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Anticipating systemic risk before it materialises
For governments, financial institutions, and enterprise operators
Platform Overview · Architecture · Intelligence Modules · API Reference · Licensing
Prexus is an AI-driven system designed to predict large-scale risks and outcomes across complex systems such as infrastructure, geopolitics, and institutional decision-making.
Instead of reacting to events, Prexus focuses on anticipating them.
Modern systems are becoming:
- Highly interconnected
- Increasingly unpredictable
- Difficult to manage using traditional models
Governments and institutions today rely on reactive strategies.
Prexus aims to shift this from reaction → prediction.
- Early-stage prototype
- Monte Carlo simulation engine (Python + Rust)
- Scenario-based risk modeling
- Probabilistic outcome forecasting
Input:
- System variables (economic, infrastructure, external risks)
Output:
- Probability of specific events
- Simulation paths across multiple scenarios
- Risk distribution over time
1. Define system variables
2. Run thousands of simulations
3. Analyze probability distributions
4. Generate predictive insights
| Component | Status |
|---|---|
| Core simulation engine | ✅ Functional |
| Prototype | ✅ Completed |
| Meteorium (Climate Risk) | ✅ Live |
| Real-world dataset integration | 🔄 Expanding |
| Healtho (Health Intelligence) | 🔨 In Build |
| Raksha (Threat Intelligence) | 🔨 In Build |
To build a sovereign intelligence layer that enables:
- Governments to predict risks before they occur
- Institutions to make high-stakes decisions with data-backed foresight
- Systems to evolve from reactive → predictive
- Python + FastAPI
- Rust (Monte Carlo simulation core)
- Go (API Gateway)
- Simulation modeling
- Probabilistic analysis
- Improve model accuracy
- Integrate real-world datasets
- Build scalable architecture
Prexus is built as a distributed, polyglot system — each layer uses the best-fit language for its role.
┌─────────────────────────────────────────────────────────────┐
│ CLIENT LAYER │
│ Gov Dashboards · Financial Terminals · Enterprise │
└──────────────────────────┬──────────────────────────────────┘
│ HTTPS / TLS 1.3
┌──────────────────────────▼──────────────────────────────────┐
│ API GATEWAY (Go) │
│ JWT Auth · ABAC · Rate Limiting · CORS · Audit Log │
└───────────────┬─────────────────────────┬───────────────────┘
│ │
┌───────────────▼──────────┐ ┌───────────▼───────────────────┐
│ INTELLIGENCE LAYER │ │ COMPUTE LAYER │
│ (Python) │ │ (Rust) │
│ · Risk Analytics │◄─► · Monte Carlo Engine │
│ · Scenario Models │ │ · VaR / CVaR │
│ · IPCC Pathways │ │ · Numerical Analysis │
└───────────────┬──────────┘ └───────────────────────────────┘
│
┌───────────────▼──────────────────────────────────────────────┐
│ DATA ADAPTER LAYER │
│ Sentinel-1 SAR · ECMWF · Bloomberg · IPCC AR6 │
└───────────────────────────────────────────────────────────────┘
| Layer | Language | Role | Key Capability |
|---|---|---|---|
| API Gateway | Go | Request routing, auth, audit | Zero-trust ABAC, JWT, rate limiting, SHA-256 hash chain |
| Intelligence Engine | Python / FastAPI | Analytics orchestration | Risk scoring, scenario modelling, IPCC pathway integration |
| Compute Acceleration | Rust | High-performance numerics | Monte Carlo simulation, VaR/CVaR, loss distribution |
| Data Adapters | Python | External data ingestion | Climate, environmental, financial signal normalisation |
| Audit Ledger | Go | Immutable event log | SHA-256 hash-chained tamper-evident records |
Prexus is a multi-module intelligence platform. Each module addresses a distinct institutional risk domain. They share a common compute layer, auth infrastructure, and audit ledger.
Status: Live
Meteorium is the environmental intelligence core of Prexus — a dedicated risk computation engine for physical climate exposure analysis. It is the first module in production.
┌─────────────────────────────────────────────────────┐
│ METEORIUM ENGINE │
├──────────────────────────┬──────────────────────────┤
│ Input Parameters │ Intelligence Output │
├──────────────────────────┼──────────────────────────┤
│ · Asset coordinates │ · Composite Risk Score │
│ · Asset valuation │ · VaR 95% │
│ · Prediction horizon │ · CVaR 95% │
│ (180d / 1y / 3y) │ · Expected Loss │
│ · Urban density factor │ · Risk Band │
│ · Climate scenario │ · Audit Receipt │
│ · Insurance coverage │ │
│ · Liquidity shock factor │ │
└──────────────────────────┴──────────────────────────┘
STOCHASTIC SIMULATION CORE
┌───────────────────────────────────────────┐
│ 10,000 Monte Carlo iterations │
│ IPCC AR6 scenario integration │
│ Urban density amplification (λ) │
│ Insurance drag factor (δ) │
│ Liquidity shock multiplier (κ) │
└───────────────────────────────────────────┘
Supported Climate Scenarios
| Scenario | ID | Description | Risk Premium |
|---|---|---|---|
| 🟢 Baseline | baseline | Orderly, Paris-aligned policy | +0% |
| 🟡 Disorderly Transition | disorderly | Delayed policy action, repricing shock | +9% |
| 🔴 Failed Transition | failed | No policy correction, full physical exposure | +16% |
Prediction Horizons
| Tactical | Strategic | Structural |
|---|---|---|
| 180 Days | 1 Year | 3 Years |
| Near-term positioning | Capital planning | Long-run mispricing |
Risk Band Classification
| Score | Band | Indicator |
|---|---|---|
| ≥ 0.85 | CRITICAL | Immediate exposure — intervention required |
| ≥ 0.75 | HIGH | Elevated repricing risk — review urgently |
| ≥ 0.60 | ELEVATED | Material risk — monitor closely |
| ≥ 0.50 | MODERATE | Acceptable range — standard monitoring |
Platform intelligence view. Add asset to see risk visualisation.
Screenshot / demo recording coming soon. The globe renders live climate risk heatmaps, asset pins with severity-graded warning tabs, RCP 8.5 scenario projection (2023–2050), and Meto AI — a 3-model intelligence assistant (Claude / GPT-4o / Gemini) with full portfolio context.
Status: In Build
Healtho applies the Prexus simulation core to population health and bio-systemic risk domains. Designed for national health authorities, pandemic preparedness agencies, and insurance actuaries.
Planned capabilities:
- Epidemic spread modelling across urban networks
- Healthcare system load forecasting under stress scenarios
- Mortality and morbidity risk curves (Monte Carlo)
- Bio-systemic shock propagation across economic sectors
- Integration with WHO datasets and national health registries
Target clearance: Level 3 · Target deployment: National governments, Central health authorities
Status: In Build
Raksha is the geopolitical and institutional threat layer of Prexus. Named for protection, it is designed to give sovereign operators 360-degree situational awareness across physical, cyber, and systemic threat vectors.
Planned capabilities:
- Geopolitical risk scoring with probabilistic conflict modelling
- Critical infrastructure threat surface analysis
- Supply chain disruption forecasting
- Cyber-physical threat correlation engine
- Macro-economic instability early-warning system
Target clearance: Level 5 · Target deployment: National governments, Sovereign wealth funds, Defence ministries
Base URL:https://prexus-intelligence.onrender.com
All protected endpoints require a Bearer JWT issued at registration.
Client Prexus API
│ │
├── POST /api/v1/auth/register ────────────►│
│ { orgName, email, password } │
│◄──────────────── 200 { token, org_id } ───┤ JWT · 15 min · Authorization
│ │ ABAC Clearance assigned
│ │
├── POST /api/v1/meteorium/run ────────────►│
│◄──────────── 200 { risk_score, VaR, ... } ┤ Level 2 clearance required
│ │
GET /health — Liveness probe
// Response 200
{
"status": "operational",
"version": "2.0.0-prx",
"ts": "2025-01-01T00:00:00Z"
}POST /api/v1/auth/register — Provision organisation
// Request body
{
"org_name": "Apex Capital Management",
"email": "operator@apex.com",
"password": "***************",
"org_type": "FINANCIAL",
"tier": "ENTERPRISE"
}
// Response 201
{
"ok": true,
"token": "eyJhR...",
"org_id": "ORG-7f3a9c2d",
"user_id": "USR-1a2b3c4d",
"role": "ORG_ADMIN",
"clearance": 2
}POST /api/v1/meteorium/run — Full Monte Carlo climate risk simulation
Required headers:Authorization: Bearer <token> · Required clearance: Level 2 · Role: ORG_ADMIN
// Request body
{
"horizonDays": 365,
"scenario": "disorderly",
"UrbanDensity": 0.65,
"InsuranceDrag": 0.40,
"LiquidityShock": 0.30,
"AssetValue": 125000000
}
// Response 200
{
"ok": true,
"mission_id": "MIS-8f2a5b9c",
"intelligence_outputs": {
"risk_score": 0.78,
"var_95": 14.2,
"cvar_95": 21.1,
"expected_loss": 24879000,
"risk_band": "HIGH"
},
"simulation_params": {
"iterations": 10000,
"horizon_days": 3600,
"scenario": "disorderly"
}
}POST /risk/asset — Single asset environmental risk (Python intelligence layer)
| Parameter | Type | Description |
|---|---|---|
asset_id | string | Unique asset identifier |
lat | float | Latitude |
lon | float | Longitude |
country_code | string | ISO 3166-1 alpha-2 |
valuation | float | Asset value in USD |
scenario | string | baseline | disorderly | failed |
horizon_days | int | 180 | 365 | 1095 |
POST /risk/portfolio — Portfolio-level aggregated risk
Aggregate risk exposure across multiple assets.
Returns: composite risk score, expected portfolio loss, per-asset breakdown, scenario stress estimates, VaR/CVaR at portfolio level.
| Code | Meaning |
|---|---|
| 200 | Success |
| 201 | Resource created (register) |
| 400 | Malformed request body |
| 401 | Missing or expired JWT |
| 403 | Insufficient clearance / CORS origin blocked |
| 429 | Rate limit exceeded (20 req / 10 s per IP) |
| 500 | Internal server error |
Layer 1 — TLS 1.3 Edge encryption (client layer)
Layer 2 — JWT + ABAC Role-based access, 15 min TTL
Layer 3 — Rate Limiting 50 req/s max, 15 sec TLS autosave
Layer 4 — CORS Policy Origin allowlist per environment
Layer 5 — Audit Ledger All actions logged to tamper-evident chain
Layer 6 — Password Hashing SHA-256 + 100k iterations
| Role | Clearance | Accessible Endpoints |
|---|---|---|
PUBLIC | 0 | /health |
ORG_VIEWER | 1 | /health, /auth/* |
ORG_ADMIN | 2 | All above + /meteorium/run |
SYS_OPERATOR | 3 | All above + admin routes |
SOVEREIGN | 5 | All routes including /raksha/* |
Every authenticated action is recorded in a tamper-evident hash-chained log:
{ action_org, ... } ──► hash: SHA-256 ──► prev_hash
│
Tamper entry 0 ──► all subsequent headers invalidated
| Service | Platform | Role |
|---|---|---|
| Go API Gateway | Render Web Service | Auth, routing, audit, rate limiting |
| Python Intelligence | Render Web Service | Analytics, risk modelling |
| Frontend | Netlify CDN | Static web delivery |
| Secrets | Sentry CDN / Render Env Vars | API keys, JWT secret (auto-generated) |
# Backend — Render# Push prexus-kernel to private GitHub repo → connect to Render# Required environment variables on Render:# ANTHROPIC_API_KEY → Your Claude API key# OPENAI_API_KEY → Your OpenAI API key# GEMINI_API_KEY → Your Gemini API key# JWT_SECRET → Run: openssl rand -base64 32# CORS_ALLOWED_ORIGINS → https://your-app.netlify.app# Frontend — Netlify# Drag and drop /frontend folder to Netlify# Update API_BASE in meteorium.html to point to Render URL# Build
docker build -t prexus-kernel:latest .# Run
docker run -p 8080:8080 \
-e JWT_SECRET=your-secret-here \
-e ANTHROPIC_API_KEY=your-key \
prexus-kernel:latest
# Health check
curl localhost:8080/health| Technology | Role | Version |
|---|---|---|
 | API gateway, middleware, audit ledger | 1.22 |
 | Analytics, risk models, IPCC integration | 3.11+ |
 | Monte Carlo engine, VaR/CVaR numerics | 1.77+ |
 | Structured API endpoints, persistence | 5.10+ |
 | Predictive intelligence dashboard | 18 |
| CesiumJS | 3D globe, geospatial visualisation | 1.114 |
| Three.js | WebGL heatwave overlays | r128 |
| Cloudflare | Managed cloud hosting | — |
| Source | Domain | Cadence | Integration |
|---|---|---|---|
| Sentinel-1 SAR | Physical / Geospatial | 6-day revisit | ESA Open |
| ECMWF | Meteorological | 51-member ensemble | API adapter |
| IPCC-AR6 Database | Climate Scenarios | Built-in | Bundled pathways |
| Terminal Database | Financial Signals | 12 ms lag | API adapter |
✅ v1.4 Meteorium Engine — Physical risk scoring, Monte Carlo,
API Gateway · Meteorium UI with 3D globe
🔄 v1.5 Raksha Module — 360-degree clearance threat intelligence,
geopolitical risk modelling, sovereign operator dashboard
🔄 v1.6 Healtho Module — Population health risk engine,
bio-systemic shock propagation, epidemic modelling
⬜ v1.7 PostgreSQL Persistence — Full asset history, org workspaces,
audit-trail queryable database
⬜ v1.8 Real-time streaming — WebSocket push for live intelligence,
multi-asset portfolio event feeds
⬜ v2.0 Macro-Economic Module — Cross-domain risk correlation,
supply chain intelligence, geospatial signals
| Capability | Status | Module | Clearance |
|---|---|---|---|
| Health / Liveness Probe | ✅ Live | Core | Public |
| Organisation Registration | ✅ Live | Core | Public |
| JWT Authentication + ABAC | ✅ Live | Core | Public |
| Monte Carlo Simulation | ✅ Live | Meteorium | Level 2 |
| VaR 95% / CVaR 95% | ✅ Live | Meteorium | Level 2 |
| Tamper-Evident Audit | ✅ Live | Core | Level 2 |
| 3D Climate Globe | ✅ Live | Meteorium | Level 2 |
| Meto AI (Claude / GPT-4o / Gemini) | ✅ Live | Meteorium | Level 2 |
| Portfolio Aggregation | 🔄 Progress | Meteorium | Level 2 |
| PostgreSQL Persistence | 🔄 Progress | Core | — |
| Raksha Threat Intelligence | 🔨 Planned | Raksha | Level 5 |
| Healtho Risk Engine | 🔨 Planned | Healtho | Level 3 |
| Macro-Economic Module | 🔨 Planned | Macro | Level 3 |
| Geospatial Signals | 🔨 Planned | Geo | Level 4 |
| Supply Chain Intelligence | 🔨 Planned | Supply | Level 3 |
| Real-time WebSocket Feed | 🔨 Planned | Core | Level 2 |
| Sector | Use Case | Key Modules |
|---|---|---|
| National Government | Climate resilience planning, infrastructure stress testing | Raksha, Geo |
| Central Banks | Systemic climate-financial risk, portfolio exposure | Meteorium, Core |
| Asset Managers | Portfolio-level climate VaR, regulatory disclosure (TCFD) | Meteorium |
| Insurance / Reinsurance | Physical risk underwriting, loss modelling | Meteorium |
| Infrastructure Planning | Asset optimisation, multi-scenario planning | Meteorium, Supply |
| Sovereign Wealth Funds | Long-horizon structural risk, geopolitical overlays | All modules |
Prexus Intelligence operates under a dual licensing model that cleanly separates open interface from proprietary intelligence.
The user interface, design system, and frontend components of Prexus are released under the Apache 2.0 License. This includes:
- All files under
/frontend/(HTML, CSS, JavaScript) - UI design tokens, component styles, layout system
- The Meteorium globe interface and dashboard shell
- Index, hub, landing, and demo pages
You may use, modify, and distribute these under standard Apache 2.0 terms.
The backend systems, intelligence pipelines, simulation engines, and data infrastructure are proprietary to Prexus Intelligence and are not licensed for external use, reproduction, or deployment without a signed agreement. This includes:
/backend/— Go API gateway, auth, audit ledger, risk proxy/data-engine/— Python intelligence layers (Layer 0–6), FastAPI endpoints/data-engine/rust/— Monte Carlo simulation engine, VaR/CVaR computation- All risk models, IPCC pathway integrations, scenario calibration logic
- The Prexus intelligence architecture, scoring algorithms, and data fusion methods
Commercial licensing, institutional pilots, and sovereign deployment agreements are available. Contact: dev@prexus.in
See LICENSE (Apache 2.0) and NOTICE (Proprietary terms) for full details.
P R E X U S I N T E L L I G E N C E
Sovereign Predictive Intelligence Infrastructure
For authorised institutional recipients only
© Prexus Intelligence. All rights reserved.