Distributed Knowledge Infrastructure

Built on a service-mesh architecture optimized for low-latency semantic search, real-time graph traversal, and horizontally scalable AI inference.

Client & Edge Layer
Web / Mobile SDKsReact, Swift, Kotlin
Global CDNEdge Caching & DDoD Protection
API GatewayAuth, Rate Limiting, Routing
↓ ↕ ↓
Core Services
Semantic SearchHybrid BM25 + Dense Retrieval
Graph EngineProperty Graph + Vector Index
AI InferenceLLM Orchestration & RAG
Auth & IdentityOIDC, RBAC, Zero-Trust
↓ ↕ ↓
Data & Storage Layer
Relational DBPostgreSQL (Metadata & Transactions)
Vector StorePGVector / Milvus (Embeddings)
Object StorageS3-Compatible (Media & Archives)
Cache LayerRedis Cluster (Session & Hot Data)

Core Components

Modular, loosely coupled services designed for independent deployment, observability, and fault isolation.

🔍 Semantic Search Engine

Combines lexical matching with contextual embeddings. Supports multi-language query expansion, synonym resolution, and intent classification for precision recall.

🕸️ Knowledge Graph Processor

Dynamic entity-resolution pipeline that ingests structured/unstructured data, deduplicates entities, and constructs hyperedges representing cross-domain relationships.

🤖 AI Inference Cluster

GPU-optimized serving layer for LLM-based summarization, citation verification, and automated content generation with strict hallucination guardrails.

🛡️ Verification & Audit Service

Automated fact-checking layer that cross-references claims against peer-reviewed sources, tracks editorial provenance, and maintains immutable change logs.

Ingestion & Processing Pipeline

From raw submission to published, verified knowledge — every piece of content passes through a deterministic, auditable workflow.

01

Source Ingestion

Multi-format ingestion (PDF, HTML, JSON-LD, academic APIs) with schema validation and metadata extraction via OCR and NLP parsers.

02

Entity Extraction & Deduplication

Named Entity Recognition (NER) maps concepts to the central ontology. Fuzzy matching and canonical ID assignment prevent fragmentation.

03

AI Verification & Enrichment

LLM-based cross-referencing against trusted corpora. Confidence scoring, citation generation, and bias detection before human review.

04

Graph Construction & Indexing

Entities and relationships are committed to the property graph. Vector embeddings are computed and synced to the search cluster.

05

Publish & Edge Sync

Versioned snapshots are deployed to CDN edge nodes. Incremental updates propagate via event-driven mesh networking.

Design Principles

Event-Driven Architecture

Async message buses (Kafka/Pulsar) decouple services, enabling real-time propagation and auditability.

🧱

Immutable Data Models

Append-only storage ensures reproducibility. Every edit creates a new version with cryptographic lineage.

🌐

Multi-Region Active-Active

Geo-replicated clusters with automatic failover. Read consistency tunable per workload (strong/eventual).

🔐

Zero-Trust Security

mTLS between services, short-lived JWTs, hardware-backed key management, and continuous compliance scanning.

📈

Observable by Default

OpenTelemetry instrumentation across all layers. Distributed tracing, structured logging, and SLO-driven alerts.

♻️

Green Compute Optimization

Model quantization, spot-instance orchestration, and carbon-aware scheduling to minimize inference footprint.

Technology Stack

Open-source first, battle-tested components selected for performance, extensibility, and community support.

Languages

Rust Go Python TypeScript

Frameworks

React / Next.js gRPC FastAPI Kubernetes

Databases

PostgreSQL Milvus Redis Apache Kafka

AI / ML

PyTorch HuggingFace LangChain vLLM

Infrastructure

AWS / GCP Terraform ArgoCD Prometheus

Security

Vault OPA mTLS SBOM

API & Integration

Access the knowledge graph, semantic search, and AI verification endpoints via REST and GraphQL. Rate limits, webhooks, and sandbox environments included.

# Semantic Search Request POST /v1/search/query Content-Type: application/json { "query": "quantum error correction thresholds", "language": "en", "include_graph": true, "verification_level": "peer_reviewed" }
}