> For the complete documentation index, see [llms.txt](https://docs.crxtoken.xyz/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.crxtoken.xyz/page-4.system-architecture/4.7-scalability-and-reliability.md).

# 4.7 Scalability and Reliability

The system must scale along several dimensions: number of users, number of connected data sources, and complexity of models. To support this, the architecture favors stateless service components where practical, horizontal scaling for compute-heavy model inference, and message-based communication between pipeline stages. Heavy computations can be scheduled asynchronously and cached, while latency-sensitive interactions, such as fetching a summary through the bot, are served from precomputed feature stores and cached insight objects.

Reliability is addressed through redundancy and graceful degradation. If a non-critical model service becomes temporarily unavailable, the system falls back to simpler but still correct summaries, rather than failing outright. If a data source stops responding, users are informed explicitly, and the system maintains insight continuity on the basis of the last available data. This approach ensures that the platform behaves predictably even under partial failure, which is essential for trust in a credit-related context.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

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

```
GET https://docs.crxtoken.xyz/page-4.system-architecture/4.7-scalability-and-reliability.md?ask=<question>
```

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.