This article provides a complete, practical walkthrough of the Splitout Code Send Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating Sales Call Intelligence: How CallForge Uses n8n to Extract and Enrich Gong Transcripts
   
   Meta Description:
   Discover how the CallForge workflow in n8n automates the extraction, transformation, and enrichment of Gong call transcripts using Salesforce, Gong.io APIs, and custom logic to prep data for AI-driven sales insights.
   
   Keywords:
   n8n workflow, Gong API, Salesforce integration, sales call processing, call transcript automation, AI sales enablement, sales ops automation, data enrichment, sales transcript processing, Gong to Notion
   
   Third-party APIs Used:
   
   1. Gong.io API – used to retrieve call transcripts and detailed call data.
   2. Salesforce API – used to fetch opportunity and account details tied to the Gong call.
   3. People Data Labs (mentioned in a sticky note, although not directly referenced in code) – used for enriching attendee data based on domain (e.g., location info, company profiling).
   
   —
   
   Article:
   
   Transforming Sales Calls into Strategic Insights: Inside the CallForge n8n Workflow
   
   In the data-driven world of modern sales operations, leveraging conversations for strategic insights is no longer optional—it's expected. But sales calls, particularly ones recorded and stored in tools like Gong, are typically unstructured and underutilized. That’s where the CallForge workflow—built within the powerful n8n automation platform—steps in to revolutionize how organizations extract value from Gong call transcripts.
   
   This article walks you through how the CallForge n8n workflow automates the ingestion, transformation, and enrichment of Gong call data, preparing it for AI-driven analysis and business intelligence applications.
   
   What Is CallForge?
   
   CallForge is a smart automation pipeline crafted in n8n designed to retrieve Gong call transcripts, enrich them with CRM data, and format the entire conversation for downstream processing—such as feeding them into an AI prompt or integrating with platforms like Notion or reporting dashboards.
   
   The goal: automate the dirty work of transcript parsing and context mapping, enabling sales, product, and marketing teams to get insights without sifting through hours of conversation manually.
   
   Step 1: Triggering the Workflow and Fetching Gong Data
   
   The process kicks off with the Execute Workflow Trigger node. Once triggered, the workflow immediately sets out to gather call data from Gong's API. Two HTTP Request nodes retrieve:
   
   - The transcript of the call, including speaker data and time-stamped dialogues.
   - Extensive call metadata such as topics, parties involved, public comments, and points of interest.
   
   This dual-fetch ensures a comprehensive picture of the call's content and context.
   
   Step 2: Parsing and Structuring the Transcript
   
   The retrieved Gong transcript is multilayered—organized by speaker IDs and containing arrays of sentences. Using n8n’s Set and JavaScript Code nodes, the workflow meticulously restructures the transcript:
   
   - It maps raw speaker IDs to human-readable terms like “Internal” (sales team) or “External” (customers).
   - It flattens the conversation and converts each dialogue line into a readable string, such as “Internal: Can you walk me through your current process?”
   
   This normalization step provides the foundation for feeding the clean transcript into AI models or internal tools.
   
   Step 3: Enriching with Salesforce Opportunity and Account Data
   
   The workflow doesn’t just stop at call content. It augments transcript data with valuable CRM insights by fetching:
   
   - Salesforce Opportunity data using the Gong call’s metadata.
   - Associated Account information, such as company name, number of employees, and lead source.
   
   Salesforce nodes use OAuth2 authentication to securely access this structured data, which is then merged with the call data using n8n’s Merge and Aggregate nodes.
   
   Step 4: Identifying Attendee Context and Company Domains
   
   To provide even more context, the workflow uses JMESPath queries to extract email addresses and names of call participants. It segregates attendees into “internal” and “external” groups and even calculates the likely company domain from email addresses—filtering out common domains like gmail.com or outlook.com to preserve company-specific intelligence.
   
   This step is crucial for integrations with systems like People Data Labs (as noted in the accompanying documentation), where the domain could be used to gather firmographic and geographic data for lead scoring or territory assignment.
   
   Step 5: Preparing the Final Data Blob
   
   With all data sources combined, the workflow passes through a final transformation using a “Isolate Notion Data” node. This stage strips out unnecessary fields and compiles a clean JSON object containing:
   
   - Call metadata (title, start time, URL)
   - Salesforce opportunity details
   - Segmented attendee lists
   - The fully formatted and enriched transcript
   
   This data blob is now ready to be consumed by downstream AI processes, such as generating meeting summaries, tagging competitors mentioned, or updating CRM tools like Pipedrive or Notion (as suggested in sticky notes).
   
   Why CallForge Matters
   
   Workflows like CallForge represent the next generation of sales intelligence tooling—code-free, highly customizable, and deeply integrated. By combining Gong’s unstructured call data with clean, structured Salesforce data, the n8n-based automation pipeline delivers actionable intelligence at scale.
   
   Whether it's enabling AI-powered call analysis, reducing manual notes for SDRs and AEs, or simply improving CRM hygiene, automations like CallForge empower business teams to focus on insights instead of data wrangling.
   
   Future Extensions
   
   The CallForge workflow is modular by design. Additional capabilities like sentiment analysis, People Data Labs enrichment, or integration with Slack and Notion could be layered in. As platforms like Gong and n8n expand their offerings, this workflow becomes an open-ended canvas for sales enablement innovation.
   
   Conclusion
   
   In an environment where calls are currency for context, automated workflows like CallForge are no longer a luxury—they’re a necessity. By tapping into APIs from Gong and Salesforce and orchestrating them inside n8n, CallForge provides a smart and scalable way to turn raw conversations into strategic assets.
   
   If you’re thinking of bringing more automation to your sales operations, let this workflow be your blueprint.
   
   — END —

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.