> ## Documentation Index
> Fetch the complete documentation index at: https://docs.synthefy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Welcome to Synthefy

> The foundational platform for tabular regression and time series forecasting

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  image="/images/hero-light.png"
  darkImage="/images/hero-dark.png"
  primaryAction={{
text: 'Get Started',
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Synthefy provides foundation models for tabular regression (**Nori**) and time series forecasting. The core objective is to deliver accurate predictions across structured data — from tabular datasets to multivariate time series with exogenous context — to support decision automation across domains like retail, finance, energy, and healthcare.

Why this matters

* **Retail**: Forecast demand by combining sales with promotions, weather, events, and traffic. Test “what-if” scenarios for price or campaign changes.
* **Telecom**: Plan capacity by simulating cell demand spikes around events and stress-testing network algorithms.
* **Healthcare**: Augment ECG/PPG datasets and stress-test classifiers using synthetic signals conditioned on patient metadata.
* **Finance/Energy**: Run counterfactuals for volatility, demand, and dispatch planning when historical data is sparse.

## Key Capabilities

<AccordionGroup>
  <Accordion title="Context-based Forecasting" icon="chart-line">
    Use multivariate histories combined with external signals (text, tabular, other time series) to improve predictions. Example: Forecast weekly SKU demand by combining sales history with promotions, weather, and local events.
  </Accordion>

  <Accordion title="Scenario Simulation" icon="workflow">
    Ask counterfactual questions and generate trajectories under specified constraints or drivers. Example: Simulate revenue and inventory impact if the price increases by 5% and a holiday event shifts foot traffic by 20%.
  </Accordion>

  <Accordion title="Anomaly Detection" icon="alert-triangle">
    Detect deviations by modeling normal behavior with context and surfacing residual patterns. Example: Flag a network KPI spike when traffic and config changes do not explain the deviation.
  </Accordion>
</AccordionGroup>

## Products

<CardGroup cols={2}>
  <Card title="Synthefy Nori (Tabular)" icon="table" href="/nori/quickstart">
    Nori is our tabular foundation model. Predict on any structured dataset with no training required.
  </Card>

  <Card title="Time Series Forecasting" icon="chart-line" href="/sdk/overview">
    Forecast with multivariate context using the Python SDK and API.
  </Card>
</CardGroup>

## Enterprise Readiness

<AccordionGroup>
  <Accordion title="Production Ready" icon="rocket">
    No training required: pre-trained models work out of the box for common tasks; fine-tuning optional.
  </Accordion>

  <Accordion title="Easy Integration" icon="wrench">
    Simple APIs, comprehensive SDKs, and Docker containers for any deployment scenario.
  </Accordion>

  <Accordion title="Deployment">
    Deployment: on-prem or private cloud today; AWS Marketplace listing (Coming Soon).
  </Accordion>
</AccordionGroup>

## Getting Help

<CardGroup cols={2}>
  <Card title="Documentation" icon="book" href="/sdk/quickstart">
    Comprehensive guides and tutorials to get you started
  </Card>

  <Card title="SDK Overview" icon="code" href="/sdk/overview">
    Learn how to use the Synthefy SDK with examples
  </Card>

  <Card title="Support" icon="life-ring" href="mailto:support@synthefy.com">
    Get help from our support team
  </Card>
</CardGroup>