The AI-Native Risk Stack: From Raw Signal to Portfolio Action
Our platform fuses autonomous data agents, explainable AI, and institutional quant engines—so your team sees weak signals early, tests them as scenarios, and acts with governance.
Why Our Architecture
Four pillars define our approach to AI-native risk intelligence.
Data Autonomy
Self-governing agents ingest, validate, and enrich data 24/7 without manual intervention.
Explainable AI
Every signal comes with evidence chains and plain-English rationale for IC and board review.
Quant Fusion
Narrative insights translate directly into factor exposures, stress paths, and optimization constraints.
Governance by Default
Audit trails, version control, and approval gates built into every layer—not bolted on.
System Overview
Six integrated layers from data ingestion to portfolio action.
Agents
Autonomous data scouts for issuers, microstructure, and regulatory signals.
Knowledge & Features
Structured feature store with entity resolution and temporal alignment.
Risk Brain
AI/ML models for anomaly detection, scenario generation, and narrative mining.
Quant Core
Factor models, stress engines, and constraints-aware portfolio optimization.
Serving & Governance
Proposal APIs, PM/IC apps, audit ledger, and board-mode reporting.
Platform Cross-Cuts
Security, observability, and cost controls 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.
Agents & Data Autonomy
Autonomous scouts that never sleep—with guardrails that keep them safe.
Issuer Scout
Monitors filings, transcripts, news, and alternative data for name-specific signals.
Microstructure Watcher
Tracks liquidity, order flow anomalies, and market-impact indicators.
Regulatory Crawler
Ingests rule changes, enforcement actions, and policy signals across jurisdictions.
Risk Brain (AI/ML)
Where narrative signals become quantified, testable hypotheses.
Narrative Mining with RAG
Retrieval-augmented generation extracts entities, events, and sentiment from unstructured text.
Anomaly & Change-Point Detection
Statistical and ML models flag regime shifts, outliers, and early-warning patterns.
Scenario Generator
Auto-constructs stress scenarios from narrative signals with P&L translation.
Calibration & Evaluation
Continuous backtesting, precision/recall tracking, and model versioning.
Security & Compliance
Enterprise-grade security with institutional compliance controls.
SSO / MFA
SAML 2.0, OIDC, and hardware token support
RBAC / ABAC
Role and attribute-based access with least-privilege defaults
Encryption
AES-256 at rest, TLS 1.3 in transit, HSM key management
Data Residency
Region-specific deployments with geo-fencing
Private VPC
Dedicated infrastructure with customer-managed keys
SIEM Hooks
Real-time security event streaming to your SOC
Technical FAQs
No. Our platform is designed as an overlay. It integrates with your current OMS, EMS, and risk platforms—adding idiosyncratic detection, proposals, and explainability without requiring a rip-and-replace.
We combine narrative signals with quantitative validation, and tune precision/recall thresholds during pilot. Most clients see false-positive rates below 15% within the first quarter.
We offer region-specific deployments across major cloud regions, Private VPC with customer-managed keys, and full on-prem deployment for sovereign or air-gapped requirements.
Every decision and action is logged with full lineage. We provide MRM documentation packs, SOC 2 reports, penetration test summaries, and custom audit extracts on request.
Ready to See the Stack in Action?
Schedule a technical deep-dive with our engineering team.