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Syenah combines Gen-AI, knowledge graphs, and real-time analytics to transform billions of data points into clear, defensible insight.
At Syenah, we reject the myth of the one perfect model. Risk doesn’t live in a single shape, so neither should intelligence.
Our fabric is built on the frontier of large language models like LLaMA-7B, LLaMA-70B, LLaMA-405B, Maverick, and more, each fine-tuned to dominate the task it was designed for.
Together, they form a living, evolving system that outpaces risk in real time.
Models cut through the noise to capture incidents the moment they surface.
No vague buckets, just hard edges, sharp clusters, and intelligence that drives action.
Every incident measured, every risk scored, every signal turned into priority.
Many models, one mission: to deliver faster, sharper, and more defensible risk intelligence.
Many models, one mission: to deliver faster, sharper, and more defensible risk intelligence.
Our risk graph (knowledge graph) maps millions of companies, suppliers, and incidents into a dynamic network. This allows us to uncover hidden dependencies, ripple effects, and systemic vulnerabilities far beyond static reports.
Real-time relationships across 1.5M+ companies and 4M+ business links.
Track how one supplier breach cascades across industries.
Visibility into 2nd, 3rd, and 4th-tier suppliers.
Graph analytics + LLMs enable contextual, fact-driven Q&A.
Syenah’s architecture brings everything together, from ingesting millions of signals to transforming them into context-rich intelligence. Data is clustered, classified, and analyzed with no human bias, stored securely in Germany, and processed in real time.
This foundation powers our risk engine, ensuring that every alert and score is backed by verifiable, explainable AI.
What sets Syenah apart is not just the data we ingest or the AI we run, it is the way we transform signals into defensible, actionable intelligence.
Our methodology fuses cutting-edge AI, graph analytics, and proprietary scoring into a framework trusted by enterprises and regulators alike.
Incidents are not isolated. Our models apply causality and time-decay functions to understand how events escalate, fade, or compound over time.
The result is scores that evolve dynamically, just like risk itself.
We calculate both direct risks (a company’s own incidents) and indirect risks (aggregated scores from suppliers up to tier 4).
This integration ensures that hidden vulnerabilities in the supply chain are reflected in the final risk score.
Every score is explainable. Clients can adjust category weights to align with mandates, while every output links back to verified sources.
Human-benchmarked models with more than 95% accuracy ensure bias-free, defensible intelligence at scale.
Our engine does not just process signals, it adapts in real time. By applying machine learning across millions of historical incidents, it continuously recalibrates thresholds for severity and credibility.
The result is a living system that sharpens with every new data point.
Risk is global, so are we. Our NLP models process over 100 languages, decoding NGO reports, local filings, and regional news.
This ensures no blind spots, whether the signal comes from Frankfurt, Nairobi, or São Paulo, it is captured, classified, and scored with the same precision.
Incidents are not isolated. Our models apply causality and time-decay functions to understand how events escalate, fade, or compound over time. The result is scores that evolve dynamically, just like risk itself.
We calculate both direct risks (a company’s own incidents) and indirect risks (aggregated scores from suppliers up to tier 4). This integration ensures that hidden vulnerabilities in the supply chain are reflected in the final risk score.
Every score is explainable. Clients can adjust category weights to align with mandates, while every output links back to verified sources. Human-benchmarked models with more than 95% accuracy ensure bias-free, defensible intelligence at scale.
Our engine does not just process signals, it adapts in real time. By applying machine learning across millions of historical incidents, it continuously recalibrates thresholds for severity and credibility. The result is a living system that sharpens with every new data point.
Risk is global, so are we. Our NLP models process over 100 languages, decoding NGO reports, local filings, and regional news. This ensures no blind spots, whether the signal comes from Frankfurt, Nairobi, or São Paulo, it is captured, classified, and scored with the same precision.
