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

# Analytics and QC

> Understand analytics, QC monitoring, localization performance insights, and operational reporting inside the Ollang Project Management Dashboard.

## Overview

The Analytics section inside the Ollang Project Management Dashboard helps organizations:

* monitor localization performance,
* evaluate multilingual quality,
* understand credit consumption,
* track operational delivery,
* benchmark AI performance,
* and analyze human review behavior.

Analytics provide visibility into:

* Order performance,
* AI localization quality,
* Human review behavior,
* QC progression,
* operational delivery,
* and billing-related insights.

***

# Analytics Visibility and Permissions

## Access Requirements

Analytics visibility requires:

* **Access Billing** permission.

Only Project Management Users with Access Billing permissions can view:

* Analytics,
* Payment Overview,
* Credit Overview,
* QC performance,
* and financial reporting.

<Info>
  Standard Project Management Users without Access Billing permission cannot access the Analytics section.
</Info>

***

# Analytics Sections

The Analytics dashboard currently includes:

<CardGroup cols={2}>
  <Card title="Order Analytics">
    Analyze localization activity across Orders.
  </Card>

  <Card title="Payment Overview">
    View invoicing and outstanding payment information.
  </Card>

  <Card title="Credit Overview">
    Monitor AI credit availability and usage.
  </Card>

  <Card title="Recent Order Details">
    Review recently processed localization Orders.
  </Card>

  <Card title="QC Score Progression by LLM">
    Understand localization quality progression over time.
  </Card>

  <Card title="Average Human Edit Percentage">
    Understand how much AI-generated output required human modification.
  </Card>
</CardGroup>

***

# Order Analytics

## Overview

Order Analytics helps organizations understand:

* localization volume,
* workflow activity,
* delivery performance,
* language pair trends,
* and operational behavior.

Organizations can analyze localization activity across:

* Order types,
* language pairs,
* delivery priorities,
* and time periods.

***

# Order Analytics Filters

Order Analytics supports the following filters:

<CardGroup cols={2}>
  <Card title="Start Date / End Date">
    Analyze operational performance within a selected timeframe.
  </Card>

  <Card title="Order Type">
    Filter analytics by localization workflow type.
  </Card>

  <Card title="Source Language">
    Analyze localization performance from a selected source language.
  </Card>

  <Card title="Target Language">
    Analyze localization behavior for specific target markets.
  </Card>

  <Card title="Order Level">
    Analyze AI-only or AI + Human Review workflows.
  </Card>

  <Card title="Delivery Type">
    Filter between standard and rush delivery workflows.
  </Card>
</CardGroup>

***

# Example Analytics Query

Example:

```text theme={null}
Filters Applied:

Start Date:
January 1

End Date:
February 28

Source Language:
English

Target Language:
French

Order Type:
Subtitle Translation
```

Result:

```text theme={null}
Localized subtitle activity
for English → French
within the selected timeframe
```

<Frame>
  <img src="https://mintcdn.com/ollang/a4ucNOBUQRWRvsN6/images/d1.png?fit=max&auto=format&n=a4ucNOBUQRWRvsN6&q=85&s=240a7ddafdb097da7bdd430884c54f36" alt="D1" width="1378" height="1040" data-path="images/d1.png" />
</Frame>

***

# Credit Overview

## Overview

Credit Overview provides visibility into:

* total AI credits,
* remaining AI credits,
* and localization consumption.

This helps organizations:

* manage localization budgets,
* understand operational usage,
* and monitor available processing capacity.

***

# Credit Visibility

The dashboard currently displays:

```text theme={null}
Total AI Credits
```

This helps operational teams understand:

* available localization capacity,
* and current AI usage availability.

***

# Payment Overview

## Overview

Payment Overview provides billing-related visibility for operational teams.

This section helps organizations understand:

* invoice-related activity,
* pending payment behavior,
* and financial status.

***

# Payment Information

The Payment Overview currently includes:

<CardGroup cols={2}>
  <Card title="To Be Invoiced">
    Pending operational amounts expected to be invoiced.
  </Card>

  <Card title="Outstanding Balance">
    Existing balance pending payment resolution.
  </Card>
</CardGroup>

<Frame>
  <img src="https://mintcdn.com/ollang/a4ucNOBUQRWRvsN6/images/D3.png?fit=max&auto=format&n=a4ucNOBUQRWRvsN6&q=85&s=273cafce4f613136fc30d32c29a64545" alt="D3" width="990" height="248" data-path="images/D3.png" />
</Frame>

# Recent Order Details

## Overview

Recent Order Details provide operational visibility into:

* recently processed Orders,
* localization activity,
* and workflow execution.

This helps Project Management Users:

* quickly monitor activity,
* identify recent deliveries,
* and review localization progress.

<Frame>
  <img src="https://mintcdn.com/ollang/a4ucNOBUQRWRvsN6/images/d4.png?fit=max&auto=format&n=a4ucNOBUQRWRvsN6&q=85&s=e52e06c2768ba2885329acd05fdec44f" alt="D4" width="1100" height="342" data-path="images/d4.png" />
</Frame>

***

# Typical Order Details

Recent Orders commonly include:

* Order information,
* localization workflow details,
* language pair information,
* status,
* and operational delivery details.

***

# Advanced Search Navigation

The Analytics section includes:

* **Advanced Search**

This action redirects users to:

* the Global Search page.

This enables teams to:

* deeply investigate Orders,
* search historical activity,
* and perform operational tracking.

Workflow:

```text theme={null}
Analytics
        ↓
Advanced Search
        ↓
Global Search Page
```

***

# QC Score Progression by LLM

## Overview

QC Score Progression by LLM provides:

* aggregated localization quality trends.

Rather than comparing individual provider runs directly, this analytics view helps organizations understand:

```text theme={null}
How localization quality
improves or changes over time
for a selected workflow.
```

***

# Available Filters

QC Score Progression supports:

<CardGroup cols={2}>
  <Card title="Start Date / End Date">
    Analyze localization quality across a selected timeframe.
  </Card>

  <Card title="Source Language">
    Filter by source language.
  </Card>

  <Card title="Target Language">
    Filter by target language.
  </Card>

  <Card title="Order Type">
    Analyze QC progression for a specific workflow.
  </Card>
</CardGroup>

***

# How QC Progression Works

The system analyzes:

* average QC scores,
* quality progression trends,
* and localization improvements.

Example:

```text theme={null}
English → French
Subtitle Translation
Last 2 Months
```

The dashboard may show:

```text theme={null}
First Average QC Score:
78%

Latest Average QC Score:
92%

Best Performing LLM:
Provider X

Improvement:
+14%
```

This helps organizations understand:

* quality evolution,
* workflow effectiveness,
* and localization performance over time.

***

# Important Clarification

<Info>
  QC Score Progression is an aggregated trend analysis and not a side-by-side comparison of every provider run.
</Info>

The platform highlights:

* first QC score average,
* latest QC score average,
* best-performing LLM,
* and quality improvement trends.

<Frame>
  <img src="https://mintcdn.com/ollang/a4ucNOBUQRWRvsN6/images/d5.png?fit=max&auto=format&n=a4ucNOBUQRWRvsN6&q=85&s=2c7800283a2b360a3c0522dddc5c03ec" alt="D5" width="1362" height="770" data-path="images/d5.png" />
</Frame>

***

# Average Human Edit Percentage

## Overview

Average Human Edit Percentage helps organizations understand:

```text theme={null}
How much AI-generated content
was modified during human review.
```

This metric is especially useful for:

* benchmarking provider quality,
* evaluating localization efficiency,
* identifying problematic language pairs,
* and optimizing workflows.

***

# Human Edit Filters

Average Human Edit Percentage supports:

<CardGroup cols={2}>
  <Card title="Start Date / End Date">
    Analyze human edit behavior within a selected timeframe.
  </Card>

  <Card title="Source Language">
    Analyze edit behavior for specific source languages.
  </Card>

  <Card title="Target Language">
    Analyze edit behavior for target markets.
  </Card>

  <Card title="Order Type">
    Evaluate edit percentage across localization workflows.
  </Card>
</CardGroup>

***

# Measurement Methodology

Human Edit Percentage is measured at:

* segment level.

This means the platform evaluates:

```text theme={null}
How much subtitle content
required modification
during human review.
```

The dashboard provides:

* overall average human edit percentage,
* and Order-level edit details.

***

# Example Human Edit Analysis

Example:

```text theme={null}
English → French
Subtitle Translation
Date Range:
30 Days
```

Result:

```text theme={null}
Average Human Edit:
22%

Order Details:
Order A → 15%
Order B → 32%
Order C → 18%
```

This helps organizations identify:

* efficient workflows,
* high-performing providers,
* and problematic localization patterns.

<Frame>
  <img src="https://mintcdn.com/ollang/a4ucNOBUQRWRvsN6/images/d6.png?fit=max&auto=format&n=a4ucNOBUQRWRvsN6&q=85&s=846d21e44fc992409c91da020e921676" alt="D6" width="1314" height="1178" data-path="images/d6.png" />
</Frame>

***

# Using Analytics for Workflow Optimization

Organizations commonly use Analytics to answer questions such as:

```text theme={null}
Which workflow performs best
for English → French subtitles?
```

```text theme={null}
Which provider reduces
human editing effort?
```

```text theme={null}
Which localization workflows
improve QC score over time?
```

This helps teams:

* improve localization quality,
* optimize provider selection,
* reduce review effort,
* and benchmark multilingual performance.

***

# Best Practices

Organizations typically achieve the best outcomes by combining:

```text theme={null}
QC Score Progression
        +
Human Edit Percentage
        +
AI QC Evaluation
        +
Human QC Annotation
```

This provides visibility into:

* AI quality,
* human correction effort,
* localization consistency,
* and long-term optimization opportunities.

***

# Important Operational Notes

<AccordionGroup>
  <Accordion title="Who can access Analytics?">
    Only users with Access Billing permission can access the Analytics section.
  </Accordion>

  <Accordion title="Can QC Score Progression compare every provider side-by-side?">
    No. The analytics show aggregated quality progression and best-performing LLM insights rather than run-by-run comparisons.
  </Accordion>

  <Accordion title="What does Human Edit Percentage measure?">
    It measures how much AI-generated content was modified during human review at segment level.
  </Accordion>

  <Accordion title="Can users filter Analytics by language pair?">
    Yes. Analytics support Source Language and Target Language filtering.
  </Accordion>

  <Accordion title="What does Advanced Search do?">
    Advanced Search redirects users to the Global Search page for deeper operational investigation.
  </Accordion>

  <Accordion title="Can Analytics help optimize workflows?">
    Yes. Analytics help organizations benchmark localization quality, provider effectiveness, and human review effort.
  </Accordion>
</AccordionGroup>