Content Intelligence

Hrizn’s Content Intelligence system analyzes your dealership’s inventory, search performance, competitive landscape, and seasonal trends every night to surface actionable content recommendations. This guide explains how the system works and how to build automations around it using the API.

How it works

Every night at 4:00 AM ET, the content intelligence pipeline runs a multi-stage analysis:

Snapshot — Aggregates your site’s inventory, articles, IdeaClouds, search console data, and analytics into a unified snapshot.
Gap analysis — Compares your snapshot against network benchmarks and runs 20+ analyzers to identify content gaps.
Scoring — Each recommendation is assigned a priority score (0-100) based on estimated traffic impact, competitive urgency, and timeliness.
Deduplication — Semantic similarity and fingerprint matching prevent duplicate recommendations across nightly runs.

The pipeline produces up to 200 active recommendations per site, covering everything from missing model pages to seasonal events.

Recommendation types

Each recommendation has a suggestion_type that indicates the kind of content opportunity. Use the content intelligence types reference for a full list, or call GET /reference/content-intelligence-types to get types with their actionable_via field.

Inventory-driven

Type	Description	Action endpoint
`missing_model_page`	Vehicle in inventory with no model research page	`POST /model-landing-pages`
`inventory_move`	Aging stock, high volume, or price segment opportunity	`POST /model-landing-pages`
`model_year_transition`	New model year arriving, needs updated content	`POST /model-landing-pages`
`price_segment_content`	Price segment (e.g. “SUVs under $35k”) with no content	`POST /ideaclouds`

Search and performance

Type	Description	Action endpoint
`search_gap`	High-impression query with no matching content	`POST /ideaclouds`
`search_position_opportunity`	Page 2 query that could move to page 1	`POST /ideaclouds`
`keyword_cannibalization`	Multiple pages competing for same keyword	`POST /ideaclouds`
`content_refresh`	Article declining in traffic, needs refresh	`POST /content/{id}/generate`
`trending_topic`	Trending topic in search with no coverage	`POST /ideaclouds`

Competitive and network

Type	Description	Action endpoint
`comparison_opportunity`	Cross-shopped vehicles for comparison article	`POST /comparisons`
`network_benchmark`	Peer dealerships have content this site is missing	`POST /ideaclouds`
`network_winning_pattern`	Pattern performing well across peer network	`POST /ideaclouds`

IdeaCloud and content

Type	Description	Action endpoint
`ideacloud_content`	IdeaCloud with unanswered questions	`POST /ideaclouds`
`ideacloud_question`	Specific uncovered question in an IdeaCloud	`POST /ideaclouds`
`seasonal_content`	Seasonal topic timely for current period	`POST /ideaclouds`

Events

Type	Description	Action endpoint
`oem_event`	OEM campaign or incentive event	`POST /sales-events`
`seasonal_event`	Seasonal sales event (e.g. year-end clearance)	`POST /sales-events`
`model_launch_event`	New model launch or reveal event	`POST /sales-events`
`community_event`	Community engagement event	`POST /sales-events`
`local_event`	Local community event	`POST /sales-events`

Safety and service

Type	Description	Action endpoint
`recall_content`	NHTSA recall for vehicle in inventory	`POST /ideaclouds`
`service_content`	Service interval milestone for popular model	`POST /ideaclouds`

Priority scoring

Every recommendation includes a priority_score from 0 to 100:

Tier	Score range	Meaning
High	80-100	High-impact opportunity — significant traffic or competitive gap
Medium	60-79	Moderate opportunity worth considering
Low	0-59	Lower urgency, still a valid gap

Use priority_min when listing recommendations to focus on the most impactful opportunities:

curl "https://api.app.hrizn.io/v1/public/content-intelligence?priority_min=80" \
  -H "X-API-Key: hzk_your_key_here"

Metadata

Each recommendation includes a metadata object with type-specific context. The shape varies by suggestion_type:

Vehicle metadata (model pages, comparisons, transitions)

{
  "make": "Toyota",
  "model": "Camry",
  "year": 2026,
  "inventory_count": 14,
  "estimated_monthly_searches": 8400
}

Comparison metadata

{
  "vehicles": [
    { "make": "Toyota", "model": "Camry", "year": 2026 },
    { "make": "Honda", "model": "Accord", "year": 2026 }
  ],
  "search_overlap_score": 0.78
}

Search metadata (search gaps, position opportunities)

{
  "query": "2026 toyota camry towing capacity",
  "impressions": 12500,
  "position": 14.2,
  "clicks": 340
}

Event metadata

{
  "eventName": "Year-End Clearance Sale",
  "category": "Sales Event",
  "startDate": "2026-12-26",
  "endDate": "2026-12-31",
  "description": "End of year clearance pricing on all remaining 2026 models"
}

Traffic estimate (when available)

{
  "trafficEstimate": {
    "estimatedMonthlyViews": 2400,
    "confidence": "high",
    "basis": "search_console_data"
  }
}

Recommendation lifecycle

Recommendations follow a four-state lifecycle:

Status	Meaning
`active`	Surfaced to the site, ready to be acted on or dismissed
`dismissed`	Reviewed and declined; stays in DB to prevent re-suggesting
`acted_on`	Content was created; linked to the article via `acted_on_article_id`
`expired`	Superseded by a newer batch or past the `expires_at` date

The nightly pipeline automatically expires old recommendations and generates fresh ones. Dismissed recommendations are kept so the pipeline can use fingerprint deduplication to avoid re-suggesting the same gap.

Full automation workflow

Here’s how to build an end-to-end automation that monitors recommendations and creates content:

Get the summary

Call GET /content-intelligence/summary to check how many active recommendations exist and which types are most common.

curl "https://api.app.hrizn.io/v1/public/content-intelligence/summary" \
  -H "X-API-Key: hzk_your_key_here"

List high-priority recommendations

Fetch the top recommendations, optionally filtering by type or article type.

curl "https://api.app.hrizn.io/v1/public/content-intelligence?priority_min=80&types=missing_model_page,comparison_opportunity" \
  -H "X-API-Key: hzk_your_key_here"

Create content from a recommendation

Use the suggestion_type and actionable_via fields to determine which endpoint to call. For example, for missing_model_page, extract the vehicle info from metadata and create a model landing page:

curl -X POST "https://api.app.hrizn.io/v1/public/model-landing-pages" \
  -H "X-API-Key: hzk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "year": 2026,
    "make": "Toyota",
    "model": "Camry"
  }'

The response includes the new article id.

Link the recommendation to the article

Close the feedback loop by marking the recommendation as acted on:

curl -X POST "https://api.app.hrizn.io/v1/public/content-intelligence/c3a1f8b2.../act" \
  -H "X-API-Key: hzk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"article_id": "a1b2c3d4..."}'

Dismiss irrelevant recommendations

For recommendations you don’t want to act on, dismiss them individually or in bulk:

curl -X POST "https://api.app.hrizn.io/v1/public/content-intelligence/bulk-dismiss" \
  -H "X-API-Key: hzk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"ids": ["id1", "id2", "id3"]}'

Required scopes

Scope	Grants access to
`content_intelligence:read`	List, get, dismiss, and summary endpoints
`content_intelligence:write`	Act-on and bulk-dismiss endpoints

For the full automation workflow you’ll also need content:write or ideaclouds:write (depending on which content creation endpoints you call).

Getting Started

Concepts

Guides

Scopes

How it works

Recommendation types

Inventory-driven

Search and performance

Competitive and network

IdeaCloud and content

Events

Safety and service

Priority scoring

Metadata

Vehicle metadata (model pages, comparisons, transitions)

Comparison metadata

Search metadata (search gaps, position opportunities)

Event metadata

Traffic estimate (when available)

Recommendation lifecycle

Full automation workflow

Required scopes

​How it works

​Recommendation types

​Inventory-driven

​Search and performance

​Competitive and network

​IdeaCloud and content

​Events

​Safety and service

​Priority scoring

​Metadata

​Vehicle metadata (model pages, comparisons, transitions)

​Comparison metadata

​Search metadata (search gaps, position opportunities)

​Event metadata

​Traffic estimate (when available)

​Recommendation lifecycle

​Full automation workflow

​Required scopes

How it works

Recommendation types

Inventory-driven

Search and performance

Competitive and network

IdeaCloud and content

Events

Safety and service

Priority scoring

Metadata

Vehicle metadata (model pages, comparisons, transitions)

Comparison metadata

Search metadata (search gaps, position opportunities)

Event metadata

Traffic estimate (when available)

Recommendation lifecycle

Full automation workflow

Required scopes