Labeled Data for LLMs
Train the next generation of cyber-AI on production-grade labels
GenAI companies building foundation models, fine-tuned agents, or RAG systems can rely on Rankiteo for high-quality, structured cybersecurity data at internet scale. Our datasets power pre-training, fine-tuning, and retrieval for models that understand cyber risk.
100M+ pages, 50K+ incident labels, rich firmographic profiles, and a global supply chain graph, all continuously updated, production-labeled, and ready for your pipeline.
Why Rankiteo data for your models
Generic web scrapes and synthetic labels fall short for cyber risk. Rankiteo datasets are generated by a live production rating engine trusted by insurers and enterprises, giving your models ground-truth signal.
Production-grade labels
Not synthetic or crowd-sourced. Labels come from Rankiteo's live rating engine, the same system used in production by insurers and enterprises.
Temporal depth
Historical snapshots let models learn trends: how ratings change before and after incidents, how patching cadence predicts breaches, and seasonal risk patterns.
Domain-specific structure
Cyber risk is complex. Our labels encode sub-score breakdowns, severity tiers, and sector context that generic datasets can't provide.
Scale & coverage
100M+ pages across every sector and geography. Models trained on Rankiteo data generalize to real-world risk assessment at internet scale.
Ready for fine-tuning
Datasets are available in formats optimized for LLM fine-tuning, RAG pipelines, and embedding models. Parquet, JSONL, or streaming API.
Continuously updated
New labels flow daily as the rating engine processes fresh data. Your models stay current without manual re-labeling.
Available datasets
Each dataset is structured, documented, and available via bulk export or streaming API. Use them independently or join across datasets with Rankiteo company IDs.
Cyber Ratings Dataset
Millions of company-level cyber ratings with granular sub-scores covering network security, patching cadence, DNS health, encryption posture, and more. Each record is timestamped and versioned so models learn temporal patterns.
Incident & Breach Labels
Structured records of cyber incidents tied to affected companies: breach type, vector, severity, sector, and timeline. Perfect for training models to predict breach likelihood or classify incident impact.
Company Risk Profiles
Rich firmographic and risk metadata: industry, geography, employee count, revenue band, technology stack, and Rankiteo risk tier. Models can learn the relationship between business context and cyber posture.
Supply Chain & Vendor Graph
Company-to-vendor dependency mappings with risk propagation labels. Train models on supply chain exposure, concentration risk, and cascading failure scenarios across the global vendor ecosystem.
How GenAI teams use Rankiteo data
Whether you are pre-training a foundation model, fine-tuning a domain agent, or building a RAG pipeline, Rankiteo data fits into your workflow.
Pre-training on cyber risk
Foundation model providers can include Rankiteo datasets in pre-training mixtures so the base model understands cybersecurity concepts, company risk language, and rating semantics from day one.
Fine-tuning underwriting agents
Insurance and fintech teams fine-tune LLMs on Rankiteo ratings + incident data to build agents that triage applications, draft risk memos, and flag high-exposure applicants automatically.
RAG for vendor risk chatbots
Embed Rankiteo company profiles into a vector store and power a retrieval-augmented chatbot that answers natural-language questions about any company's cyber posture in real time.
Frequently asked questions
We support Parquet, JSONL, and streaming via API. Each record includes structured fields, metadata, and a unique company identifier for joining across datasets.
Ready to power your models with production cyber data?
Talk to our data team about licensing, custom exports, and API access. We work with foundation model providers, InsurTech companies, and enterprise AI teams.