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

# How Allyy works

> The end-to-end journey from raw supporter data to optimised decisions

Allyy turns your supporter history into decisions. The platform follows the same five stages every time, whatever the business question:

<Steps>
  <Step title="Connect your data" icon="database">
    You connect one or more **data sources** (your CRM, warehouse, files, or a streaming feed) inside a **dataset**, and map them to Allyy's data model. See [Connect a data source](/processes/data).
  </Step>

  <Step title="Build a model" icon="brain">
    Allyy trains a **model** on your history to answer a specific question — *will this person donate?*, *will they lapse?*, *how much should we ask for?* See [What Allyy can predict](/models/what-allyy-can-predict).
  </Step>

  <Step title="Generate predictions" icon="wand-magic-sparkles">
    The trained model scores your supporters, producing a **prediction** (a probability or an amount) for each one. See [Generating predictions](/predictions/generating-predictions).
  </Step>

  <Step title="Make a decision" icon="bullseye">
    Allyy combines those scores — for example propensity × expected amount — and **optimises** them into a ranked, budget-aware contact list. See [From predictions to decisions](/predictions/decisions).
  </Step>

  <Step title="Act, then measure" icon="chart-line">
    You **export** the decision to your campaign tools, run the campaign, and use the **dashboards** to compare what actually happened against what the model expected.
  </Step>
</Steps>

## The pieces, and how they fit

Everything in Allyy is one of a small number of objects. If you understand these, you understand the platform:

| Object          | What it is                                                                                      |
| --------------- | ----------------------------------------------------------------------------------------------- |
| **Dataset**     | A project-level container holding the data sources for one body of work.                        |
| **Data source** | A connection to your data (BigQuery, SQL Server, CSV, SFTP, Salesforce, …), batch or streaming. |
| **Model**       | A trained predictor for one question (propensity, churn, expected amount, lifetime value, …).   |
| **Prediction**  | The scores a model produces for a population at a point in time.                                |
| **Decision**    | Scores combined and optimised into a ranked action list for a campaign.                         |
| **Export**      | A decision pushed out to your tools (or pulled via API).                                        |
| **Workflow**    | Automation that runs syncs, training, scoring and exports on a schedule.                        |

<Note>
  The **Monitoring** page tracks all of these in one timeline — Datasets, Models, Predictions, Exports and Workflows — so you can see at a glance what ran, when, and whether it succeeded. See [Monitoring & logs](/processes/monitoring_dashboard).
</Note>

## Descriptive vs predictive

Allyy gives you two complementary lenses:

<CardGroup cols={2}>
  <Card title="Predictive" icon="brain">
    Models, predictions and decisions look **forward** — who to contact next and how.
  </Card>

  <Card title="Descriptive" icon="chart-line">
    The [Analytics dashboards](/analytics/overview) look **backward** — what your supporter base looks like today and how it's trending, no modelling required.
  </Card>
</CardGroup>

The two are complementary: the descriptive analytics show what your file looks like today, and the predictive models act on it. You can use either or both.