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Taxonomy Manager

Define required photo checklists for media workflows

What Is a Taxonomy?

A taxonomy is a predefined checklist of required shots for a specific use case. It tells the Media Processing Node’s Coverage Scoring section what photos to expect and how to recognize them. Each taxonomy includes:
  • Required Shots: Must be captured before proceeding
  • Conditional Shots: Required only when a specific condition is met
  • AI Descriptions: How Claude identifies each shot type
  • Extraction Links: Which shots should trigger OCR data extraction
Taxonomies are managed separately from workflows, allowing you to reuse the same shot requirements across multiple workflows and clients.

Accessing the Taxonomy Manager

Navigate to Settings → Taxonomy Manager to view, create, and edit taxonomies. The List View displays all available taxonomies with:
  • Taxonomy name and icon
  • Industry category
  • Shot count (required vs. conditional)
  • Type badge (Plura Template or Custom)
Use the filters to narrow by industry or type, or search by name.

Creating a Taxonomy

1

Click Create Taxonomy

Or clone an existing template to start with a base configuration.
2

Set Basic Info

  • Name: Descriptive name (e.g., “Auto Damage - Standard”)
  • Industry: Category for filtering (Auto, Homeowners, Healthcare, Logistics)
  • Icon: Visual identifier for the list view
  • Description: Optional context for your team
3

Add Shot Requirements

Define each photo you need. See Shot Configuration below.
4

Configure Conditions

For conditional shots, specify when they’re required.
5

Link Extraction Fields

Connect shots to extraction fields for OCR (e.g., VIN plate → VIN extraction).
6

Save

Your taxonomy is now available in the Media Processing Node dropdown.

Shot Configuration

Each shot in a taxonomy has the following fields:
FieldPurposeExample
Shot IDVariable name for code/triggersfront_full
Display NameHuman-readable label shown to users”Front of Vehicle”
AI DescriptionHow Claude recognizes this shot type”Full front view showing hood, grille, headlights, and front bumper”
DistanceExpected framingWide / Mid / Close-up
RequirementWhen this shot is neededRequired / Conditional / Optional
ConditionLogic for conditional shotsairbags_deployed == true
Extract FieldLink to extraction fieldVIN, License Plate, etc.

Writing Good AI Descriptions

The AI Description is critical — it’s what Claude uses to determine if a photo matches a shot type. Good descriptions include:
  • What should be visible in the frame
  • Relative positioning and framing
  • Key identifying features
Example — Front of Vehicle:
Full front view showing hood, grille, headlights, and front bumper. 
Vehicle should fill most of the frame. 
Shot from directly in front, not at an angle.
Example — VIN Plate:
Close-up of the VIN plate on the dashboard (visible through windshield) 
or on the door jamb sticker. All 17 characters must be clearly readable.
Be specific about what makes this shot different from similar shots. For corners, specify which corner and the expected angle (e.g., “45-degree angle showing both front and driver side”).

Conditional Shots

Conditional shots are only required when a specific condition is true. This keeps the shot list manageable while ensuring you capture situational documentation.

Condition Syntax

Conditions use the same variable syntax as Decision Triggers:
{variable} == {value}
{variable} != {value}
{variable} > {value}

Common Conditions

ShotConditionWhen Required
Interior / Airbagsairbags_deployed == trueWhen airbags deployed
Glass Close-upglass_damaged == trueWhen glass is damaged
Undercarriageflood_damage == trueFor flood claims
Roof Damagehail_reported == trueFor hail claims

Setting Condition Variables

Condition variables can be set by:
  1. Data Extraction in an earlier node
  2. API Call that returns claim details
  3. User Input captured in the conversation
  4. Workflow Variables passed from the trigger

Plura Template Library

Plura ships with pre-built taxonomies for common use cases. These can be used as-is or cloned and customized.

Auto Insurance

TaxonomyShotsDescription
Auto Damage - Standard12 (9 req, 3 cond)Standard FNOL documentation
Auto Damage - Total Loss15 (12 req, 3 cond)Extended for total loss evaluation
VIN Verification - Quick4 (4 req)Quick VIN + plate capture

Homeowners Insurance

TaxonomyShotsDescription
Water Damage - Standard14 (10 req, 4 cond)Water intrusion documentation
Fire Damage - Standard16 (12 req, 4 cond)Fire and smoke damage
Roof Damage - Standard10 (8 req, 2 cond)Roof condition assessment

Healthcare

TaxonomyShotsDescription
Insurance Card2 (2 req)Front and back of insurance card
Driver’s License2 (2 req)Front and back of ID

Logistics

TaxonomyShotsDescription
Package Intake6 (4 req, 2 cond)Shipment receiving
Damage Documentation8 (6 req, 2 cond)Damage claims

Example: Auto Damage - Standard

Here’s the complete breakdown of the Auto Damage - Standard taxonomy:

Required Shots (9)

Shot IDDisplay NameAI DescriptionDistance
front_fullFront of VehicleFull front view showing hood, grille, headlights, bumperWide
rear_fullRear of VehicleFull rear view showing trunk/tailgate, taillights, bumperWide
left_sideLeft SideFull driver side from front wheel to rear wheelWide
right_sideRight SideFull passenger side from front wheel to rear wheelWide
corner_flFront-Left Corner45° angle showing front and driver sideWide
corner_frFront-Right Corner45° angle showing front and passenger sideWide
corner_rlRear-Left Corner45° angle showing rear and driver sideWide
corner_rrRear-Right Corner45° angle showing rear and passenger sideWide
damage_closeupDamage Close-upClose-up of primary damage area, minimum 2 photosClose-up

Required with Extraction (3)

Shot IDDisplay NameExtract FieldValidation
vin_plateVIN PlateVIN NumberVIN Checksum
license_plateLicense PlatePlate NumberUS Plate Format
odometerOdometerMileageNumber (0-500000)

Conditional Shots (3)

Shot IDDisplay NameCondition
interior_airbagsInterior / Airbagsairbags_deployed == true
glass_damageGlass Close-upglass_damaged == true
undercarriageUndercarriageflood_damage == true

Taxonomy Versioning

When you update a taxonomy, existing in-flight conversations continue using the version they started with. New conversations use the updated taxonomy.
Significant taxonomy changes should be tested before deployment. Consider cloning the taxonomy and testing the new version in a staging workflow before updating the production taxonomy.

JSON Import/Export

For power users, taxonomies can be exported and imported as JSON.

Export

Click Export JSON on any taxonomy detail view to download the configuration.

Import

Use Create Taxonomy → Import JSON to create a new taxonomy from a JSON file.

JSON Structure

{
  "name": "Auto Damage - Standard",
  "industry": "auto_insurance",
  "icon": "🚗",
  "shots": [
    {
      "id": "front_full",
      "display_name": "Front of Vehicle",
      "ai_description": "Full front view showing hood, grille...",
      "distance": "wide",
      "requirement": "required",
      "extract_field": null
    },
    {
      "id": "vin_plate",
      "display_name": "VIN Plate",
      "ai_description": "Close-up of VIN plate...",
      "distance": "closeup",
      "requirement": "required",
      "extract_field": "vin"
    },
    {
      "id": "interior_airbags",
      "display_name": "Interior / Airbags",
      "ai_description": "Interior shot showing deployed airbags...",
      "distance": "wide",
      "requirement": "conditional",
      "condition": "airbags_deployed == true",
      "extract_field": null
    }
  ]
}

Best Practices

Start with Templates

Clone a Plura template and customize rather than building from scratch.

Be Specific in Descriptions

Detailed AI descriptions reduce misclassification and improve coverage accuracy.

Use Conditional Shots

Don’t overload required shots — use conditions to keep the flow manageable.

Link Extraction Fields

Connect shots to extraction when you need OCR data from that specific photo.

Next Steps