svadri.ai — Quantitative Intelligence Platform
Capital, Reimagined by AI
svadri.ai fuses machine learning, real-time market data, and quantitative research to surface institutional-grade signals — built for the modern investor.
2.4B+
Data Points Processed Daily
340ms
Avg Signal Latency
99.97%
Model Uptime
18+
Asset Classes Covered
SIMULATEDCore Technology
The Engine Behind the Edge
Every layer of our stack is purpose-built for capital markets — from data ingestion pipelines to the final signal delivery API.
ML Ensemble Engine
Gradient boosting, transformer-based attention models, and reinforcement learning agents trained on decades of market microstructure data.
Sub-Second Signals
Real-time factor scoring with 340ms average latency. Signals are refreshed continuously across equities, crypto, fixed income, and commodities.
Alternative Data Fusion
Satellite imagery, sentiment NLP, options flow, and macro indicators are ingested, normalized, and weighted dynamically by the model.
Graph-Based Correlation
Asset relationships modeled as dynamic knowledge graphs. Contagion risk and cross-asset spillovers are detected before they appear in price.
Backtesting Infrastructure
Walk-forward validation with realistic slippage, market impact, and transaction cost models. No look-ahead bias by design.
Institutional-Grade Security
SOC 2 Type II architecture, end-to-end encryption, and air-gapped model training environments. Your data never leaves your perimeter.
Quantitative Research
Research-Driven Alpha
Our quant team publishes original research on factor dynamics, alternative data, and risk modeling — the same work that powers our live signal engine.
Momentum Decay in High-Frequency Regimes
We document a systematic decay in cross-sectional momentum alpha when intraday volatility exceeds the 90th percentile. Our model adapts factor weights dynamically.
Earnings Call Tone as a Predictive Signal
Using transformer-based sentiment scoring on 12 years of earnings transcripts, we identify a statistically significant 3-day drift following tone shifts in management guidance.
Tail Risk Estimation via Extreme Value Theory
Traditional VaR models underestimate tail risk by 40–60% during regime transitions. We propose an EVT-augmented framework that captures fat-tail dynamics more accurately.
Market Intelligence
Cross-Asset Signal Dashboard
Our models monitor 18+ asset classes simultaneously, surfacing regime shifts and relative-value opportunities in real time.
Risk & Analytics
Risk Is a First-Class Citizen
Alpha without risk management is speculation. Every signal we generate is paired with a real-time risk overlay that monitors exposure, correlation, and tail events.
Portfolio VaR (95%)
Expected Shortfall
Sharpe Ratio
Max Drawdown
Tail Risk Detection
Extreme Value Theory models identify fat-tail events before they materialize in price. Stress scenarios are run continuously against the live portfolio.
Regime Classification
Hidden Markov Models classify the current market regime — trending, mean-reverting, or crisis — and adjust position sizing and factor weights accordingly.
Correlation Monitoring
Dynamic correlation matrices are updated in real time. When cross-asset correlations spike toward 1, the system automatically reduces gross exposure.
Drawdown Controls
Automated circuit breakers halt new signal generation when intraday drawdown thresholds are breached, protecting capital during adverse conditions.
Our Approach
From Raw Data to Actionable Signal
A disciplined, repeatable pipeline that transforms noise into edge — every step auditable, every output explainable.
Data Ingestion
We ingest 2.4B+ data points daily — price feeds, alternative data, macro releases, earnings transcripts, and options flow — normalized and timestamped to microsecond precision.
Feature Engineering
Raw data is transformed into 200+ engineered features spanning momentum, value, quality, sentiment, and macro factors. Features are dynamically weighted by regime.
Model Ensemble
An ensemble of gradient boosting, LSTM, and transformer models vote on signal direction and magnitude. Disagreement between models is itself a risk signal.
Risk Overlay
Every signal passes through a real-time risk filter: VaR limits, correlation checks, and regime-adjusted position sizing before it reaches the output layer.
Signal Delivery
Clean, structured signals are delivered via API with confidence scores, factor attribution, and recommended position sizes — ready to plug into any execution system.
About
Founded on First Principles
Founded by Silicon Valley technologists with a simple goal: combine mathematics, data, and technology with disciplined human judgment to build strategies, manage risk, and create long-term value.
We saw a gap: institutional-quality quantitative intelligence was locked behind multi-million-dollar infrastructure. We built svadri.ai to change that — making research-grade signals accessible to sophisticated investors everywhere.
SVADRI.AIFirst Principles
Challenge assumptions and solve problems from fundamental truths — not convention, consensus, or precedent.
Truth Seeking
Relentlessly pursue objective truth through evidence, rigorous research, and intellectual honesty.
Leadership
Lead with conviction, sound judgment, and the courage to make decisions under uncertainty.
What We Do
We capture uncorrelated, non-linear alpha that's only accessible through our proprietary AI.
Our technology is built to identify and validate causative signals, not merely correlated ones, that predict stock returns.
The distinction matters more than it sounds. Ice cream sales and drowning rates rise together every summer, but neither causes the other — heat does. Most quantitative systems find the ice cream. Our AI is built to find the heat.
Get in Touch
Ready to Find Your True North?
Whether you're an institutional investor, family office, or sophisticated individual — we'd love to show you what quantitative intelligence can do for your portfolio.
< 24 hours
Response Time
2–5 days
Onboarding
Global
Coverage