Company Install Time Series

company_install_time_series — technology adoption trend over time / monthly install intensity / is a company growing or declining a product / tech churn. Returns a monthly installation-intensity time series per product for one company. Use for TREND questions — adoption growth, decline, or churn. Identify the company by EITHER company_domain OR hg_id (exactly one required). Do NOT use for a point-in-time answer: for CURRENT installed tech stack use company_technographic; for department/role usage use company_fai; for dollar spend use company_spend. Each data_points[].intensity is an integer 1-31 = days the product was detected that month (null = no detection). Most-recent point is typically null (current month incomplete). For trend analysis use intensity_momentum (positive = growing, negative = declining; magnitude meaningful), not raw intensity; current_intensity is a separate aggregate NOT on the 1-31 scale. Filtering is ID-based only — numeric product_ids/vendor_ids or string category_ids; resolve IDs first. Filter IDs matching nothing return products: [] with HTTP 200 and 0 credits — this tool sets the warning field when filters were provided but nothing matched. Use country_codes with granularity='country' for per-country breakdowns. Credit cost: 3 per product returned; 0 on empty results.

Credits

3 — 3 per product returned. See the full credit table for how AI Credits work.

Parameters

Name	Type	Default	Description
`company_domain`	string	-	The company domain to look up (e.g., 'cisco.com'). Provide EITHER company_domain OR hg_id — exactly one is required. Protocol prefixes (http://, https://), leading www., and trailing paths/queries/fragments are accepted and stripped automatically; case is normalized. match_confidence in the response is 0.90 when identified by domain.
`hg_id`	string	-	The hex HG company id (31-32 alphanumeric chars) as returned by search_companies. Provide EITHER hg_id OR company_domain — exactly one is required. match_confidence in the response is 1.0 when identified by hg_id.
`product_ids`	array	-	Filter by numeric HG product IDs (integers, e.g. [26434, 22]). Resolve IDs with product_search_and_enrich first — the upstream filters by integer ID only, so names or slug-style IDs return nothing.
`vendor_ids`	array	-	Filter by numeric HG vendor IDs (integers, e.g. [376]). Resolve IDs with get_vendor_information first — the upstream filters by integer ID only.
`category_ids`	array	-	Filter by HG category IDs (strings, e.g. ['cat-crm']). Resolve IDs with list_product_categories first — the upstream filters by category ID only, not name.
`country_codes`	array	-	ISO 3166-1 alpha-2 country codes (e.g. ['US', 'GB']). Use with granularity='country' to get per-country intensity breakdowns; each returned product then carries a country_code.
`granularity`	string	-	'global' aggregates intensity across all countries (default upstream behavior). 'country' returns one row per product per country with country_code populated on each product.
`time_range`	string	`last_24_months`	Time range for the series. Options: last_6_months, last_12_months, last_24_months, last_36_months. Default: last_24_months. Note: each option returns N+1 data points because the current incomplete month is appended as a null tail (e.g. last_6_months → 7 points, last_12_months → 13 points).
`max_results`	integer	`10`	Maximum number of products to return (1-50, default 10).

Required Integrations

This tool is only available when your organization has the following integration configured in the HG Insights Integrations settings:

HG Insights (Data API) (hginsights_v2_data_api)

Use Cases

How has a company's usage of a product trended over the past N months? — filter by product_ids
Is a company ramping up or winding down a specific vendor's footprint? — read intensity_momentum after a vendor_ids filter
Detect adoption growth or churn across a company's tech stack over time — unfiltered call, inspect data_points per product
Compare recent momentum across a category of tools at a company — category_ids filter, sort by intensity_momentum
Confirm whether a product's decline is recent or long-running — widen time_range to last_36_months

Example Usage

Cisco's tech-adoption trend over the last 2 years (default range)

{
  "tool": "company_install_time_series",
  "arguments": {
    "company_domain": "cisco.com"
  }
}

Snowflake usage trend at Cisco over the last 12 months (by product ID)

{
  "tool": "company_install_time_series",
  "arguments": {
    "company_domain": "cisco.com",
    "product_ids": [
      26434
    ],
    "time_range": "last_12_months"
  }
}

Per-country momentum for a specific vendor over 36 months

{
  "tool": "company_install_time_series",
  "arguments": {
    "company_domain": "cisco.com",
    "vendor_ids": [
      376
    ],
    "country_codes": [
      "US",
      "GB"
    ],
    "granularity": "country",
    "time_range": "last_36_months"
  }
}

Response Format

Field	Type	Description
`company`	object	Matched company details including ID, name, and match confidence
`company.company_id`	string	Resolved company ID
`company.company_name`	string	Company display name
`company.match_confidence`	number	Confidence of company match (0.0-1.0). 1.0 for hg_id, 0.90 for domain.
`time_range`	object	Time range covered by the returned data points
`time_range.start_date`	string	Start date (YYYY-MM format)
`time_range.end_date`	string	End date (YYYY-MM format)
`time_range.granularity`	string
`products`	array	Products with their time series data
`products[].product_id`	string
`products[].product_name`	string
`products[].vendor_name`	string \| null
`products[].category`	string \| null
`products[].is_active`	boolean	Whether the product was verified within the last 90 days
`products[].current_intensity`	number \| null	Aggregate intensity from global install data — not on the 1-31 daily scale
`products[].intensity_momentum`	number \| null	Momentum float — positive means growing, negative means declining; magnitude is meaningful (larger absolute values = stronger trend direction)
`products[].country_code`	string \| null	ISO alpha-2 country code. Populated when granularity='country'; null for global rows.
`products[].data_points`	array
`products[].data_points[].date`	string	YYYY-MM format
`products[].data_points[].intensity`	number \| null	Days the product was detected that month (1-31), null if no detection
`credits_consumed`	number	Credits consumed (3 per product returned)
`warning`	string	Present when filters were provided but no products matched — explains the miss and how to resolve it

company_technographic, product_search_and_enrich, get_vendor_information, get_product_category, company_spend, company_fai

Credits​

Parameters​

Required Integrations​

Use Cases​

Example Usage​

Response Format​

Related Tools​