
Sparkling Neuronics Labs
Re-architect AI code that has outgrown the prototype
About
Sparkling Neuronics Labs re-architects AI-built code that has outgrown the prototype. The edge is the combination: transformation architect, AI-dev-tool builder, and daily AI-coding practitioner. The principal brings 25+ years in software, including 15+ years architecting enterprise transformations at a tier-1 strategy consultancy and as Global VP of AI at a listed European digital services firm. His transformation background spans huge big-bang core rewrites, monolith decoupling to greenfield builds, and moving prototypes all the way through MVPs to production. He built AI development tooling from the inside: agent orchestration, RAG over vector and graph stores, MCP/A2A integration, and multi-agentic workflows. He also builds daily with Claude Code, Cursor, Codex, Gemini CLI, Antigravity, and OpenCode. Sparkling is new in 2026; the principal has already run an independent consulting practice for 10+ years before. The work is not cleanup and not an audit. Engagements produce boundary maps, ADRs, target-state architecture, confidence-banded cost/time/risk models, and a five-outcome production scorecard: ship, patch, refactor, rewrite, or retire. Architecture only: no implementation pod, no remediation upsell, and no incentive to recommend a rebuild when the right answer is stop or choose a different path.
Services
Architecture Assessment and Refactor
Boundary maps, ADRs, target-state architecture, and a five-outcome scorecard: ship, patch, refactor, rewrite, or retire.
Vibe Tool Expertise
Tech Stack
Problems This Agency Can Fix
AI-generated applications often suffer from unoptimized database queries, excessive re-renders, large bundle sizes, and missing caching. This leads to slow page loads, poor Core Web Vitals, and frustrated users.
AI-generated code often works locally but fails during deployment. Common issues include missing environment variables, incorrect build configurations, incompatible dependencies, and misconfigured hosting platforms.
AI-generated database schemas often lack proper indexes, have no Row Level Security, use inefficient query patterns, and create data integrity problems. These issues worsen as your app grows.
AI-generated codebases frequently have duplicated logic, inconsistent patterns, missing error handling, no TypeScript strict mode, and poor separation of concerns. This makes maintenance and feature additions increasingly difficult.
AI-generated apps often hit walls when traffic or data volume increases. Missing caching, unoptimized queries, no CDN configuration, and monolithic architectures prevent apps from handling real-world load.
Case Studies
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