From Raw Data to Verified Knowledge
The Aevum AI & NLP Pipeline is a modular, high-throughput processing architecture designed to ingest unstructured text, extract entities, resolve ambiguities, and map relationships to our global knowledge graph. Built on transformer-based language models and hybrid symbolic-neural reasoning, it operates with sub-50ms latency at scale. Every article, citation, and metadata tag passes through this pipeline before publication.
Pipeline Workflow
Ingestion
Multi-source text, PDF, academic papers, structured data
Preprocessing
Normalization, tokenization, language detection
NLP Engine
NER, dependency parsing, sentiment & intent
Knowledge Graph
Entity linking, relation extraction, ontology mapping
Verification
Cross-reference, fact-checking, confidence scoring
Publishing
Markdown/HTML generation, SEO optimization, CDN push
Pipeline Components
Contextual Embeddings v3.2
Dense vector representations trained on 140+ languages. Enables semantic search, paraphrase detection, and cross-lingual retrieval with >94% MTEB benchmark score.
Entity Resolution Engine
Deterministic + probabilistic matching algorithm that disambiguates names, places, and concepts against our curated ontology. Handles polysemy and temporal shifts automatically.
Multi-Modal Reasoning
Integrates text, tables, and structured citations. Uses chain-of-thought prompting with constrained decoding to maintain academic rigor and hallucination resistance.
Real-Time Fact Verification
Claims are cross-referenced against primary sources, peer-reviewed databases, and temporal knowledge bases. Outputs confidence scores and source trails for transparency.
Dynamic Ontology Updater
Continuously expands the knowledge graph schema. New domains, emerging terminology, and cross-disciplinary links are auto-suggested and queued for expert review.
Latency Optimizer
Quantized models, KV-cache optimization, and edge caching reduce median inference time to 42ms. Supports burst scaling during high-traffic publication events.
Performance & Compliance
| Metric | Value | Status |
|---|---|---|
| Model Architecture | Hybrid Transformer + Neuro-Symbolic Graph | Active |
| Parameter Count | 7B (Core) + 13B (Specialized Adapters) | Active |
| Supported Languages | 140+ (High, Medium, Low Resource) | Active |
| Median Latency | 42ms (p99: 118ms) | Active |
| Throughput | ~12,000 requests/sec (Cluster) | Active |
| Fact-Verification Accuracy | 99.84% (Human Eval Set) | Active |
| Compliance | GDPR, SOC2 Type II, ISO 27001, WCAG 2.1 | Active |
API & SDK Usage
Query the pipeline directly via REST or use our official Python/Node.js SDKs. All endpoints support streaming, pagination, and structured JSON-LD output.
import aevum_sdk
client = aevum_sdk.Client(api_key="ae_live_xxxxxxxxx")
# Process raw text through the full pipeline
response = client.nlp.process(
text="The discovery of CRISPR-Cas9 revolutionized genetic engineering...",
pipeline="full",
output_format="json-ld"
)
print(response["entities"])
print(response["knowledge_graph_links"])
print(response["confidence_score"])Ready to Integrate the Pipeline?
Access production-grade NLP, knowledge graph mapping, and verification tools via our public API. Free tier includes 10K requests/month.
Read Documentation → Generate API Key