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

# Fix Inbox

> A central queue of AI-suggested data quality fixes you can review, approve, and apply in bulk — with a confidence-based trust system that lets you auto-approve the safe ones.

## Overview

The Fix Inbox is where every AI-suggested correction lives until you act on it.
{/* mintlify rebuild trigger */} Whenever ORCA detects a quality issue it can fix — a misclassification, a format violation, a missing value with a confident imputation strategy — it creates a proposed correction step grouped under the file it belongs to. You review them in one place, approve the ones you trust, and apply them in bulk.

This is different from one-shot [auto-remediation](/remediation), which generates a fix plan for a single file at the moment you click "Remediate". The Fix Inbox is a continuous queue across every analyzed file in your workspace.

## How fixes get into the inbox

Fixes are created automatically by several engines:

| Engine                   | Triggers                                                          | Example                                                                              |
| ------------------------ | ----------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
| **Rule suggester**       | After every job completes                                         | "Add a `not_null` rule on `customer_email` (3% nulls detected)"                      |
| **Correction suggester** | When a quality issue has a confident strategy                     | "Standardize phone format to `+CC NNNNNNNNNN`"                                       |
| **Anomaly explainer**    | When [anomaly detection](/anomaly-detection) flags a metric drift | "Investigate `revenue` — current value departs sharply from its historical baseline" |

Each suggestion has:

* **Confidence score** (0–1) — how sure the engine is the fix is correct
* **Source** — `auto` (engine-generated) or `user` (manually created)
* **Status** — `proposed`, `active` (approved), `rejected`, or `applied`
* **Preview** — before/after sample so you can verify the change before applying

## The trust system

ORCA learns which fixes you typically approve and uses that to set an **auto-apply confidence threshold** per workspace. High-confidence fixes are one-click approvable, and the highest-confidence fixes are pre-checked when you open the inbox so you can clear the queue in a single click.

You can see and adjust the threshold from your **Org profile** trust history. As you accept or reject suggestions, the trust history updates and the threshold adapts.

## Working with the inbox

Open **Fix Inbox** from the sidebar.

<Steps>
  <Step title="Review pipelines">
    Suggestions are grouped by file into pipelines. Each pipeline shows the file name, total proposed steps, and a summary of the change types (null fills, format fixes, deduplication, etc.).
  </Step>

  <Step title="Inspect a step">
    Click any step to see the before/after preview, the engine that produced it, and the confidence score.
  </Step>

  <Step title="Approve high-confidence fixes in bulk">
    Click **Approve all high-confidence** to one-shot accept every step above your trust threshold across all pipelines. This is the fastest way to clear the queue.
  </Step>

  <Step title="Select and act on a subset">
    Use the checkboxes to multi-select steps. The bulk action bar lets you approve, reject, or apply just the selected ones.
  </Step>

  <Step title="Apply">
    Approving a step marks it `active`. To actually execute the changes against your file, click **Apply** on the pipeline. ORCA generates a remediated copy — your original file is never modified.
  </Step>
</Steps>

## Rejecting and editing

Rejection is just as important as approval — every reject teaches the trust system what you don't want.

* **Reject** removes the step from the queue and records negative feedback.
* **Edit** lets you tweak a step's parameters before approving (e.g. change the imputation value, adjust a regex).

Both actions feed the [feedback API](/api-reference/endpoints) so future suggestions become more accurate.

## API access

Every action in the inbox is available via the REST API:

```bash theme={null}
# List all pipelines with their steps
GET /api/v1/corrections

# List rule suggestions across files
GET /api/v1/suggestions?min_confidence=0.9

# Approve all high-confidence steps in a pipeline
POST /api/v1/corrections/{pipeline_id}/approve-all-high-confidence

# Apply a pipeline
POST /api/v1/corrections/{pipeline_id}/apply

# Submit feedback on a single suggestion
POST /api/v1/suggestions/{suggestion_id}/feedback
```

## Tips

* **Start conservative.** Set your auto-apply threshold to the strictest setting for the first week. Watch what gets approved. Then loosen it if you trust the system.
* **Use rejection as teaching.** A rejected suggestion isn't lost work — it's training data that improves the next round.
* **Pair with [contracts](/contracts).** If a fix repeatedly clears the same violation, promote it to a contract rule so the issue never reaches the inbox in the first place.

## What's next?

* [Auto-remediation](/remediation) — one-shot fix plans for individual files
* [Data contracts](/contracts) — turn recurring fixes into enforced rules
* [AI Readiness](/ai-readiness) — see how applied fixes affect your readiness scores