Work that required real engineering judgment.

The Situation

A workforce management consultancy operating across Denmark and Spain needed to integrate their scheduling platform with six third-party enterprise systems, including ERP, payroll, and time-registration services, each with different data models and update frequencies. Previous integration attempts had created a fragile, undocumented sync layer that broke regularly and required manual intervention.

Our Approach

We rebuilt the integration layer with a clean event-driven architecture, introduced a canonical data model that translated between the six external formats, and added observability so the team could diagnose and fix issues independently. We also worked directly with the client's engineers to document every integration pattern and establish standards for future additions.

Outcomes

• Eliminated 80% of manual sync interventions within 3 months
• Reduced integration incident time-to-diagnosis from hours to minutes
• Enabled two new enterprise client onboardings without custom integration work
• Delivered full internal documentation and runbooks

The Situation

A growing staffing and scheduling marketplace had accumulated significant technical debt after rapid early growth. The mobile app backend had grown from a simple REST API to a complex system with undocumented business logic, inconsistent data models, and no clear ownership of critical workflows. The team was spending more time on incidents than features.

Our Approach

We performed a full architecture audit, then systematically rebuilt the backend: introducing domain separation, replacing ad-hoc database queries with a maintainable repository layer, and implementing event-driven sync between mobile clients and backend state. We worked alongside the existing team, not replacing them, and established engineering standards that carried forward after our engagement ended.

Outcomes

• Reduced mobile app API error rate by 94%
• Cut deployment cycle from bi-weekly to daily
• Enabled the internal team to ship independently after engagement
• Full API documentation and integration test coverage on core flows

The Situation

A retail technology startup was building AI-assisted unmanned store solutions but had no product infrastructure to support store onboarding, inventory management, access control, or operational reporting. They needed a platform built quickly, correctly, and in a way that could scale as the product matured.

Our Approach

We designed and built the core platform: store management, real-time inventory sync, age-verification and access control APIs, and an operational dashboard. We integrated AI-assisted stock prediction using purchasing pattern data. The architecture was designed for modularity so the client could hire and extend the platform in-house after initial delivery.

Outcomes

• Complete platform launched in 14 weeks
• Live in first physical store within 18 weeks
• AI inventory prediction accuracy above 87% in first 90 days
• Clean, documented codebase handed over to client team