Engineering & Architecture

Technical Approaches

Behind every verified article lies a sophisticated stack of AI models, graph databases, semantic pipelines, and distributed infrastructure. Here's how we build knowledge at scale.

Core Architecture Stack

A modular, cloud-native architecture designed for high availability, low-latency retrieval, and continuous learning.

🌐 Frontend Layer

React-based SPA with server-side rendering for SEO. Component-driven UI with real-time search indexing and lazy-loaded knowledge graphs.

Next.js 14 TypeScript Tailwind Vercel Edge

⚙️ Backend Services

Microservices architecture handling authentication, content routing, caching, and API orchestration. Event-driven with async message queues.

Go gRPC Kafka Redis

🧠 AI/ML Pipeline

Custom transformer fine-tunes for multilingual NLP, entity resolution, and semantic clustering. Integrated with vector search for contextual retrieval.

PyTorch LangChain Milvus ONNX

📊 Data & Storage

Hybrid storage strategy: document stores for articles, graph databases for relationships, and time-series for analytics and version tracking.

PostgreSQL Neo4j S3/Glacier Timescale

AI & NLP Processing Pipeline

Raw knowledge becomes structured, verified content through a deterministic, multi-stage pipeline optimized for accuracy and traceability.

1. Ingestion & Normalization

Documents, academic papers, and verified sources are parsed, deduplicated, and normalized into a unified JSON-LD schema. Metadata extraction preserves provenance.

Apache Tika · PDFTron · Custom Scraper

2. Entity Extraction & Coreference

Multilingual NER models identify people, places, concepts, and temporal markers. Coreference resolution links pronouns and aliases to canonical entities.

spaCy · Stanza · Custom BERT-finetunes

3. Semantic Embedding & Clustering

Content is vectorized into 1536-d embeddings. Hierarchical clustering groups related concepts, enabling cross-disciplinary knowledge discovery.

SentenceTransformers · FAISS · HDBSCAN

4. Synthesis & Structuring

LLMs draft structured articles following editorial templates. Outputs are constrained via JSON schemas and validated against style guides before human review.

Aevum-Base-70B · Structured Generation · Guardrails

Multi-Tier Verification System

Accuracy isn't optional. Our verification engine combines statistical confidence scoring, source cross-referencing, and expert oversight.

🔍 Source Provenance Check

Every claim is mapped to primary sources. DOI, ISBN, and archived URLs are verified. Paywalled content is cross-checked via institutional partnerships.

⚖️ Contradiction Detection

Logical consistency models flag conflicting statements across articles. Temporal versioning resolves outdated information automatically.

👥 Expert Review Queue

High-impact or newly generated articles enter a randomized expert review pool. Domain specialists validate accuracy before public publication.

📈 Confidence Scoring

Each entry receives a dynamic accuracy score based on source quality, citation count, and historical edit stability. Scores decay without periodic review.

def verify_claim(claim, sources):
# Multi-signal validation pipeline
confidence = calculate_provenance_score(sources)
contradiction = run_logical_consistency(claim)
if confidence > 0.92 and not contradiction:
return {"status": "verified", "score": confidence}
return {"status": "queued_for_review"}

Infrastructure & Scalability

Built for global scale with edge caching, auto-scaling compute, and resilient data replication across regions.

Deployment & Orchestration

  • Container Orchestration
  • CI/CD Pipelines
  • Canary Releases
  • Auto-Scaling Groups
  • Multi-Region Failover

Performance Metrics

  • Avg. API Latency <45ms
  • Search Index Sync Real-time
  • Uptime SLA 99.99%
  • Daily Article Updates ~12,400
  • Vector DB Query Time <12ms

Developer Ecosystem & API

Access the full knowledge graph, search endpoints, and article streaming via our public API. SDKs available for Python, TypeScript, and Go.

# Initialize client & query knowledge graph
from aevum_sdk import Client, QueryBuilder
client = Client(api_key="ak_live_...")
results = client.graph.query(
entity="Quantum Computing",
relations=["related_to", "evolved_from"],
limit=10
).execute()
print(results.confidence_score) # 0.97