The Company Bridge Pattern: Solving HubSpot's Cross-Object Problem

HubSpot custom objects are powerful for data modelling. You can create Battlecard objects, Customer Intelligence records, Product Marketing Engines, and Partnership Intelligence systems. But here's the problem: they're second-class citizens in HubSpot's automation architecture.

Try to build a workflow that says "When Battlecard object is updated, update associated Deal", and you'll discover: custom objects can't directly enrol in workflows based on their own property changes.

Try to create logic that says "When Deal moves to stage X, create or update associated custom object", and you'll discover: workflows can't directly manipulate custom objects.

The result? Intelligence systems that require manual updates, data synchronisation that happens through exports/imports, and automation gaps that force product operations teams into endless workarounds.

THE THREE WORKFLOW LIMITATIONS

Understanding why custom objects struggle in HubSpot requires understanding three fundamental limitations:

Limitation 1: Enrolment Triggers

Standard Objects (Contact, Company, Deal, Ticket): Can enrol in workflows based on property changes, form submissions, email engagement, page views, list membership, and dozens of other triggers.

Custom Objects: Can only enrol based on:

• Record creation
• Manual enrolment
• List membership (with significant limitations)

What This Means: You can't create a workflow that says "When custom object property X changes to Y, do Z." Property-based enrolment—the foundation of most automation—doesn't exist for custom objects.

Real-World Impact: You build a Competitive Intelligence system with a Battlecard custom object. When the "Win Rate vs Competitor X" property updates (triggered by closed-won Deal analysis), you want to alert sales leadership if the win rate drops below 40%.

With standard objects: Simple workflow with property trigger.

With custom objects: Not possible directly. The property update happens in a vacuum.

Limitation 2: Cross-Object Actions

Standard Objects: Workflows can create, update, and associate records across standard objects. "When Deal closes, create Company, create Contact, update both with Deal properties" works perfectly.

Custom Objects: Workflows cannot directly create or update custom object records. You can associate existing custom objects, but you can't manipulate their properties or create new records via workflow actions.

What This Means: Intelligence systems that need to flow data from Deals → Custom Objects → Back to Deals hit a wall. You can read custom object data in deal views, but you can't automate the writes.

Real-World Impact: You have a Customer Intelligence custom object that aggregates product usage, support interactions, and engagement data. When a Deal closes-won, you want to automatically create a Customer Intelligence record pre-populated with Deal properties (segment, use case, initial ARR).

With standard objects: Workflow creates and populates a record.

With custom objects: Manual creation required or complex API workarounds.

Limitation 3: Association Logic

Standard Objects: Rich association logic exists. "If Contact is associated with Company AND Company is associated with Deal, then..." works natively.

Custom Objects: Association-based logic is limited. You can filter by "has association to object type" but can't easily trigger workflows when associations are created/removed or use association properties in conditions.

What This Means: Intelligence systems that depend on association relationships (e.g., "Which Battlecards are associated with active Deals?") struggle to maintain data currency through automation.

Real-World Impact: You want to automatically update a "Currently in Active Deals" counter on Battlecard objects whenever Deals associated with that competitor move to/from active stages.

With standard objects: Association-triggered workflow updates counter. With custom objects: Counter goes stale, requires manual refresh.

WHY THIS MATTERS FOR INTELLIGENCE SYSTEMS

If you're just using custom objects for simple data storage—logging activities, maintaining reference lists—these limitations are annoying but manageable.

But if you're building intelligence systems that require:

• Data flowing automatically between objects
• Triggered actions based on intelligence patterns
• Cross-object analysis and aggregation
• Real-time currency of derived properties

Then these limitations are architectural blockers.

Example Intelligence System Broken by Limitations:

Competitive Intelligence Operating System:

• Battlecard custom object (90 properties)
• Associated with Company (which competitor they use/evaluate)
• Associated with Deal (which competitors appear in deals)
• Intelligence properties: Win rate vs competitor, avg deal cycle, common objections

Required Automation:

1. When Deal with Competitor X closes (won/lost), update Battlecard Win Rate
2. When the Battlecard Win Rate drops below the threshold, alert product marketing
3. When Deal enters stage "Competitive Evaluation", surface relevant Battlecard
4. When a new Company indicates Competitor X usage (technographic data), create/update the association

With Standard Objects Only: All four automations: straightforward workflows.

With Custom Objects:

• Automation 1: Blocked (can't update custom object from Deal workflow)
• Automation 2: Blocked (can't trigger on custom object property change)
• Automation 3: Partially possible (can read, can't automatically refresh)
• Automation 4: Blocked (can't create associations via workflow)

Result: Intelligence system becomes glorified spreadsheet. Data exists, but doesn't flow. Insights emerge but don't trigger action.

THE COMPANY BRIDGE PATTERN: OVERVIEW

Here's the fundamental insight that unlocks cross-object automation:

The Company object is universally connected and workflow-capable.

Every intelligence system involves Companies:

• Competitive intelligence: Which competitors does Company evaluate?
• Customer intelligence: Which Companies are customers?
• Partnership intelligence: Which partners does Company work with?
• Product marketing intelligence: Which market segments is the Company in?

The Company object:

• Can enrol in workflows based on property changes
• Can be updated by workflows, triggered from Deals, Contacts, and Tickets
• Has rich association logic
• Can trigger downstream workflows

Bridge Pattern Principle: Rather than trying to make custom objects workflow-aware directly, make them workflow-aware through the Company object.

Use Company as the intermediary layer that:

1. Receives signals from custom objects (via associations)
2. Processes logic in workflows (Company is workflow-capable)
3. Sends signals back to custom objects (via association updates)

Think of the Company as a "hub" in a hub-and-spoke architecture; all signals route through it.

PATTERN COMPONENTS (ABSTRACTED)

Component 1: Property Mirroring

Critical intelligence properties that exist on custom objects should be mirrored to the associated Company records.

Example:

• The Battlecard object has "Win_Rate_vs_Competitor_X" property
• The Associated Company has "CI_Win_Rate_vs_Competitor_X" property (mirror)
• When custom object updates (via API, manual edit, or batch), trigger updates mirrored the Company property
• Company property change can now trigger workflows

Why This Works: You've made the custom object property "workflow-aware" by creating a workflow-triggerable proxy on Company.

Component 2: Association-Triggered Updates

Use association creation/removal as signals to trigger Company property updates, which then trigger workflows.

Example:

• When the Battlecard is associated with the Company, set the Company property "Has_Active_Battlecard" = Yes
• This triggers workflow: "When Has_Active_Battlecard changes to Yes, check Deal stage and surface battlecard if in competitive evaluation"

Why This Works: Association changes (which are difficult to detect directly) become property changes (which are easy to detect) via the bridge.

Component 3: Bidirectional Data Flow

Intelligence often needs to flow in both directions:

• Deal → Custom Object: Deal closes, update intelligence aggregates
• Custom Object → Deal: Intelligence pattern detected, update Deal properties

Both directions route through the Company:

• Deal → Company → Custom Object (via Company property mirroring)
• Custom Object → Company → Deal (via Company as trigger point)

Why This Works: The Company sits between custom objects and standard objects, enabling "indirect" updates that wouldn't be possible directly.

WHEN TO USE THE BRIDGE PATTERN

Use When:

1. Intelligence Aggregation Required
   • You need to aggregate data from multiple objects onto custom objects
   • Example: Customer Intelligence object aggregating Deal history, support tickets, and product usage
   • Bridge enables automated aggregation via Company workflows
2. Cross-Object Triggers Needed
   • Actions on standard objects (Deal, Contact) should trigger custom object updates
   • Example: Deal stage change should update the associated Battlecard "Active Deals Count"
   • Bridge enables Deal → Company → Custom Object flow
3. Derived Properties Must Stay Current
   • Custom object has calculated properties (win rates, health scores, trend analysis)
   • These properties must update automatically, not manually
   • Bridge enables automated recalculation via workflow triggers
4. Intelligence Must Drive Action
   • Intelligence patterns detected in custom objects should trigger alerts, tasks, and notifications
   • Example: The Churn risk score in the Customer Intelligence object should create a CSM task
   • Bridge enables custom object insight → Company property → workflow action

Don't Use When:

1. Simple Reference Data
   • A custom object is just a lookup table or reference list
   • No automation or data flow required
   • The bridge adds unnecessary complexity
2. One-Way Data Flow Only
   • Data only flows into the custom object, never triggers actions
   • Example: Activity log that's write-only
   • The bridge provides no value
3. No Company Association Logic
   • The intelligence system doesn't logically connect to the Companies
   • Example: Internal process tracking unrelated to customer/prospect companies
   • The bridge pattern doesn't apply (no bridge to build)

IMPLEMENTATION COMPLEXITY CONSIDERATIONS

The Bridge Pattern isn't "simple." It introduces architectural complexity that must be justified by value.

Complexity Factors:

Property Proliferation: Mirroring custom object properties to Company creates property sprawl. A Battlecard object with 90 properties might require 15-20 mirrored properties on Company. This affects:

• Property management overhead
• Report/view complexity
• Data governance

Workflow Multiplication: Each mirrored property potentially requires:

• Workflow to update the Company property when the custom object changes
• Workflow to trigger actions when the Company property changes
• Error handling for workflow failures

A system with 5 custom objects and 10 critical properties each = 50+ workflows.

Association Management: Bridge relies on correct associations between custom objects and Companies. Broken associations = broken data flows. Requires:

• Validation logic
• Association health monitoring
• Remediation workflows

Synchronisation Lag: Data doesn't flow instantaneously. Workflow execution → property update → next workflow trigger introduces a seconds-to-minutes lag. For most intelligence systems, this is acceptable, but real-time requirements may conflict.

Maintenance Burden: Changes to the custom object schema require parallel changes to:

• Company mirrored properties
• All dependent workflows
• Reports and dashboards

Schema evolution becomes more complex.

ALTERNATIVE APPROACHES (TRADE-OFFS)

Before implementing the Bridge Pattern, consider alternatives:

Alternative 1: API-Based Automation

Use external automation (Zapier, Make, custom middleware) to directly manipulate custom objects via the HubSpot API.

Pros:

• Direct custom object manipulation (no bridge needed)
• More powerful logic is possible
• Cleaner HubSpot architecture

Cons:

• External dependency (another system to maintain)
• API rate limits (can be constraining at scale)
• Latency (API calls slower than native workflows)
• Cost (middleware/automation platform fees)

When to Use: Complex logic that exceeds workflow capabilities, or when you already have automation infrastructure.

Alternative 2: Simplified Object Model

Redesign the intelligence system to use fewer custom objects, leaning more on standard objects.

Pros:

• No bridge pattern needed
• Native workflow capabilities
• Lower complexity

Cons:

• Data model compromises (forcing intelligence into standard object structures)
• Property limits on standard objects (can hit 10,000 property limit on Company)
• Mixing operational and analytical data (muddies standard objects)

When to Use: The Intelligence system is relatively simple and doesn't require extensive custom data structures.

Alternative 3: Reporting-Only Custom Objects

Accept that custom objects will be updated via batch processes (API, imports) rather than real-time workflows. Use them for reporting/analysis, not operational automation.

Pros:

• Simplest implementation
• No bridge pattern needed
• Custom objects pure analytical layer

Cons:

• No real-time intelligence (batch update lag)
• Manual intervention is required for updates
• Intelligence can't trigger operational actions

When to Use: The Intelligence system is primarily analytical (reports, dashboards) rather than operational (alerts, task creation, deal automation).

DECISION FRAMEWORK

Should you implement the Bridge Pattern?

Answer these questions:

1. Does your intelligence system require automated data flow between custom objects and standard objects?
   • No → Bridge pattern unnecessary
   • Yes → Continue
2. Can your use case be solved with API-based automation instead?
   • Yes, and you have automation infrastructure → Consider API approach
   • No, or infrastructure doesn't exist → Continue
3. Is the intelligence operationally critical (drives actions) or analytically valuable (informs reports)?
   • Analytical only → Consider reporting-only approach
   • Operational → Continue
4. Do you have HubSpot admin capacity to maintain 20-50+ workflows and mirrored properties?
   • No → Bridge pattern will become maintenance burden
   • Yes → Bridge pattern likely appropriate
5. Will the intelligence system exist long-term (12+ months) or is it experimental?
   • Experimental → Avoid bridge complexity until validated
   • Long-term → Bridge pattern justified

Scoring: If you answered "Continue" to questions 1-3 and "Yes" to questions 4-5, the Bridge Pattern is likely appropriate despite complexity.

THE BROADER PRINCIPLE

The Company Bridge Pattern is a specific implementation of a broader principle:

When building on platforms with architectural constraints, find the "universal connector" object and route logic through it.

This principle applies beyond HubSpot:

• Salesforce: Account object as a bridge to custom objects
• Monday.com: Board items as a bridge to subitems
• Airtable: Linked record fields as bridges between bases

The pattern is: identify which object type has full automation capabilities, use it as an intermediary for objects that don't.

CONCLUSION: COMPLEXITY FOR CAPABILITY

The Company Bridge Pattern isn't elegant. It's a workaround for platform limitations. It introduces property proliferation, workflow multiplication, and maintenance complexity.

But for intelligence systems that require automated, real-time data flows between custom objects and operational processes, it's often the only native solution that works at scale.

The question isn't "Is this complex?" (it is), but rather "Is the intelligence valuable enough to justify the complexity?" (often yes).

If you're building Customer Intelligence systems, Competitive Intelligence operating systems, Product Marketing intelligence hubs, or Partnership intelligence frameworks, systems that need to detect patterns, trigger actions, and maintain currency automatically, the Bridge Pattern unlocks capabilities that would otherwise require expensive external automation or constant manual intervention.

The trade-off is complexity for capability. Choose wisely.

About ARISE GTM: We build self-updating competitive intelligence systems for B2B SaaS companies. The complete ARISE OS deploys in 1 day, is customised in 30 days to your GTM motion, and optimised over the next 2 months to market forces. It includes automated monitoring, AI-powered synthesis, and CRM-native delivery.

Last Updated: January 2026