Solution Delivered
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↳ Architectural Redesign & Code Restructuring 
 
Plexteq's architect began with a comprehensive audit of all services, producing detailed architecture diagrams and an actionable improvement roadmap. Domain-Driven Design principles were applied to redefine service boundaries, creating clean separation between acoustic data ingestion, feeding decision logic, sensor telemetry, and UI-facing APIs. Both the Python and C# codebases were refactored around consistent layered architecture patterns, standardizing naming conventions, dependency injection, error handling, and module organization across the full stack.
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↳ Messaging Infrastructure Overhaul 
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The RabbitMQ topology was redesigned from the ground up. Exchange and queue naming conventions were standardized, routing strategies were formalized, and dead-letter queues were introduced across all critical message flows to guarantee delivery semantics. Akka actor hierarchies were restructured with proper supervision strategies, enabling fine-grained failure isolation and automatic restart policies. Message schemas were versioned and documented, eliminating the silent schema drift that had caused data integrity issues in production.
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↳ Reliability Engineering & Crash Elimination 

Plexteq applied fail-fast and circuit-breaker patterns throughout the services to convert unhandled exceptions into observable, recoverable events rather than silent crashes. Health-check endpoints were implemented on all services and integrated with automated restart policies. Retry logic with exponential backoff was introduced for transient failures in network and messaging layers. Critical subsystems - including feeding command execution and sensor data ingestion - were given redundancy and state recovery mechanisms to maintain operation continuity during partial failures.
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↳ Centralized Logging & Observability 

A centralized structured logging infrastructure was established, aggregating logs from all Python and C# services into a single queryable system with consistent log levels, correlation IDs, and contextual metadata. This transformed incident response from hours of manual SSH triage to seconds of filtered log queries. Operational dashboards were built to surface service health metrics, queue depths, and message throughput in real time, enabling the engineering team to proactively address bottlenecks before they affected fish farm operations.

↳ Dependency Management & Build Standardization 

Python dependency management was standardized using Poetry with pinned lockfiles, eliminating the environment inconsistency that had caused intermittent failures. C# projects were migrated to consistent NuGet package versions with central package management. CI pipeline steps were introduced to audit dependencies for known vulnerabilities and flag version drift. This brought the development-to-production environment gap from a chronic pain point to a non-issue.