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.
Why Our Architecture
Four pillars define our approach to AI-native risk intelligence.
Predictive Intelligence
Machine learning models for stock, fundamental, and macro data forecasting with proven quantitative methods.
Agent Intelligence
AI agents for sentiment analysis, macro scenario generation, and transforming unstructured data into structured insights.
Explainable Signals
Every signal comes with evidence chains and clear rationale for IC and board review.
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.
Predictive Agents & Ranking
AI-driven price forecasting, risk calculations, and portfolio ranking from investment universe.
Knowledge & Features
Structured feature store with entity resolution and temporal alignment.
Risk Brain
AI/ML models for anomaly detection plus LLM agents for scenario generation and narrative mining.
Quant Core
Factor models, stress engines, and constraints-aware portfolio optimization.
Serving Layer
CustomAPIs, PM/IC apps, and reporting interfaces (governance customized per client).
Platform Cross-Cuts
CustomSecurity and observability spanning all layers (cost controls customized per client).
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.
Data Platform
Unified data infrastructure for comprehensive analytics.
Data Lineage
Full provenance tracking from source to feature
Intelligent Transformation
AI agents convert unstructured data into structured features
Feature Store
Entity-resolved, temporally-aligned features for ML models
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.
Guardrails
- Rate limits and cost caps
- Hallucination detection
- Human escalation triggers
Risk Heartbeat Output
Agents feed a continuous "Risk Heartbeat"—a real-time stream of prioritized signals with confidence scores, entity links, and evidence chains.
Risk Brain
Where narrative signals become quantified, testable hypotheses.
Sentiment Analysis
LLM-powered extraction of market sentiment from news, filings, and social data.
Macro Scenario Generation
AI agents generate macro scenario data for stress testing and forward-looking analysis.
Data Transformation
Transform unstructured data into structured inputs for ML and mathematical models.
Intelligent Data Selection
Agents curate and select relevant data for downstream forecasting and risk models.
Quantitative Analytics
Mathematical models and ML-powered analytics for risk and portfolio optimization.
Predictive Analytics
AI-powered models for stock, fundamental, and macro data prediction.
Risk Calculations
Factor models, stress testing, and tail risk analysis.
Portfolio Optimization
Constraints-aware optimization with risk-adjusted ranking.
Proposal Outputs
Change in Value-at-Risk from proposed action
Expected P&L impact under baseline and stress
Tracking error shift vs. benchmark
Plain-English explanation with evidence links
Serving & Governance
Core platform capabilities with custom governance tailored to your architecture.
Serving and governance layers are customized per client to integrate with your existing architecture, compliance requirements, and reporting workflows.
API Layer
RESTful endpoints for integration with your existing systems.
Dashboard Apps
Purpose-built interfaces for portfolio managers and analysts.
Reporting
Customizable reports for IC decks and board presentations.
Explainability
Clear rationale with evidence links—designed for IC decks, board presentations, and regulator inquiries. Every number traces back to source.
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
Model Risk Management & Explainability
Built for validation teams and regulators—not just data scientists.
Model Registry
Centralized catalog with versioning and metadata
Challenger Models
Automated comparison against baseline and alternative approaches
Drift Monitors
Statistical tests for input drift, concept drift, and performance decay
Validation Cadence
Scheduled validation cycles with automated reporting
Documentation Packs
MRM-ready documentation for auditors and regulators
Deployment Options
Choose the deployment model that fits your requirements.
Managed SaaS
Multi-tenant cloud, fastest time-to-value.
- No infrastructure management
- Automatic updates
- Shared compute
Managed Private VPC
Dedicated infrastructure in your cloud region with customer-managed keys.
- Data isolation
- Customer-managed keys
- Custom network policies
On-Prem / Sovereign
Full deployment in your data center or sovereign cloud for maximum control.
- Air-gapped option
- Regulatory compliance
- Full data sovereignty
Integration & Extensibility
Connect to your ecosystem—or extend ours.
Data Feeds
- Market data (Bloomberg, Refinitiv)
- Alternative data providers
- Internal data lakes
- Custodian/admin files
Trading Systems
- OMS/EMS integration
- FIX protocol support
- Pre-trade analytics
- Post-trade reconciliation
APIs & SDKs
- REST and GraphQL APIs
- Python/R SDKs
- Webhook notifications
- Streaming WebSocket feeds
Custom Modules
- Plug-in architecture
- Custom agent development
- White-label options
- Custom reporting
Cost & Efficiency Controls
Custom optimization strategies tailored to each client's infrastructure.
Cost controls are designed and implemented based on each client's unique architecture, usage patterns, and infrastructure requirements.
Two-Tier Agents
Fast/cheap scouts for triage; deep agents for validated signals only.
RAG Grounding
Retrieval-first architecture minimizes expensive generative calls.
Intelligent Caching
Feature and inference caching reduces redundant computation.
Reserved vs. Burst
Baseline reserved compute with auto-scaling burst for peak loads.
Technical FAQs
Ready to See the Stack in Action?
Schedule a technical deep-dive with our engineering team.