ConceptGame · Engineering Studio
I Build Scalable Systems, AI Platforms, and Developer Tools
Solutions Architect focused on MongoDB, AI pipelines, and cloud-native systems across AWS, GCP, and Azure — from freemium products to distributed trading and LLM routing platforms.
I design production architectures that stay operable under real load: clear boundaries, measurable reliability, and tooling teams can actually ship with.
Dario Maddaluno
Solutions Architect · Founder
I build and operate production AI platforms and cloud-native systems end to end — freemium products with Stripe entitlements, multi-strategy trading stacks, and LLM routers with live budgets. The through-line is reliable architecture, fast iteration, and systems that stay understandable as they scale.
Live Products
Production systems in active use — each with real billing, data, or routing responsibilities, not demos.
Systems & Architecture Work
Alongside live products, these platforms are in active engineering — focused on automation depth, AI explainability, and multi-service operations.
Pro League Project
End-to-end sports betting automation: ingest market data, evaluate opportunities against budget rules, and execute on a schedule with operator-visible controls.
Open system →DCRE
Multi-model video pipeline that classifies frames across 22 safety categories with explainability, targeting ~0.5–1s per frame for multi-user workloads.
Posting Manager Platform
Social publishing architecture spanning scheduling, media management, and analytics — designed for multi-user scale rather than a single-account tool.
How I Build Systems
A practical engineering mindset for products that have to run in production — not just look impressive in a pitch deck.
Systems over features
I design durable boundaries and data flows first, then ship features inside that architecture.
Scale and real-time by default
Workloads that matter usually need shared caches, queues, or schedulers — not one-off scripts.
Complexity only when earned
No RAG, microservices, or exotic stacks unless the problem clearly pays for them.
Velocity without chaos
Clear modules, typed contracts, and operable dashboards so teams can move fast safely.
AI as an accelerator
I use AI to speed prototyping and boilerplate — architecture and validation stay engineered.
AI-Assisted Development Workflow
AI is part of how I ship — tightly scoped to productivity, never a substitute for system design.
Accelerate the boring path
AI speeds scaffolding, refactors, and exploratory prototypes so more time goes to hard design choices.
Keep architecture human-owned
System boundaries, data models, and failure modes are designed and reviewed manually before they ship.
Assist, don’t decide
Models help with options and drafts — production decisions stay with engineering judgment and tests.
Optimize for operable software
Correctness, observability, and maintainability beat clever one-offs every time.
Tech Stack
Tools chosen for delivery speed and operational clarity — not fashion.
Backend
Node.js · Python · FastAPI
APIs, workers, and long-running services.
Frontend
React · Next.js · TypeScript
Product UIs and operator dashboards.
Data
MongoDB · MySQL
Document and relational state for product and ops.
AI
PyTorch · Transformers · OpenCV
Models, pipelines, and vision workloads.
Cloud
AWS · GCP · Azure
Multi-cloud delivery when the problem needs it.
DevOps
Docker · PM2
Containers and process orchestration in production.