ML & AI-Powered Risk:
From Data to Decision
Machine learning models for prediction, LLM agents for data enrichment, and quantitative engines for risk analysis—delivering actionable insights with explainability.
Architecture
Four pillars define our approach to AI-native risk intelligence.
Predictive Intelligence
ML models for stock, fundamental, and macro data forecasting with proven quantitative methods.
Agent Intelligence
AI agents for sentiment, macro scenarios, and unstructured data transformation.
Explainable Signals
Every signal comes with evidence chains and clear rationale.
Governance by Default
Audit trails, version control, and approval gates built into every layer.
Six integrated layers from data ingestion to portfolio action.
Predictive Agents & Ranking
AI-driven price forecasting and portfolio ranking
Knowledge & Features
Structured feature store with entity resolution
Risk Brain
AI/ML models for anomaly detection + LLM agents
Quant Core
Factor models, stress engines, portfolio optimisation
Serving Layer
APIs, PM/IC apps, and reporting interfaces
Platform Cross-Cuts
Security and observability spanning all layers
The Narrative → Math Bridge
Unstructured signals from agents flow into the Risk Brain, which extracts quantified risk metrics. These feed directly into the Quant Core for stress scenarios and optimization—ensuring every narrative insight has a clear P&L translation.
"Narrative signals flow into quantified risk metrics. Risk metrics flow into stress scenarios. Stress scenarios flow into actionable proposals—with evidence you can audit at every step."
The Stack — From Data to Decision
Four integrated layers power the full pipeline from raw data to actionable proposals.
Data Lineage
Full provenance tracking from source to feature. Every data point traces back to its origin with transformation history.
Intelligent Transformation
AI agents convert unstructured data—filings, transcripts, news—into structured features for ML models.
Feature Store
Entity-resolved, temporally-aligned feature store. No look-ahead bias, no data leakage, no surprises.
Signal → Risk → Action
"Issuer Scout detected management change + earnings miss at [Entity X]. Confidence: 0.87"
Pipeline
Risk Brain → Factor Decomposition → Quant Core
- Reduce position by 30%
- Hedge via CDS (-$2.1M notional)
- Expected ΔVaR: -18%
- 4 evidence links attached
Technical FAQs
Ready to See the Stack in Action?
Schedule a technical deep-dive with our engineering team.