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

# Introduction

> Important predictions start here

```python theme={null}
from hollerith import Hollerith

clf = Hollerith()
clf.fit(train, target="risk_label")
preds = clf.predict(test)
```

That is the whole call. There is no training step, nothing to tune, and no pipeline to
maintain.

## What Hollerith is

Hollerith is a transformer pretrained to predict directly from tables, trained and served by
Monarcha. It reads your labeled table at prediction time rather than learning from it.

`fit` computes no gradients. It hands the model your table, and `predict` scores new rows
against it.

The name is Herman Hollerith, whose punch card carried the 1890 census and made the first
machine-readable table.

## What that gets you

* **A result before you build a pipeline.** Point it at a labeled table and predict. You find
  out whether the signal is there before you spend the week on feature engineering.
* **One endpoint instead of many models.** You keep one call where you would otherwise keep a
  trained model per table, per segment or per customer.
* **Nothing to tune.** There is no learning rate, no tree depth, no search space and no
  retraining schedule.

## What it costs

* **Your table is read on every call.** Its size is a cost you pay each time rather than once.
* **`fit` is a real job on a GPU.** It takes seconds to minutes and bills the rows you send.
* **Column names carry no meaning.** Text and dates are read as categories, so the model sees
  which rows share a value, not what the value means.

"No training" is a claim about gradients. It is not a claim about cost or latency, and
[How Hollerith works](/concepts/how-hollerith-works) is honest about the difference.

## Where to start

* [Quickstart](/quickstart) — install, key, and a first prediction
* [How Hollerith works](/concepts/how-hollerith-works) — why reading a table beats training on it
* [The model](/reference/model) — what it is good at, and where it is not the right tool
* [Improving accuracy](/guides/improving-accuracy) — what to try when the first number disappoints