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Recommended Technical Architecture
1. Recommended Technical Architecture
The architecture is built on a Backend for Frontend (BFF) pattern to centralise business logic and eliminate the "quadruplex implementation" risks inherent in native development.
Frontend Shell: Flutter (Dart) using the Skia/Impeller rendering engine. This is preferred for its pixel-perfect UI control and deep integration with native accessibility APIs (iOS VoiceOver/Android TalkBack) over a 5-year maintenance cycle.
AI Orchestration Tier:
Agent-to-Agent (A2A) Protocol v1.2: Facilitates secure, cross-boundary negotiation handshakes between the user's mobile agent and regional organisation agents using lightweight JSON-RPC schemas.
Model Context Protocol (MCP): Standardises how the "Rietaviškis" agent dynamically discovers and invokes municipal tools and database resources without exposing direct database links.
Privacy-First Edge AI: Google AI Edge (LiteRT) runtime deployed on-device. This allows quantised models (e.g., Gemini Nano) to process sensitive user data—such as disability markers or NEET status—locally to satisfy GDPR/BDAR Article 25 (Privacy by Design).
Data Strategy:
Hybrid Connectivity: Utilises Zero-Copy Data Federation (e.g., Snowflake or Databricks) to query large-scale municipal datasets in place, avoiding the cost and latency of traditional ETL pipelines.
Local-First Ledger: A local, sandboxed SQLite database (encrypted via 256-bit AES) stores sensitive profiling parameters and a durable log of pending changes for offline-first functionality.
Quality Assurance: A centralised server-side integration testing suite (1,000+ tests) replaces fragmented client-side testing to maintain system integrity during parallel coexistence of old and new features.
2. Recommended UX Assets
The UX assets are grounded in Universal Design principles to bridge the digital divide for seniors and those with low digital literacy.
The AI Concierge ("Rietaviškis"): A 24/7 conversational assistant providing support in Lithuanian. It uses a voice-to-text semantic interface to guide users through complex municipal service applications.
Accessibility Anchors:
Multi-modal Validation: Alternatives to QR scanning for event check-ins, such as Bluetooth Low Energy (BLE) beacons or audio-watermark checks, to assist users with motor-control challenges.
Strict UI Constraints: Touch targets of at least 48×48 logical pixels and layouts that remain stable even under 200% system-level text scaling.
Gamification & Economic Visuals:
Point Matrix UI: Real-time visual feedback (badges and progress charts) showing "Credits" earned through verified civic participation.
Career Tiers: Visual progression markers for global career tiers: Explorer → Builder → Verified.
"Atvirumo" (Openness) Leaderboard: A transparent public dashboard that ranks local organisations based on their inclusivity and volume of verified interactions.
System Status Indicators: Header-based Online/Offline indicators that provide reassurance to users when data is being edited locally.
3. Why This Approach? (Justification)
Time to Market (TTM) Optimisation: The BFF architecture reduces the labour effort for functional changes by four times, accelerating TTM from 4–5 weeks to just 1–1.5 weeks.
Operational Stability: Shifting to centralised server-side testing is projected to reduce critical production incidents by 60% and save millions in operational expenditures annually.
Regulatory Compliance: Using Edge AI ensures that sensitive vulnerability markers never leave the user's handset, which is a non-negotiable requirement for legal and security certification under GDPR.
Engagement Velocity: The PoP module targets a Verification Velocity of < 30 seconds from physical scan to secure ledger update, ensuring the system functions efficiently even with poor municipal Wi-Fi.
Economic Impact: Connecting local points directly to the WTA Flywheel provides immediate value to NEET youth, giving them zero-cost access to global economic pathways and remote training portfolios


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