Deterministic real-time backend for continuous data streams
Anonymised case study
Due to a non-disclosure agreement, we can't share the client name or project name. What you read here is the description of a real delivered project — anonymised.
Background
A European IoT company processed real-time sensor data. The platform initially grew quickly, but the processing logic had been built ad-hoc: every new customer got customisations, and the async pipelines became complexly intertwined. Their support team was firefighting weekly, and they were losing customer trust over inexplicable data discrepancies.
Problem → Solution
Async updates created inconsistent data states with unpredictable production behaviour.
A rule-based, deterministic processing engine with strict validation — predictable output for every input.
Approach
We re-architected the entire processing logic as a rule-based, event-driven system. The key insight: every input must have a deterministic output, regardless of processing order. The migration was gradual — old and new pipelines ran side-by-side for validation, and we only switched traffic when the new system matched or exceeded the old in correctness.
Outcome
4×
Traffic on the same infrastructure
0
Data inconsistencies since launch
~50%
Support escalations cut (month 1)
Data inconsistencies are gone; the system handles continuous load stably.
Operations
LIVE24/7 production operations, Firebase infra management, deployment automation and incident response.
Selected work
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