See the threat before it moves.
Rinjani correlates indicators, vulnerabilities, and adversary activity across every feed — using multi-modal LLMs and Retrieval-Augmented Generation to keep your ground truth current, around the clock.
Establish a ground truth platform with augmented generative learning.
Large language models lose data freshness fast. Retrieval augmentation pulls relevant intelligence from external knowledge bases in real time — so every assessment reflects current events, not last quarter's training set.
- Built-in scalable open-source toolsHugging Face, PyTorch, RAG, and LLMs integrate directly into your workflow.
- Open-source integrationsConnect open intelligence sources for comprehensive, correlated coverage.
- Open API specificationsAnalysts, integrators, and developers wire Rinjani into any existing stack.
A comprehensive platform for threat intelligence
Tailored threat intelligence, modeling, and risk assessment — customized for specific industry needs with multi-modal, AI-driven analysis.
Unbiased algorithms
Predictive threat analysis without bias — fair, accurate assessment every time.
Self-seeding integration
Incorporates diverse data formats for full insight while honoring IT security, governance, and policy.
Advanced search
Track the threat landscape across sources and formats from a single query — simple, efficient navigation.
Adaptive learning
Real-time adaptation to detect external, internal, domestic, supply-chain, and emerging threats.
Multi-modal, AI-driven threat analysis
Establish a ground-truth platform with augmented generative learning across text, code, network, and binary signals.
LLM & RAG
Large language models plus retrieval augmentation — a continuously self-augmenting analysis engine.