An automated sales call analyzer fixes the note-taking problem at the source: it transcribes every call, scores qualification criteria, extracts objections, and pushes structured data into your CRM without a human touching it. By combining n8n's workflow automation with Gladia's async audio intelligence API, you can ship a multilingual, CRM-integrated call analyzer in under a day. This guide provides the exact architecture and n8n workflow structure to build a pipeline that transcribes, scores, and syncs sales calls directly to your CRM.
Sales call analyzer: architecture and workflow
An automated sales call analyzer ingests a recorded sales call, extracts structured intelligence (BANT scores, objections, competitor mentions), and pushes that data into the systems your team already uses. Reps stop logging calls manually, managers get consistent qualification scores across every deal, and coaching is based on what reps actually said, not what managers remember.
Sales call analyzer workflow
The pipeline follows six stages:
- Trigger: A new call recording becomes available via Google Drive, a Zoom webhook, or a dialer webhook.
- Transcription: n8n sends the audio URL to Gladia's async API with diarization and language detection enabled.
- Webhook delivery: Gladia processes the audio and POSTs the diarized JSON transcript to your n8n webhook URL.
- LLM analysis and CRM formatting: An OpenAI or Claude node scores BANT criteria, extracts objections, and formats the output as CRM-ready JSON.
- CRM update: A HubSpot or Salesforce node maps the structured output to custom deal and contact fields.
- Slack alert: A conditional Slack node notifies the sales team when a call scores above your qualification threshold.
Preventing multilingual accuracy regressions
For global sales teams, transcription failure is the silent data corruption problem. When a rep in Singapore switches between English and Mandarin mid-call, or a prospect in the Netherlands speaks Dutch-accented English, most STT APIs either fail silently or return garbled output. Every wrong name, wrong number, or missed phrase that enters the LLM prompt produces a corrupt BANT score and a wrong CRM entry. Gladia's Solaria-1 is designed to handle accented speech and code-switching, which is why teams processing multilingual call volumes choose it as the transcription layer.
Quick start: Gladia + n8n setup
Before building, confirm these four components are ready:
- n8n instance: Self-hosted or cloud, with an accessible public webhook URL.
- Gladia account: Free tier includes 10 hours of transcription for testing. Generate your API key from the Gladia dashboard.
- CRM API access: HubSpot OAuth2 credentials or a Salesforce connected app.
- LLM API key: OpenAI or Anthropic. Any model with structured JSON output works.
Configure Gladia for accurate call transcription
Gladia's async API is built for post-call analysis. Processing the complete recording enables higher accuracy, returning a diarized transcript with speaker labels, word-level timestamps, named entities, and sentiment scores.
Generate your Gladia API key
- Log in to your Gladia dashboard.
- Navigate to Settings > API Keys and click Create New Key.
- In n8n, create a Header Auth credential with header name
x-gladia-key and paste your key as the value.
Gladia async API: polling vs. webhooks
A webhook-based architecture is event-driven: you submit the transcription job, return control to n8n immediately, and Gladia POSTs the completed result to your webhook URL the moment processing finishes. This eliminates unnecessary polling API calls and handles variable processing times naturally across different call lengths. The Gladia n8n integration guide walks through the exact HTTP Request node configuration, including setting webhook_url in your POST body.
Diarization for reliable BANT scoring
Speaker diarization is what makes the transcript useful for BANT scoring. Without it, the LLM cannot distinguish rep statements from prospect statements, which produces unreliable qualification scores. The JSON output assigns a speaker index to each utterance, with start and end timestamps, confidence scores, and the detected language. See the full diarization documentation for configuration options.
A typical diarized utterance looks like this:
{
"speaker": 0,
"start": 0.73,
"end": 2.36,
"confidence": 0.92,
"text": "We have budget approved for next quarter.",
"language": "en"
}
Speakers are assigned indexes by order of appearance. The first speaker to talk becomes speaker 0, the second becomes speaker 1, and so on. That mapping is consistent across the entire transcript, giving the LLM clean attribution to work with.
Activate your 10-hour free trial
Start with 10 free hours to run a test call through the full pipeline before committing to a paid plan.
Designing the n8n call analyzer logic
Step 1: configure the call recording trigger
The first node depends on where recordings land. For Zoom, use n8n's Webhook node to catch recording.completedrecording completion events, which typically include a download URL for the audio file. For Google Drive recordings, use n8n's Google Drive trigger watching a specific folder. Either way, extract a publicly accessible audio URL that Gladia can fetch directly.
Step 2: send audio to Gladia's async API
Add an HTTP Request node with the following configuration (the audio_url references the URL extracted in Step 1):
- Method: POST
- URL:
https://api.gladia.io/v2/transcription/Gladia's async transcription endpoint (see API reference) - Headers:
x-gladia-key: [your-key], Content-Type: application/json - Body:
{
"audio_url": "{{ $json.audioUrl }}",
"diarization": true,
"language_behaviour": "automatic single language",
"webhook_url": "https://your-n8n-instance.com/webhook/gladia-callback"
}
Setting language_behaviour to "automatic single language" detects the dominant language and automatically translates segments spoken in secondary languages into the dominant language. For calls where you need to preserve multiple languages in the transcript, use code-switching mode instead. The full parameter reference is in the Gladia API docs.
Step 3: receive Gladia's API results
Add a Webhook node as the entry point for Gladia's callback. Set the HTTP method to POST and copy the production URL. Paste this URL as the webhook_url value in your Step 2 HTTP Request node. When Gladia finishes processing, it POSTs the full result to this endpoint. The n8n Webhook node documentation covers how to configure response behavior and how n8n structures the incoming payload.
Step 4: format the transcript for LLM analysis
Add a Code node to format the diarized utterances into a labeled transcript string for the LLM prompt:
const utterances = $json.body.result.prediction;
let transcript = '';
utterances.forEach(u => {
const label = u.speaker === 0 ? 'Rep' : 'Prospect';
transcript += `[${u.start.toFixed(1)}s] ${label}: ${u.text}\n`;
});
return [{ transcript }];
LLM analysis: BANT scoring and objection extraction
Optimizing LLM prompts for BANT scores
Use a system prompt that constrains the model to return only JSON, which prevents free-text responses that break downstream CRM mapping:
You are a B2B sales qualification analyst. Analyze this transcript and return
a JSON object:
{
"bant_budget": { "amount": "value extracted from conversation", "confidence": "high|medium|low" },
"bant_authority": { "decision_maker": "string", "confidence": "high|medium|low" },
"bant_need": { "primary_problem": "string", "priority": "critical|high|medium|low" },
"bant_timeline": { "timeframe": "string", "urgency": "immediate|high|medium|low" },
"overall_bant_score": 0-100,"numeric score",
"qualification_summary": "string",
"next_steps": ["string"],
"prospect_company": "string"
}
Mark "high confidence" only when the prospect explicitly confirmed the criterion.
TRANSCRIPT:
{{ $json.transcript }}
The Attention x Gladia case study (Attention is an AI sales coaching platform) shows how this pattern powers CRM population, coaching scorecards, and conversation intelligence in production.
Pinpointing sales call obstacles
Add a second LLM call to extract objections and competitor mentions. Ask the model to return verbatim_quote fields pulled directly from the transcript rather than paraphrased summaries, with objection_type, severity, and response_strategy fields. Verbatim quotes preserve the context that makes an objection useful for rep coaching.
CRM-ready sales call insights
Before the CRM node, add a Set node to enforce a strict output schema, mapping the LLM's raw output to the exact field names your HubSpot or Salesforce properties expect. This decouples the LLM output format from the CRM schema so you can swap LLM providers without updating the CRM node configuration.
Integrate call insights directly into CRM
Mapping sales call data to HubSpot
Add a HubSpot node, select the Deal resource, and choose Update as the operation. Map to these HubSpot custom properties: bant_overall_score, bant_budget, bant_authority, bant_need, bant_timeline, objections_summary, competitors_mentioned, call_recording_link. The n8n HubSpot node documentation covers OAuth2 setup and all available field operations.
Configure Salesforce for call insights
For Salesforce, swap the HubSpot node for n8n's Salesforce node and target the Opportunity object. Authenticate via OAuth2 with a connected app in your Salesforce org, then map bant_overall_score__c and related custom bant_overall_score >= 70fields to match your Salesforce schema.
Handling API rate limits and failures
Add an Error Trigger node to catch failures from the HubSpot or Salesforce update step. Route failures to a Slack message with the call ID and error message so a team member can manually review and retry. For high-volume batch processing, add a Wait node between CRM updates to stay within HubSpot's API rate limits.
Configure Slack alerts for call insights
When to send Slack alerts
Add an IF node before the Slack step with a condition such as bant_overall_score > 75 above your qualification threshold for high-priority notifications. Add a second branch for competitor mentions: if competitors_mentioned is not empty, route to a separate channel regardless of BANT score. See the n8n Slack node docs for block kit formatting options.
How to format Slack alerts for fast rep response
Format the Slack message to include direct links to the CRM record and the call recording:
🎯 *Qualified Lead: {{ $json.prospect_company }}*
BANT Score: {{ $json.bant_overall_score }}/100
Need: {{ $json.bant_need.primary_problem }}
Timeline: {{ $json.bant_timeline.timeframe }}
<{{ $json.recording_url }}|Play Recording>
Reusable n8n workflow for call analysis
Quickly import the n8n workflow JSON
The complete importable workflow covers all six stages: trigger, Gladia API call, webhook, transcript formatting, LLM analysis, and CRM update. To import: open n8n, click "Workflows" > "Add Workflow" > "Import from JSON", and paste the exported JSON.
n8n workflow environment setup
After import, configure four credentials in Settings > Credentials following the n8n credentials guide:
- Gladia: Header Auth, header name
x-gladia-key, value from your dashboard. - HubSpot: Select the predefined HubSpot type and complete the OAuth2 flow.
- OpenAI: API Key type, paste your key from platform.openai.com.
- Slack: Select the predefined Slack type and authorize in your workspace.
In the Gladia HTTP Request node, set the webhook_url field to the production URL from your n8n Webhook node.
Run end-to-end workflow tests
Upload a sample call recording to your trigger source, activate the workflow in test mode, and trace each node's output panel. Verify that the Gladia webhook fires, the Code node produces a correctly labeled transcript, the LLM returns valid JSON, and the HubSpot node completes successfully. Use Gladia's async transcription quickstart to generate a sample result for dry-run testing without a live call.
From build to business value: your next steps
Budgeting for large-scale AI analysis
Per-hour pricing removes the projection uncertainty that per-seat SaaS models introduce. The Growth plan rate of $0.20/hr applies with an upfront commitment. Here is what the numbers look like at realistic call volumes using Gladia's public pricing:
| Monthly Volume |
Gladia Growth (from $0.20/hr) |
Gladia Starter ($0.61/hr) |
Gong (50 seats, est.) |
| 100 hrs |
from $20 |
$61 |
~$7,100+/mo (seat-based) |
| 1,000 hrs |
from $200 |
$610 |
~$7,100+/mo (seat-based) |
| 10,000 hrs |
from $2,000 |
$6,100 |
~$10,800+/mo (seat-based) |
Gong charges per recorded user seat, not per call volume. Per Vendr transaction data, mid-market seat costs commonly fall between $1,200 and $2,400 annually, with 50-99 user deployments ranging from $1,520 to $2,400 per seat annually depending on tier. A 50-person sales team typically pays roughly $85,000 to $130,000 annually ($7,100 to $10,800 per month) including platform fees and negotiated rates. The Growth base rate includes all features (diarization, NER, sentiment, translation) with no add-on fees.
Preventing multilingual regressions
A wrong name or missed budget figure in a multilingual call corrupts the CRM record, skews the coaching score, and produces a misleading BANT summary. For teams serving European or Southeast Asian markets, Gladia covers 42 languages no other API-level competitor supports, including Tagalog, Bengali, Punjabi, Tamil, and Marathi. Across those languages and more, Solaria-1 delivers on average 29% lower WER on conversational speech and 3x lower DER than alternatives, benchmarked across 8 providers and 74+ hours of audio. See the full async benchmark methodology for dataset and scoring details.
Handling workflow failures in production
Gladia maintains 99.9%+ uptime and processes calls without requiring pre-provisioning or capacity forecasting. On Growth and Enterprise plans, your audio is never used to retrain models and no opt-out action is required. If a webhook delivery fails, implement a retry using n8n's Error Trigger node. For compliance documentation covering GDPR, SOC 2 Type II, ISO 27001, and HIPAA, see the compliance hub.
Validating your automated call analyzer
Gladia's supported diarization languages
Speaker diarization is designed to detect, segment, and label speakers across audio regardless of language. Diarization is available exclusively in async workflows, where it works with Gladia's full 100+ language transcription catalog. This matters for contact center teams where diarization and multilingual accuracy must both be reliable in the same call. Diarization is not available in real-time workflows.
Swapping LLM providers in the analyzer?
Because the LLM is a separate n8n node receiving plain text and returning JSON, you can swap OpenAI for Claude, Gemini, or a self-hosted Llama instance by changing one node. The same applies to the CRM: swap HubSpot for Salesforce, Pipedrive, or any CRM with an API by replacing a single node. Off-the-shelf platforms like Gong lock you into their extraction logic and integration set. This build gives you full control over the prompt, the model, and the destination.
Call recording data lifecycle
Data governance is tier-specific. On the Starter plan, your audio can be used for model training by default. On Growth and Enterprise plans, your data is never used for model training and no opt-out is required. This is the default behavior, verified in the DPA. PII redaction is an optional feature that must be explicitly enabled in the API request body. It does not activate automatically on any plan. Full documentation is at the Gladia compliance hub.
Start with 10 free hours and have your integration in production in less than a day.
FAQs
What does Gladia's async API cost for 1,000 hours monthly?
On the Growth plan, 1,000 hours costs from $200/month ($0.20/hr with upfront commitment), with diarization, NER, translation, and sentiment included. On Starter, the same volume costs $610/month ($0.61/hr).
Is speaker diarization available in real-time transcription?
No. Diarization is available in async workflows only. For post-call analysis pipelines, async diarization provides higher accuracy because the model uses the full recording context.
Does Gladia train models on my sales call audio by default?
On Growth and Enterprise plans, your audio is never used for model retraining and no opt-out action is required. On the Starter plan, data can be used for training by default.
How long does it take to integrate Gladia into an n8n workflow?
Customer integrations have been completed in under 24 hours. The n8n integration guide covers the HTTP Request node configuration, and the Gladia team is available on Slack for direct support.
Can I use this pipeline with Salesforce instead of HubSpot?
Yes. Replace the HubSpot node with n8n's Salesforce node, authenticate via OAuth2, and target the Opportunity object. The LLM output schema and Set node mapping stay the same.
Does PII redaction activate automatically in the transcript?
No. PII redaction is optional and must be explicitly enabled in the API request body. It does not run by default on any plan.
Key terms glossary
WER (Word Error Rate): The percentage of words in a transcript that differ from the correct reference, calculated as (substitutions + deletions + insertions) / total reference words. Lower WER means higher accuracy.
DER (Diarization Error Rate): The percentage of audio time incorrectly attributed to a speaker, measuring how accurately the system separates who said what.
Speaker diarization: The process of segmenting an audio recording by speaker identity, assigning a consistent label to each utterance across the full recording. Async-only in Gladia.
Code-switching: Mid-conversation language changes where a speaker shifts from one language to another within a single conversation. Most STT APIs fail silently on this.
BANT: A sales qualification framework covering Budget, Authority, Need, and Timeline, used to score whether a prospect is likely to buy within a defined window.
Async transcription: Audio processing that runs on the complete recording after it finishes, rather than streaming in real time. Async produces higher accuracy and supports diarization because the model has full context.
Webhook: An HTTP callback that sends data to a specified URL when an event occurs. In this pipeline, Gladia POSTs the completed transcript to your n8n webhook URL instead of waiting to be polled.
