dozens of interconnected services across multiple teams, Reibus International's engineers faced a familiar challenge: their system had grown too complex for any one person to fully grasp. By transforming how they approached technical design, they achieved what many think impossible - faster development with fewer bugs. Their 15% velocity increase and 30% reduction in integration bugs demonstrate how better technical context during design and implementation can help teams ship confidently even as systems grow more complex.
Like many fast-growing companies, Reibus faced a common scaling pain: their marketplace platform had grown from a handful of services to dozens of interconnected systems spread across different teams. Their existing approach - one engineer investigating and documenting technical designs in Word docs, followed by Slack discussions - worked fine when the system was smaller.
But as their architecture grew more distributed, this process began to break down. A seemingly simple change to inventory processing could unexpectedly impact CRM synchronization owned by another team. Engineers were spending hours piecing together system context before they could even begin designing solutions.
"Our system had evolved beyond what any single engineer could hold in their head. What looked like a simple change could cascade through half a dozen services. We needed a way to make these connections visible before they became production surprises," explains MacKenzie, Engineering Manager.
The team needed a way to make system complexity visible and manageable without slowing down development. After analyzing where engineers spent most of their time, three clear requirements emerged:
Instant Context: Engineers needed to quickly understand how services functioned and interacted without extensive codebase exploration.
Pattern Discovery: Teams needed to easily find and reference similar past solutions
Connected Workflow: Technical design needed to integrate seamlessly with existing development tools
Through team feedback, we implemented Nimbus to address these specific challenges. The key was tight integration with their development workflow - from Github and Jira integration to the ability to reference live code during design reviews.
"The team got immediate value from the Github integration and diagramming capabilities," notes MacKenzie. "Our QA engineers particularly appreciated being able to insert code snippets directly into design docs - it made reviews much more concrete."
Engineers could reference code directly, understand system architecture, and maintain context throughout implementation. They could create Jira tickets directly from the design document, maintaining clear lineage between design decisions and implementation tasks. This traceability helped keep the entire team aligned as features progressed from design to delivery.
"What really clicked was how it brought everything together," says MacKenzie. "The team could focus on solving problems instead of jumping between tools."
After two months and 10 significant technical designs, the results exceeded expectations:
📊 Hard Numbers:
30% fewer integration bugs (zero critical production issues)
15% faster development cycles
10 major features shipped confidently
Zero increase in design overhead
👩💻 Engineer Experience
40% improvement in early issue detection for technical designs
40% easier exploration of alternative technical approaches
20% increase in technical decision confidence
15% reduction in unexpected implementation challenges
"We’re getting more out of our technical designs and reviews," noted a senior engineer. "Because we have the system information readily accessible and digestible, we’re catching potential issues earlier and making better tradeoffs."
The success at Reibus shows how technical design can scale without becoming a bottleneck, but the improvements we've seen are just the beginning. We're working toward a future where technical design tools don't just help document decisions - they actively partner with engineers to make better ones.
Imagine starting a design and automatically seeing not just the immediate dependencies, but the full chain of potential impacts across services. Through a combination of static analysis and machine learning, we could identify subtle patterns that might indicate future problems: unusual data flows, potential performance bottlenecks, or hidden dependencies that cross team boundaries.
The real game-changer will be moving from reactive to proactive design assistance. Instead of just showing what exists, these tools could help engineers explore what could be. By analyzing patterns across the entire codebase, they could suggest proven approaches, highlight potential reuse opportunities, and even predict areas where extra care might be needed during implementation.
"What gets me excited is how this could change our design conversations," MacKenzie notes. "Imagine if we could automatically understand how a change might ripple through our services, or get suggestions about similar patterns we've used before. That's when it gets really interesting."
For teams like Reibus, this means moving beyond just documenting decisions to actually enhancing how those decisions are made. It means spending less time piecing together context and more time on meaningful technical choices. Most importantly, it means being able to move fast with confidence, even as systems continue to grow in complexity.
Key Takeaway: Technical design tools should evolve with your system's complexity. By focusing on making system context more accessible and maintaining tight integration with development workflows, teams can make better technical decisions without sacrificing velocity.
Ready to make system complexity manageable?
Book a 30-minute technical demo to see how Nimbus could help your team ship faster and with more confidence.
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