Industry Solution

AI-powered financial intelligence. Pure .NET.

Build quantitative trading strategies, risk assessment systems, and financial analytics with purpose-built AI models. No Python dependency means faster execution, easier compliance, and seamless integration with existing .NET financial infrastructure.

Challenges we solve

Python dependency in production trading systems

AiDotNet runs natively in .NET with zero Python overhead. No GIL bottleneck, no interop latency, no separate runtime to maintain.

Fragmented ML libraries for financial use cases

FinBERT sentiment, DeepAR forecasting, GARCH volatility, and portfolio optimization all in one unified library.

Model IP protection for proprietary strategies

AES-256-GCM encrypted model weights with three-layer obfuscation. Your alpha stays proprietary.

Regulatory compliance for model deployment

Self-hosted model serving with full audit trail, license management, and on-premises deployment. No data leaves your infrastructure.

Key capabilities

Market Sentiment Analysis

Analyze earnings calls, news articles, SEC filings, and social media for market sentiment signals.

FinBERT FinGPT SecBERT BloombergGPT

Time Series Forecasting

Predict prices, volumes, and financial metrics with state-of-the-art temporal models.

DeepAR Chronos N-BEATS TFT PatchTST Informer

Risk & Portfolio Management

VaR, GARCH volatility, Black-Scholes pricing, and portfolio optimization.

GARCH VaR Black-Scholes Monte Carlo Factor Models

Anomaly & Fraud Detection

Detect fraudulent transactions, market manipulation, and unusual trading patterns.

IsolationForest AutoEncoder DeepSVDD DAGMM TranAD

Document Processing

Extract data from financial documents, statements, and regulatory filings.

LayoutLMv3 Donut PaddleOCR FinBERT NER

Alternative Data Analysis

Process satellite imagery, social media, and unstructured data for alpha signals.

CLIP ResNet BERT Whisper ViT

Typical workflow

1

Data ingestion

Connect to market feeds, news APIs, and internal databases using .NET data connectors.

2

Feature engineering

Build technical indicators, sentiment scores, and alternative data features with AiDotNet preprocessing.

3

Model training

Train FinBERT for sentiment, DeepAR for forecasting, and anomaly detectors on historical data.

4

Strategy backtesting

Backtest trading strategies using trained models against historical market data.

5

Model encryption

Protect proprietary strategies with AES-256-GCM encryption and three-layer obfuscation.

6

Production deployment

Deploy as REST/gRPC endpoints with license management via AiDotNet.Serving.

Financial AI with AiModelBuilder

C# 
using AiDotNet;

// Sentiment analysis with AiModelBuilder
var sentimentModel = await new AiModelBuilder<float, float[], float>()
    .ConfigureModel(new FinBERT<float>())
    .ConfigureOptimizer(new AdamOptimizer<float>())
    .ConfigurePreprocessing()
    .BuildAsync(financialTexts, sentimentLabels);

var sentiment = sentimentModel.Predict(earningsCallText);

// Time series forecasting with AiModelBuilder
var forecastModel = await new AiModelBuilder<float, float[], float>()
    .ConfigureModel(new Chronos<float>("chronos-t5-large"))
    .ConfigurePreprocessing()
    .BuildAsync(historicalPrices, futureValues);

var forecast = forecastModel.Predict(recentPrices);

// Anomaly detection with AiModelBuilder
var anomalyModel = await new AiModelBuilder<float, float[], float>()
    .ConfigureModel(new IsolationForest<float>(
        contamination: 0.01f))
    .BuildAsync(normalTransactions, labels);

var anomalies = anomalyModel.Predict(newTransactions);

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