> For the complete documentation index, see [llms.txt](https://intelfactor.gitbook.io/doc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://intelfactor.gitbook.io/doc/intelfactor-ai/architecture-and-data/system-overview.md).

# System overview

High-level architecture and responsibilities.

Explains system zones and responsibilities.

### High-Level Architecture

IntelFactor consists of two primary layers:

```mermaid
flowchart LR
  subgraph OT[Zone 1: Production (OT)]
    CAM[Cameras / sensors] --> EDGE[Edge node]
    EDGE --> DET[Inspection + guardrails]
    DET --> EVT[(Local event DB)]
    DET --> EVID[(Local evidence store)]
    DET --> RCA[Local RCA / decision support]
  end

  subgraph SITE[Zone 2: Site data zone (on-prem)]
    HUB[Site hub services]
    HUB --> PG[(Relational DB)]
    HUB --> OBJ[(Object storage)]
    HUB --> DASH[Dashboards]
    HUB --> REG[Model / artifact registry]
  end

  subgraph REMOTE[Zone 3: Remote access (optional)]
    VPN[VPN / jump host]
    OBS[Monitoring / alerts]
  end

  EDGE -. controlled conduit .-> HUB
  VPN -. maintenance only .-> HUB
  HUB --> OBS
```

### Edge Layer

* Captures production signals
* Executes inspection logic
* Classifies outcomes
* Packages evidence
* Emits structured events

### Cloud Layer

* Ingests inspection events
* Stores inspection records
* Provides aggregated analytics
* Supports dashboards and reporting

Each layer has clearly defined responsibilities and failure boundaries.

### Next steps

* [Deployment overview](/doc/intelfactor-ai/architecture-and-data/deployment-overview.md)
* [Edge pipeline](/doc/intelfactor-ai/architecture-and-data/edge-pipeline.md)
* [Interfaces](/doc/intelfactor-ai/architecture-and-data/interfaces.md)


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://intelfactor.gitbook.io/doc/intelfactor-ai/architecture-and-data/system-overview.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.
