🧠 LLM Architecture — ZoRobotics Core Intelligence Stack

The foundation of the ZoRo AI platform is a custom large language model stack built and maintained by ZoRobotics Technologies. This architecture powers all AI Agent behavior, inference sessions, memory sync operations, and Web3-integrated logic across the ZoRo network.

The LLM developed by ZoRobotics is based on the LLaMA model family, fine-tuned and adapted to support encrypted memory retrieval, real-time context propagation, and verifiable inference in a multi-agent environment. Unlike public-facing chat wrappers, this model operates within a persistent ecosystem that includes private user profiles, decentralized training signals, and zk-backed interaction trails.

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🧩 Ecosystem Design Objectives

ZoRobotics is building an end-to-end AI execution layer for Web3 — where agents are not static interfaces but long-lived, composable digital entities that evolve, store memory, and operate under wallet-based identity. The ecosystem integrates on-chain primitives, biometric input, structured knowledge storage, and smart contract coordination to achieve this.

The architecture includes:

1. 🔍 Continuous Knowledge Engine (Vector Database)

A vectorized memory graph operates as a live knowledge base for all agents. It is:

• Continuously trained via chat interactions, agent prompts, and usage context
• Enriched via Web3 signals, on-chain activity, user profile embeddings
• Expanded using external web scanning (based on retrieval-augmented generation, RAG architecture)
• Ingests structured project-level training tasks and multi-modal annotation data

The vector system enables real-time search, contextual re-ranking, and multi-agent memory propagation with semantic relevance scoring. It forms the “long-term memory” substrate of the agent layer.

2. ⚙ Server Infrastructure — Node.js & Nest.js

The backend stack is built with Nest.js and TypeScript, serving as the control and routing layer between the LLM core, Web3 infrastructure, and the user/client interfaces. The responsibilities include:

• Request orchestration: Directs prompts, session calls, and inference jobs to appropriate agent contexts
• Web3 integration: Wallet login, signature-based authorization, identity sync, role gating
• Session logic: Handles encrypted memory writes, profile scoring, and agent ranking
• Subscription logic: Manages tiered access via ZORO token, including 20 ZORO/month base AI access
• External API surface: Exposes endpoints for third-party access, custom workflows, and developer tooling

All interactions from front-end clients (web, Telegram, mobile) pass through this Nest.js backend, which enforces access control and standardizes how the LLM stack responds to context-specific queries.

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🔗 The Role of LLM in ZoRo

The model is deployed not as a monolithic endpoint but as a modular service within a zk-verified, agent-driven AI stack. Every inference is logged, retrievable, and linked to persistent agent memory. Multiple users can operate parallel agents with context-aware personalization, while developers can invoke the same LLM instance with custom data overlays, stream access, or fine-tuned subroutines.

ZoRobotics maintains full control of the LLM pipeline — from fine-tuning cycles and prompt engineering to memory sync strategies and knowledge distillation from on-chain or user-generated input. The architecture ensures alignment between scalable inference, decentralized data input, and Web3-native identity models.

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