Get Your Data
Verint - How to Get Your Conversation Data
A practical guide to getting your conversation data out of Verint - covering API access, Da Vinci transcription, PII redaction, media export, and how to route structured data into your downstream systems.
What you'll learn
- What conversation data you can extract from Verint - transcripts, media files, IM transcripts, and interaction metadata
- How to access data via the Verint Business API - OAuth 2.0 authentication, Da Vinci endpoints, and pagination
- Three extraction patterns: historical backfill, incremental polling, and event-triggered
- How to connect Verint data pipelines to Zapier, n8n, and Make
- Advanced use cases - WFM-quality correlation, cross-platform benchmarking, compliance evidence chains, and agent development
Data
What Data You Can Extract From Verint
Verint captures far more than just recordings. Every interaction produces a set of structured assets that can be extracted via the Business API and Da Vinci - transcripts, media files, IM conversations, WFM schedule data, and rich interaction metadata.
Common fields teams care about
API Access
How to Get Transcripts via the Verint API
Verint exposes interactions and transcripts through its REST/HTTP Business API and Da Vinci Speech Transcription API. The workflow is: authenticate with OAuth 2.0, list interactions by date range, then fetch the transcript for each interaction ID.
Authenticate
Verint uses OAuth 2.0 with token-based authentication. Request an access token from the token endpoint using your client credentials. Pass the token as a Bearer token in the Authorization header on every subsequent request.
POST /auth/token
Content-Type: application/x-www-form-urlencoded
grant_type=client_credentials
&client_id=<your_client_id>
&client_secret=<your_client_secret>
# Response: { "access_token": "eyJ...", "expires_in": 3600 }
# Use in subsequent requests:
Authorization: Bearer <access_token>List interactions by date range
Call the Business API interactions endpoint with a filter object specifying your date range. Results are paginated - each response includes an offset and limit for fetching the next page.
GET /api/v1/interactions?startDate=2025-01-01T00:00:00Z
&endDate=2025-02-01T00:00:00Z
&offset=0&limit=100
Authorization: Bearer <access_token>
Content-Type: application/jsonThe response returns an array of interaction objects with interactionId, startTime, duration, agentId, and associated metadata. Keep paginating until the returned count is less than your limit.
Fetch the transcript
For each interaction ID, request the transcript via the Da Vinci Speech Transcription API: GET /davinci/api/v1/transcripts/{interactionId}. The response contains an array of utterances, each with a speaker ID, timestamp, confidence score, and text segment.
GET /davinci/api/v1/transcripts/INT-2025-00482 Authorization: Bearer <access_token> Content-Type: application/json
Each utterance in the response includes speakerId, startTime, endTime, confidence, and text. Reassemble into plain text by concatenating utterances, or preserve the structured format for per-speaker analysis. Optionally, apply PII redaction via the Transcription Editing API before export.
Handle rate limits and transcript availability
Rate limits
Verint enforces per-endpoint rate limits that vary by your enterprise agreement. When you receive a 429 response, back off using exponential retry logic. For bulk operations, pace requests conservatively and persist your pagination offset between runs.
Transcript timing
Da Vinci transcription is asynchronous - transcripts are not available the instant an interaction ends. Processing lag depends on queue depth and system load. Build a buffer into your extraction timing or implement a retry with exponential backoff for recently completed interactions.
Patterns
Key Extraction Flows
There are three practical patterns for getting transcripts out of Verint. The right choice depends on whether you're doing a one-off migration, running ongoing extraction, or need near real-time processing.
Backfill (Historical Export)
One-off migration of past interactions
Define your date range — typically 6–12 months of historical interactions, or all available data if migrating off Verint’s native analytics
Query the Business API interactions endpoint with startDate and endDate filters. Paginate through the full result set using offset and limit, collecting all interaction IDs
For each interaction ID, fetch the transcript via the Da Vinci Speech Transcription API. Pace requests conservatively to stay within rate limits
Optionally apply PII redaction via the Transcription Editing API before storing. Store each transcript with its interaction metadata (ID, date, agent, duration) in your data warehouse
Once the backfill completes, run your analysis pipeline against the stored data in bulk
Incremental Polling
Ongoing extraction on a schedule
Set a cron job or scheduled trigger (hourly, daily, etc.) that runs your extraction script. Refresh your OAuth 2.0 token at the start of each run
On each run, query the Business API with startDate set to your last successful poll timestamp. Paginate through new results
Fetch transcripts for any new interaction IDs via the Da Vinci API. Use the interaction ID as a deduplication key to avoid reprocessing
Route each transcript and its metadata to your downstream pipeline — analysis tool, warehouse, or automation platform
Update your stored offset / timestamp to the current run time for the next poll cycle
Event-Triggered
Near real-time on interaction completion
Configure an event notification in your Verint platform. Verint can fire events when an interaction is processed and the transcript becomes available through its notification framework
When the event fires, parse the payload to extract the interaction ID and metadata
Immediately fetch the transcript via the Da Vinci API using the interaction ID from the event
Route the transcript and metadata downstream — to your analysis pipeline, CRM updater, or automation tool
Automation
Send Verint Transcripts to Automation Tools
Once you can extract transcripts from Verint, the next step is routing them through Semarize for structured analysis and into your downstream systems. Below are end-to-end example flows - each showing the full pipeline from Verint trigger through Semarize evaluation to CRM, Slack, or database output.
Verint → Zapier → Semarize → CRM
Poll Verint for new interactions, fetch the transcript via Da Vinci, send it to Semarize for structured analysis, then write the scored output - signals, flags, and evidence - directly to your CRM.
Setup steps
Create a new Zap. Choose "Schedule by Zapier" as the trigger and set the interval to hourly. This replaces a native Verint trigger since Verint doesn’t have a native Zapier integration.
Add a "Webhooks by Zapier" Action (Custom Request) to list new interactions from Verint. Set method to GET, URL to your Verint Business API interactions endpoint, and add your Bearer token from OAuth 2.0 authentication.
Add a second "Webhooks by Zapier" Action to fetch the transcript from the Da Vinci API. Set method to GET and pass the interactionId from the previous step in the URL path.
Add a third "Webhooks by Zapier" Action. Set method to POST, URL to https://api.semarize.com/v1/runs. Add your Semarize API key as a Bearer token. In the body, set kit_code to your Kit, mode to "sync", and map the transcript text into input.transcript.
Add a Formatter step to extract individual brick values from the Semarize JSON response — quality_score, compliance_flag, empathy_score, etc.
Add a Salesforce (or HubSpot, Sheets, etc.) Action to write the extracted scores and signals to your CRM record.
Test each step end-to-end, then turn on the Zap.
Verint → n8n → Semarize → Database
Poll Verint for new interactions on a schedule, fetch transcripts via Da Vinci, send each one to Semarize for analysis, then write the structured scores and signals to your database. n8n's native loop support handles pagination and batch processing.
Setup steps
Add a Cron node as the workflow trigger. Set the interval to your desired polling frequency (hourly works well for most teams).
Add an HTTP Request node to refresh the Verint OAuth 2.0 token. Set method to POST, URL to your Verint token endpoint, and pass client_id and client_secret in the body.
Add an HTTP Request node to list new interactions from Verint. Set method to GET, URL to the Business API interactions endpoint, configure Bearer auth with the token from the previous step, and filter by startDate since last run.
Add a Split In Batches node to iterate over the returned interaction IDs. Inside the loop, add an HTTP Request node to fetch each transcript via the Da Vinci API.
Add a Code node (JavaScript) to reassemble the utterances array into a single transcript string. Join each utterance’s text, prefixed by speaker role.
Add another HTTP Request node to send the transcript to Semarize. Set method to POST, URL to https://api.semarize.com/v1/runs. Add your API key as a Bearer token. Set kit_code, mode to "sync", and map the transcript into input.transcript.
Add a Code node to extract the brick values from the Semarize response — quality_score, compliance_flag, empathy_score, evidence, confidence.
Add a Postgres (or MySQL / HTTP Request) node to write the structured output. Use interaction_id as the primary key for upserts.
Activate the workflow. Monitor the first few runs to verify Semarize responses are arriving and writing correctly.
Verint → Make → Semarize → CRM + Slack
Fetch new Verint transcripts on a schedule, send each to Semarize for structured analysis, then use a Router to branch the scored output - alert on compliance flags via Slack and write all signals to your CRM.
Setup steps
Create a new Scenario. Add a Schedule module as the trigger, set to your desired interval (15–60 minutes is typical).
Add an HTTP module to refresh the Verint OAuth 2.0 token. Set method to POST, URL to your Verint token endpoint, and pass client credentials in the body. Store the returned access_token.
Add an HTTP module to list new interactions from Verint. Set method to GET, URL to the Business API interactions endpoint, configure Bearer auth with the refreshed token, and filter by startDate since the last run.
Add an Iterator module to loop through each interaction. For each, add an HTTP module to fetch the transcript via the Da Vinci API.
Add another HTTP module to send the transcript to Semarize. Set URL to https://api.semarize.com/v1/runs, add your Bearer token, and set kit_code, mode to "sync", and input.transcript from the previous step. Parse the response as JSON.
Add a Router module. Define Branch 1 with a filter: bricks.compliance_flag.value equals true. Leave Branch 2 as a fallthrough (no filter).
On Branch 1, add a Slack module to alert your compliance team when a flag is detected. Map the quality score, compliance flag, and interaction ID into the message.
On Branch 2, add a Salesforce module to write all brick values (quality_score, compliance_flag, empathy_score) to the appropriate record.
Set the scenario schedule and activate. Monitor the first few runs in Make’s execution log.
What you can build
What You Can Do With Verint Data in Semarize
Semarize enables cross-silo WFM-quality analysis, cross-platform benchmarking, auditable evidence chains, and the ability to build your own tools on structured conversation signals from Verint.
Same-Day Evaluation Framework Updates
Portable Scoring That Moves at Your Speed
What Semarize generates
When your quality framework changes — new disclosure requirements, updated escalation criteria, revised empathy rubric — you need scoring to keep pace. Update your Semarize kit the same day the policy changes. Re-score the last 30 days of transcripts against the new framework overnight. The structured output shows exactly which agents would have failed under the new criteria — before the new criteria go live. Proactive coaching targets those agents in the gap week between policy change and enforcement, eliminating the compliance blind spot entirely.
Learn more about QA & ComplianceCross-Platform Quality Benchmarking
Unified Scoring
What Semarize generates
Your enterprise runs Verint for phone recording, but chat goes through Zendesk and video support through a separate platform. Run transcripts from all three through the same Semarize evaluation kit. Every agent gets a unified score regardless of platform. You discover that Agent Group B scores 79 on phone but 48 on chat — a coaching gap invisible when each platform scores independently. One framework, one scoring system, every platform, every channel.
Learn more about Data ScienceRegulatory Evidence Chain Builder
Compliance Automation
What Semarize generates
Your compliance team needs more than a pass/fail score — they need an auditable evidence chain. Run every interaction through a regulatory evidence kit. Semarize returns disclosure_delivered (with timestamp and verbatim match), consent_obtained (with the exact phrase used), prohibited_language_absent (with any near-matches flagged), and regulatory_version_applied. Each scored interaction generates a structured evidence package that maps directly to your regulatory filing template. Audit prep drops from 3 weeks to 3 days.
Learn more about QA & ComplianceCustom Agent Development Platform
Structured API Output
What Semarize generates
A contact center director vibe-codes a React app that ingests Semarize scores from every Verint interaction. This app tracks granular skill development over time — not just overall scores. Each agent has a profile showing empathy_trajectory, resolution_technique_improvement, product_knowledge_depth, and de_escalation_mastery tracked weekly for 6 months. New hires who plateau on empathy by week 4 get paired with mentors. The app identifies that mentored agents reach full proficiency 3 weeks faster. L&D uses the platform to design personalised development plans backed by conversation evidence.
Learn more about Sales CoachingWatch out for
Common Challenges & Gotchas
These are the issues that come up most often when teams start extracting transcripts from Verint at scale.
Enterprise-gated API access
Verint API access is not self-serve. You need your account team or system administrator to provision credentials through the customer portal. Plan for procurement lead time before building integrations.
OAuth 2.0 token lifecycle management
Verint uses OAuth 2.0 with token-based authentication. Access tokens expire and must be refreshed. Build token refresh logic into your automation workflows to avoid silent failures mid-extraction.
Multiple API families
Verint has 150+ APIs spread across the Business API, Da Vinci, Transcription Editing, and Community platforms. Finding the right endpoint for your use case requires navigating multiple documentation sets.
Transcript processing delay
Da Vinci speech transcription is asynchronous. Attempting to fetch a transcript too soon after an interaction ends will return empty or incomplete data. Build in a delay or implement a polling mechanism.
Media file payload sizes
Exporting voice and video recordings produces large payloads. Plan for external storage (S3, Azure Blob) when working with media exports. Inline transfer of recordings through automation tools will hit size limits.
PII redaction coordination
If your compliance policy requires PII redaction, you must run the Transcription Editing API before exporting to downstream systems. Coordinate the redaction step in your pipeline to avoid sending unredacted data to external tools.
Duplicate processing protection
Without idempotency checks, re-running an extraction flow can process the same interaction twice. Use interaction IDs as deduplication keys to ensure each transcript is handled exactly once.
FAQ
Frequently Asked Questions
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