Technology

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.

L1

Predictive Agents & Ranking

AI-driven price forecasting, risk calculations, and portfolio ranking from investment universe.

L2

Knowledge & Features

Structured feature store with entity resolution and temporal alignment.

L3

Risk Brain

AI/ML models for anomaly detection plus LLM agents for scenario generation and narrative mining.

L4

Quant Core

Factor models, stress engines, and constraints-aware portfolio optimization.

L5

Serving Layer

Custom

APIs, PM/IC apps, and reporting interfaces (governance customized per client).

L6

Platform Cross-Cuts

Custom

Security 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

ΔVaR

Change in Value-at-Risk from proposed action

ΔP&L

Expected P&L impact under baseline and stress

ΔTE

Tracking error shift vs. benchmark

Rationale

Plain-English explanation with evidence links

Serving & Governance

Core platform capabilities with custom governance tailored to your architecture.

Custom Implementation

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.

Custom Implementation

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

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, 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.