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

# Quality Scores

> How FlexOrch grades documents and what the scores mean for your datasets.

## Overview

Every processed document receives a **quality score** (0–100) and a **grade** (A–D). These help you filter high-quality records and identify documents that may need attention before inclusion in a dataset.

***

## Quality grades

| Grade | Score range | What it means                                        |
| ----- | ----------- | ---------------------------------------------------- |
| **A** | 85–100      | High quality — key fields extracted, low noise       |
| **B** | 65–84       | Good — minor gaps or slight noise                    |
| **C** | 45–64       | Moderate — notable extraction issues or OCR problems |
| **D** | 0–44        | Low — significant problems; review before using      |

***

## Factors that affect the score

* **Field fill rate** — How many expected fields for the document type were successfully extracted
* **Noise ratio** — Proportion of the document that is non-informative content (e.g., page numbers, repeated headers)
* **OCR confidence** — For scanned documents, a low OCR confidence score caps the grade at C

***

## Accessing quality data

Quality information is available in the job response:

```json theme={null}
{
  "processing_summary": {
    "quality": {
      "score": 91,
      "grade": "A",
      "field_fill_rate": 0.94,
      "ocr_confidence": null
    }
  }
}
```

`ocr_confidence` is `null` when OCR was not used (text-based PDF or DOCX).

***

## Quality trend

Track quality over time on the **Usage → Quality** tab in the platform, or via the API:

```bash theme={null}
curl "https://api.flexorch.com/v1/usage/quality-trend?period=30d" \
  -H "X-API-KEY: dfx_your_key_here"
```

***

## Filtering by grade

Use the platform's **Datasets → Ready to Build** tab to filter completed jobs by grade before building a dataset. Or filter in your own pipeline:

```python theme={null}
from flexorch_sdk import FlexOrch

client = FlexOrch(api_key="dfx_your_key_here")

# Only include grade A and B jobs in the dataset
good_jobs = [j for j in client.jobs.list() if j.quality_grade in ("A", "B")]
dataset = client.datasets.build(job_ids=[j.id for j in good_jobs])
```

***

## Leaving feedback

If a result looks wrong, you can submit feedback directly from the Jobs page (thumbs up / thumbs down) or via the API:

```bash theme={null}
curl -X POST "https://api.flexorch.com/v1/jobs/{id}/feedback" \
  -H "X-API-KEY: dfx_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"rating": "negative", "issue": "wrong_fields"}'
```