KALKI-1.5

KALKI AI — System Architecture Blueprint

Executive Overview

KALKI AI (Krishna Artificial Lattice Keystone Intelligence) is designed as an Intelligence Operating System (IOS). It unifies large language models (LLMs), vision-language models (VLMs), small language models (SLMs), mixture-of-experts (MoE) routing, autonomous multi-agent orchestration, retrieval-augmented generation (RAG), and defensive cybersecurity into a single, high-throughput, low-latency framework operating across Cloud, Edge, Mobile, Desktop, Smartwatch, and IoT devices.

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High-Level System Architecture Diagram

graph TD
    subgraph Layer 1: User Interface Layer
        UI_Web["Web App (Next.js/React)"]
        UI_Mob["Mobile App (Flutter)"]
        UI_Desk["Desktop App (Tauri/Electron)"]
        UI_IoT["Smartwatch & IoT Nodes"]
        UI_API["Third-Party API Gateway"]
    end

    subgraph Layer 6: Security & Governance Layer (Perimeter & In-Line)
        SEC_Auth["OAuth2 / MFA / JWT"]
        SEC_RBAC["Role-Based Access Control"]
        SEC_Enc["TLS 1.3 & AES-256 E2EE"]
        SEC_Guard["AI Safety & Defense Guardrails"]
    end

    subgraph Layer 2: Multimodal Perception Layer
        PERC_Text["Text & Structured Doc Parser"]
        PERC_Vision["OCR & Scene Understanding (VLM)"]
        PERC_Audio["Speech-to-Text / Audio Features"]
        PERC_Sensor["IoT & Sensor Stream Ingestion"]
    end

    subgraph Layer 4: Agent Orchestration Layer
        AGENT_Planner["Planner Agent"]
        AGENT_Research["Research Agent"]
        AGENT_Memory["Memory Agent"]
        AGENT_Executor["Executor Agent"]
        AGENT_Validator["Validator Agent"]
        AGENT_Security["Security Agent"]
        MCP_Bus["MCP & A2A Protocol Router"]
    end

    subgraph Layer 3: Reasoning & Model Layer
        MOE["MoE Router (Mixture of Experts)"]
        LLM["High-Reasoning LLM Cluster"]
        SLM["Edge Quantized SLMs (ONNX/GGML)"]
        LCM["Low-Latency Conversational Model"]
        VLM["Vision-Language Model Engine"]
    end

    subgraph Layer 5: Knowledge & RAG Layer
        RAG_Hybrid["Hybrid Search (Dense + Sparse BM25)"]
        RAG_VecDB["Vector Store (Qdrant / FAISS)"]
        RAG_KG["Knowledge Graph (Neo4j / RDF)"]
        RAG_Rerank["Cross-Encoder Re-Ranker"]
        MEM_Hier["Hierarchical Memory Store"]
    end

    subgraph Layer 7: Infrastructure Layer
        INFRA_K8s["Kubernetes Cloud Cluster"]
        INFRA_Edge["Edge Runtime (TFLite / ExecuTorch)"]
        INFRA_Obs["Prometheus / Grafana / Jaeger"]
    end

    UI_Web --> SEC_Auth
    UI_Mob --> SEC_Auth
    UI_Desk --> SEC_Auth
    UI_IoT --> SEC_Auth
    UI_API --> SEC_Auth

    SEC_Auth --> SEC_Guard
    SEC_Guard --> PERC_Text
    SEC_Guard --> PERC_Vision
    SEC_Guard --> PERC_Audio
    SEC_Guard --> PERC_Sensor

    PERC_Text --> MCP_Bus
    PERC_Vision --> MCP_Bus
    PERC_Audio --> MCP_Bus
    PERC_Sensor --> MCP_Bus

    MCP_Bus <--> AGENT_Planner
    AGENT_Planner <--> AGENT_Research
    AGENT_Planner <--> AGENT_Memory
    AGENT_Planner <--> AGENT_Executor
    AGENT_Planner <--> AGENT_Validator
    AGENT_Planner <--> AGENT_Security

    AGENT_Executor <--> MOE
    MOE --> LLM
    MOE --> SLM
    MOE --> LCM
    MOE --> VLM

    AGENT_Research <--> RAG_Hybrid
    RAG_Hybrid --> RAG_VecDB
    RAG_Hybrid --> RAG_KG
    RAG_Hybrid --> RAG_Rerank

    AGENT_Memory <--> MEM_Hier

    MOE --> INFRA_K8s
    SLM --> INFRA_Edge
    INFRA_K8s --> INFRA_Obs

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Detailed Layer Breakdown

Layer 1: User Interface Layer

• Web Application: Next.js 14 App Router, React 18, Tailwind CSS, WebSockets streaming.
• Mobile Application: Flutter cross-platform mobile client supporting iOS, Android, and WearOS.
• Desktop Application: Tauri (Rust + Web frontend) for minimal footprint and native hardware integration.
• Voice & IoT Assistants: Micro-client SDK providing push-to-talk, wake-word detection, and MQTT telemetry.
• API Gateway: REST & gRPC endpoints protected by OAuth2, rate limiting, and mTLS.

Layer 2: Multimodal Perception Layer

• Text & PDF Processing: PyMuPDF + Tesseract OCR fallback for scanned multi-page documents.
• Vision Perception: CLIP / Qwen2-VL embeddings for object classification, visual Q&A, and chart analysis.
• Audio Perception: Whisper Large v3 for multilingual speech recognition; Bark / Coqui for low-latency TTS.
• Sensor Ingestion: Real-time event streams (JSON/Protobuf over WebSockets & MQTT) normalized into structured state matrices.

Layer 3: Reasoning & Model Layer

• MoE Router: Dynamic routing based on prompt complexity, required context size, and SLA requirements.
• LLM Cluster: Llama-3 70B / Claude-3.5 Sonnet / Mixtral-8x22B for complex multi-step reasoning.
• SLMs for Edge: Phi-3 Mini (3.8B), Llama-3.2 1B/3B quantized to INT4 via llama.cpp / ONNX runtime for offline edge deployment.
• LCMs (Low-Latency Conversational Models): Speculative decoding and KV-cache optimization yielding <150ms time-to-first-token (TTFT).

Layer 4: Agent Orchestration Layer

• Planner Agent: ReAct + Tree-of-Thought decomposition into graph execution DAGs.
• Research Agent: Deep web search, RAG querying, and document synthesis.
• Memory Agent: Context resolution, memory index retrieval, and dynamic prompt injection.
• Executor Agent: MCP tool execution, sandboxed code execution (Docker/Wasm), and API calling.
• Validator Agent: Output assertion, hallucination detection, and schema verification.
• Security Agent: Defensive threat monitoring, prompt injection detection, and compliance authorization.

Layer 5: Knowledge & RAG Layer

• Hybrid Vector & Keyword Search: Reciprocal Rank Fusion (RRF) joining Qdrant dense vector search (Cosine distance) and BM25 sparse keyword indexing.
• Re-Ranker: Cross-Encoder (bge-reranker-large) scoring top 50 candidates down to top 5 context windows.
• Knowledge Graph: Entity-Relation extraction storing semantic triples in Neo4j for multi-hop graph reasoning.

Layer 6: Security & Governance Layer

• Zero Trust Model: Mandatory JWT validation per RPC, role-based capability boundaries.
• Data Encryption: TLS 1.3 in-transit, AES-256-GCM at-rest for all memory vectors and user documents.
• AI Safety Guardrails: Llama-Guard 3 + custom rule filter preventing unsafe code generation or privilege escalation attempts.

Layer 7: Infrastructure Layer

• Cloud Cluster: Kubernetes (EKS/GKE) with Horizontal Pod Autoscaler (HPA) and Nvidia Kube-Ray GPU management.
• Edge Deployment: Cross-compiled binaries for ARM64 / Apple Silicon / Android NDK with local vector index storage (SQLite-vec).

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Latency Budget Allocation (<500ms Target)

Stage	Component	Latency Budget
1	API Gateway & Security Auth	15ms
2	Perception & Audio/Text Tokenization	35ms
3	Memory & RAG Retrieval	180ms
4	MoE Routing & Agent Planner	40ms
5	LLM Speculative Token Generation (TTFT)	180ms
6	Security Audit & Guardrail Verification	30ms
Total	End-to-End Latency Target	480ms

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