Contact's Colleagues

Overview

The Contacts Colleagues signal maps a prospect's current colleagues and team members at their company. In B2B sales, deals are rarely won through a single contact — multi-threading (building relationships with multiple stakeholders) dramatically increases close rates and deal velocity. This signal gives your team the data to multi-thread effectively.

Autobound analyzes LinkedIn company membership data to identify colleagues of your target contacts. For each colleague, we capture their name, title, seniority level, department, LinkedIn URL, and email when available. The pipeline groups colleagues by department and seniority, making it easy to identify the full buying committee around a prospect.

The data is enriched with department classification (sales, marketing, engineering, finance, etc.) and seniority levels (entry through C-level), allowing you to quickly navigate an organization's hierarchy. If you're working a deal with a VP of Marketing, this signal helps you find the CMO above them, the directors alongside them, and the managers below them.

This signal is particularly valuable when combined with other Autobound signals. For example, if you detect a Job Change for a VP of Engineering, you can immediately pull their new colleagues to identify the CTO, other engineering leaders, and the procurement team — building your account map before your first meeting.

Available Subtypes

Signal	Subtype Enum	Description
Current Colleague	`colleague`	A person who currently works at the same company as the target contact
Same Department	`sameDepartmentColleague`	A colleague in the same department as the target contact
Direct Hierarchy	`hierarchyColleague`	A colleague who appears to be in the target contact's direct reporting chain (above or below)

Schema

{
  "signal_id": "d4e5f6a7-8901-4b2c-def0-123456789abc",
  "signal_type": "contacts-colleagues",
  "signal_subtype": "colleague",
  "detected_at": "2026-03-18T10:30:15.442Z",
  "association": "contact",
  "contact": {
    "name": "Sarah Kim",
    "first_name": "Sarah",
    "last_name": "Kim",
    "email": "[email protected]",
    "job_title": "VP of Marketing",
    "seniority_level": "vp",
    "department": "marketing",
    "linkedin_url": "linkedin.com/in/sarah-kim-marketing",
    "city": "San Francisco",
    "state": "California",
    "country": "US"
  },
  "company": {
    "name": "Acme Corp",
    "domain": "acmecorp.com",
    "linkedin_url": "linkedin.com/company/acme-corp",
    "description": "Acme Corp builds enterprise workflow automation software for operations teams.",
    "industries": ["Software Development"],
    "employee_count_low": 501,
    "employee_count_high": 1000
  },
  "data": {
    "colleagues": [
      {
        "name": "Michael Torres",
        "first_name": "Michael",
        "last_name": "Torres",
        "email": "[email protected]",
        "job_title": "Chief Marketing Officer",
        "seniority_level": "clevel",
        "department": "marketing",
        "linkedin_url": "linkedin.com/in/michael-torres-cmo",
        "city": "San Francisco",
        "state": "California",
        "country": "US",
        "relationship_to_contact": "likely_superior",
        "confidence": 0.85
      },
      {
        "name": "Jennifer Liu",
        "first_name": "Jennifer",
        "last_name": "Liu",
        "email": "[email protected]",
        "job_title": "Director of Demand Generation",
        "seniority_level": "director",
        "department": "marketing",
        "linkedin_url": "linkedin.com/in/jennifer-liu-demandgen",
        "city": "New York",
        "state": "New York",
        "country": "US",
        "relationship_to_contact": "peer",
        "confidence": 0.75
      },
      {
        "name": "David Patel",
        "first_name": "David",
        "last_name": "Patel",
        "email": "[email protected]",
        "job_title": "VP of Sales",
        "seniority_level": "vp",
        "department": "sales",
        "linkedin_url": "linkedin.com/in/david-patel-sales",
        "city": "Austin",
        "state": "Texas",
        "country": "US",
        "relationship_to_contact": "cross_functional_peer",
        "confidence": 0.70
      },
      {
        "name": "Amanda Brooks",
        "first_name": "Amanda",
        "last_name": "Brooks",
        "email": "[email protected]",
        "job_title": "Marketing Operations Manager",
        "seniority_level": "manager",
        "department": "marketing",
        "linkedin_url": "linkedin.com/in/amanda-brooks-mops",
        "city": "San Francisco",
        "state": "California",
        "country": "US",
        "relationship_to_contact": "likely_report",
        "confidence": 0.80
      }
    ],
    "total_colleagues_found": 47,
    "colleagues_returned": 4,
    "departments_represented": ["marketing", "sales"],
    "seniority_distribution": {
      "clevel": 1,
      "vp": 1,
      "director": 1,
      "manager": 1
    }
  }
}

Example: Engineering Team Map

This example shows colleague data for an engineering leader, useful for selling developer tools or infrastructure products.

{
  "signal_id": "e5f6a7b8-9012-4c3d-ef01-23456789abcd",
  "signal_type": "contacts-colleagues",
  "signal_subtype": "sameDepartmentColleague",
  "detected_at": "2026-03-12T08:45:33.198Z",
  "association": "contact",
  "contact": {
    "name": "Ryan Nakamura",
    "first_name": "Ryan",
    "last_name": "Nakamura",
    "email": "[email protected]",
    "job_title": "VP of Engineering",
    "seniority_level": "vp",
    "department": "engineering",
    "linkedin_url": "linkedin.com/in/ryan-nakamura-eng",
    "city": "Seattle",
    "state": "Washington",
    "country": "US"
  },
  "company": {
    "name": "DataVault",
    "domain": "datavault.io",
    "linkedin_url": "linkedin.com/company/datavault",
    "description": "DataVault provides cloud-native data warehousing and analytics infrastructure.",
    "industries": ["Software Development"],
    "employee_count_low": 201,
    "employee_count_high": 500
  },
  "data": {
    "colleagues": [
      {
        "name": "Lisa Chang",
        "first_name": "Lisa",
        "last_name": "Chang",
        "email": "[email protected]",
        "job_title": "CTO",
        "seniority_level": "clevel",
        "department": "engineering",
        "linkedin_url": "linkedin.com/in/lisa-chang-cto",
        "city": "Seattle",
        "state": "Washington",
        "country": "US",
        "relationship_to_contact": "likely_superior",
        "confidence": 0.90
      },
      {
        "name": "Carlos Mendez",
        "first_name": "Carlos",
        "last_name": "Mendez",
        "email": "[email protected]",
        "job_title": "Director of Platform Engineering",
        "seniority_level": "director",
        "department": "engineering",
        "linkedin_url": "linkedin.com/in/carlos-mendez-platform",
        "city": "Seattle",
        "state": "Washington",
        "country": "US",
        "relationship_to_contact": "likely_report",
        "confidence": 0.82
      },
      {
        "name": "Priya Sharma",
        "first_name": "Priya",
        "last_name": "Sharma",
        "email": "[email protected]",
        "job_title": "Director of Security Engineering",
        "seniority_level": "director",
        "department": "engineering",
        "linkedin_url": "linkedin.com/in/priya-sharma-security",
        "city": "San Francisco",
        "state": "California",
        "country": "US",
        "relationship_to_contact": "likely_report",
        "confidence": 0.78
      }
    ],
    "total_colleagues_found": 89,
    "colleagues_returned": 3,
    "departments_represented": ["engineering"],
    "seniority_distribution": {
      "clevel": 1,
      "director": 2
    }
  }
}

Field Reference

Core Fields

Field	Type	Required	Description
`signal_id`	string (UUID)	✓	Unique identifier for this signal instance
`signal_type`	string	✓	Always `"contacts-colleagues"`
`signal_subtype`	string	✓	Subtype: `colleague`, `sameDepartmentColleague`, or `hierarchyColleague`
`detected_at`	string (ISO 8601)	✓	Timestamp when colleagues were identified
`association`	string	✓	Always `"contact"` — this is a contact-level signal

Contact Object

The contact is the target prospect whose colleagues are being mapped.

Field	Type	Required	Description
`contact.name`	string	✓	Full name of the target prospect
`contact.first_name`	string		First name
`contact.last_name`	string		Last name
`contact.email`	string (nullable)		Email address
`contact.job_title`	string		Current job title
`contact.seniority_level`	string		Seniority level: `entry`, `individual_contributor`, `manager`, `director`, `vp`, `clevel`
`contact.department`	string (nullable)		Department classification
`contact.linkedin_url`	string	✓	LinkedIn profile URL
`contact.city`	string (nullable)		City
`contact.state`	string (nullable)		State or region
`contact.country`	string (nullable)		Country code (ISO 3166-1 alpha-2)

Company Object

Field	Type	Required	Description
`company.name`	string	✓	Company name
`company.domain`	string		Primary website domain
`company.linkedin_url`	string		LinkedIn company page URL
`company.description`	string		Brief company description
`company.industries`	array[string]		Industry classifications
`company.employee_count_low`	integer		Lower bound of employee count estimate
`company.employee_count_high`	integer		Upper bound of employee count estimate

Data Object — Colleague Records

Each colleague in the data.colleagues array contains:

Field	Type	Required	Description
`colleagues[].name`	string	✓	Full name of the colleague
`colleagues[].first_name`	string		First name
`colleagues[].last_name`	string		Last name
`colleagues[].email`	string (nullable)		Email address. May be null — use Resolution to enrich
`colleagues[].job_title`	string		Current job title
`colleagues[].seniority_level`	string		Seniority level: `entry`, `individual_contributor`, `manager`, `director`, `vp`, `clevel`
`colleagues[].department`	string (nullable)		Department classification
`colleagues[].linkedin_url`	string	✓	LinkedIn profile URL
`colleagues[].city`	string (nullable)		City
`colleagues[].state`	string (nullable)		State or region
`colleagues[].country`	string (nullable)		Country code
`colleagues[].relationship_to_contact`	string		Inferred relationship: `likely_superior` (higher in the org chart), `peer` (same level, same department), `cross_functional_peer` (same level, different department), `likely_report` (lower in the org chart)
`colleagues[].confidence`	float (0.0-1.0)		Confidence in the relationship inference

Data Object — Summary Fields

Field	Type	Description
`data.total_colleagues_found`	integer	Total number of colleagues identified at the company
`data.colleagues_returned`	integer	Number of colleagues included in this signal (top results by relevance)
`data.departments_represented`	array[string]	List of departments represented in the returned colleagues
`data.seniority_distribution`	object	Count of colleagues by seniority level in the returned set

Data Activation

Timing

Colleague data reflects the current state of a company's org. People change roles and leave companies regularly, so this data is most accurate within 30 days of detected_at. For long sales cycles, request a refresh of colleague data before key meetings.

Uniqueness

One signal per target contact. The signal contains an array of colleagues, not one signal per colleague. If you need colleagues for multiple contacts at the same company, each contact gets their own signal.

Delivery

GCS Bucket: autobound-contacts-colleagues
File Format: JSONL (one signal per line) + Parquet
Folder Structure: Timestamped delivery folders
Refresh Cadence: Weekly

Each delivery arrives in a timestamped folder. Treat all signals in a new folder as current.

API Usage

Generate Content API

{
  "enabledInsights": ["colleague", "sameDepartmentColleague"],
  "disabledInsights": []
}

Generate Insights API

{
  "insightSubtype": "colleague"
}

Example Output

"I've been chatting with Jennifer on the demand gen side about how we're helping teams like yours streamline campaign attribution. Since you're overseeing the broader marketing strategy, wanted to loop you in — would a quick 15 min make sense this week?"

Use Cases

Use Case	How to Apply
Multi-threading	Identify 3-5 stakeholders across the buying committee. Engage them in parallel to build consensus and accelerate the deal
Top-down selling	Find the C-level or VP above your champion. Use them as an executive sponsor to fast-track procurement
Bottom-up adoption	Find the managers and ICs who will actually use your product. Build grassroots support before the executive conversation
Cross-functional alignment	Use `cross_functional_peer` relationships to identify stakeholders in adjacent departments (e.g., the VP of Sales alongside your VP of Marketing contact)
Account mapping	Combine with CRM data to visualize the full org chart and identify coverage gaps in your account
New account entry	When a Job Change signal fires, immediately pull colleagues to build your account map before the first meeting

Coverage

Refresh: Weekly
Coverage: Millions of companies via LinkedIn organizational data
Best for: Enterprise sales, multi-threaded deal execution, ABM, account mapping, strategic selling

Signal Schema — Full schema reference for all Autobound signals
Resolution — How contact and company data is resolved and enriched
Signal Catalog — Browse all available signal types
Shared Previous Employer — Find shared work history for rapport-building
Job Change — Detect when contacts change jobs — then pull their new colleagues