# Overview

Everything you need to integrate PredictHQ into your models, pipelines, and AI systems.

<a href="/pages/DsKDAetowkuExiU7GNln" class="button primary">API Quickstart</a> <a href="/spaces/kEFs8urDbSJqBmXUI3Lv" class="button secondary">API Reference</a>

## Core APIs

* [Events API](/api/events/search-events.md) - Structured, deduplicated real-world events for discovery, explainability, and building trust in AI-driven decisions
* [Features API](/api/features/get-features.md) - Pre-built, model-ready demand features aggregated from real-world events
* [Forecasts API](/api/forecasts/overview.md) - Event-driven demand forecasts without building or maintaining your own model
* [Beam API](/api/beam/overview.md) - Identify the real-world events that materially move your demand
* [Saved Locations API](/api/saved-locations/overview.md) - Define and manage reusable business locations for consistent querying across Events, Features, Beam, and Forecasts APIs

## How It Fits Together

PredictHQ APIs work as a pipeline, not in isolation. The [Standard Integration Pattern](/integrations/integration-guides/standard-integration-pattern.md) shows the recommended production architecture - from location setup through to forecasting and explainability.

## Example

Use your Beam Analysis ID to pull model-ready ML features for any date range - location, filters, and feature selection are applied automatically.

```python
import requests

response = requests.post(
    url="https://api.predicthq.com/v1/features/",
    headers={
        "Authorization": "Bearer $API_TOKEN",
        "Accept": "application/json"
    },
    json={
        "beam": {
            "analysis_id": "$ANALYSIS_ID"
        },
        "active": {
            "gte": "2026-06-01",
            "lte": "2026-06-30"
        }
    }
)

print(response.json())
```

## AI & Agents

* [Build with AI](/ai/build-with-ai.md) - Connect any MCP-compatible AI assistant to PredictHQ APIs through natural language
* [MCP Server](/ai/mcp.md) - Query events, demand intelligence, and all PredictHQ APIs without writing code
* [Bolt](/ai/bolt.md) - AI-native developer environment built into the WebApp

## Resources

* [API Overview](/api/overview/authenticating.md) - Authentication, rate limits, and API conventions
* [Guides](/getting-started/guides.md) - Step-by-step integration guides for core APIs and use cases
* [Python SDK](/sdks/python-sdk.md) - The official Python client for the PredictHQ API
* [Data Science Notebooks](/getting-started/data-science-notebooks.md) - Jupyter notebooks for Beam, Features API, and demand forecasting
* [System Status](https://www.predicthqstatus.com/) - Live status and incident history for PredictHQ services


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# Agent Instructions: Querying This Documentation

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
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```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.