Company Install Time Series

Track how a company's technology adoption changes over time: returns a monthly installation-intensity time series per product for one company (by domain). Use this when the question is about a TREND over time — adoption growth, decline, or churn — e.g. 'How has Cisco's usage of Snowflake changed over the past 2 years?' or 'Is company X ramping up or winding down its AWS footprint?' Do NOT use this when you want a point-in-time answer: for the company's CURRENT installed tech stack use company_technographic (snapshot); for department/role usage use company_fai; for dollar spend use company_spend. Each data_points[].intensity is an integer 1-31 = the number of days the product was detected that month (null = no detection that month). current_intensity is a separate aggregate integer from global install data and is NOT on the 1-31 daily scale — use intensity_momentum (positive = growing, negative = declining; magnitude is meaningful) for trend analysis rather than comparing raw intensity values. IMPORTANT: The most-recent data point is typically null because the current month is incomplete; the penultimate point may also be partial if queried early in a new month — treat it as provisional. BEFORE filtering, resolve exact canonical names and numeric IDs first — get_vendor_information for vendor names, get_product_category for category names, product_search_and_enrich for product names and numeric productIds. Filter values that don't match exact canonical names return products: [] with HTTP 200 and 0 credits — indistinguishable from a genuine no-data result. Unlike company_technographic, this tool emits a warning field whenever filters were provided but nothing matched, explaining the miss and how to resolve it. 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
`companyDomain` Required	string	-	The company domain to look up (e.g., 'cisco.com'). Required. Protocol prefixes (http://, https://), leading www., and trailing paths/queries/fragments are accepted and stripped automatically; case is normalized.
`products`	array	-	Optional. Restrict the series to specific products by name (e.g., ['Snowflake Platform', 'Databricks']). Values must be EXACT canonical product names — a near-match ('Snowflake' vs 'Snowflake Platform') silently returns no products. Resolve names with product_search_and_enrich first, or prefer productIds. Omit to return the company's top products by intensity.
`productIds`	array	-	Optional. Restrict the series to specific products by ID. Must be numeric HG product IDs (e.g. '26434') — slug-style IDs silently return nothing. The most reliable filter: obtain the numeric ID from product_search_and_enrich, then pass it here instead of a product name.
`vendors`	array	-	Optional. Restrict the series to products from specific vendors. Values must be EXACT canonical vendor names (e.g. 'Microsoft Corporation', not 'Microsoft') — a short/informal name silently returns no products. Resolve the canonical name with get_vendor_information before filtering.
`categories`	array	-	Optional. Restrict the series to products in specific categories. Values must be EXACT canonical category names (e.g. 'Infrastructure-as-a-Service (IaaS)', not 'Cloud Infrastructure') — a paraphrase silently returns no products. Resolve the canonical name with get_product_category before filtering.
`timeRange`	string	`last_24_months`	How far back the monthly series extends. Options: last_6_months, last_12_months, last_24_months, last_36_months. Default: last_24_months. Pick a longer range for slow-moving adoption/churn trends, a shorter one for recent momentum. 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).
`maxProducts`	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 Phoenix Integrations settings:

HG Insights (v2) (hginsights_v2)

Use Cases

How has a company's usage of a product trended over the past N months? — filter by product/productIds
Is a company ramping up or winding down a specific vendor's footprint? — read intensity_momentum after a vendors 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 — categories filter, sort by intensity_momentum
Confirm whether a product's decline is recent or long-running — widen timeRange to last_36_months

Example Usage

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

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

Snowflake usage trend at Cisco over the last 12 months

{
  "tool": "company_install_time_series",
  "arguments": {
    "companyDomain": "cisco.com",
    "products": [
      "Snowflake Platform"
    ],
    "timeRange": "last_12_months"
  }
}

Microsoft-vendor footprint momentum at Cisco over 36 months

{
  "tool": "company_install_time_series",
  "arguments": {
    "companyDomain": "cisco.com",
    "vendors": [
      "Microsoft Corporation"
    ],
    "timeRange": "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 domain match (0.0-1.0)
`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[].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​