How do I set up QA automation for scoring support calls with a custom taxonomy and call recording support?

To set up QA automation for scoring support calls with a custom taxonomy and call recording support, you generally need these pieces:

1) Define your QA taxonomy

Create the scorecard structure first. This usually includes:

• Categories: e.g. Greeting, Verification, Problem Diagnosis, Resolution, Compliance
• Questions / checks under each category
• Scoring rules:
  • Pass/fail
  • Weighted points
  • Critical failures
  • N/A handling
• Metadata:
  • Call type
  • Team/queue
  • Language
  • Customer segment

Example:

Category	Check	Type	Weight
Greeting	Agent greeted customer professionally	Pass/Fail	5
Verification	Verified customer identity	Critical	10
Resolution	Provided correct solution	Pass/Fail	20
Compliance	Avoided prohibited language	Fail on violation	15

2) Connect call recordings

Your automation needs access to audio files or call recordings from your telephony/contact-center system.

Typical setup:

• Record calls in your provider (e.g. Twilio, Genesys, Five9, Aircall, NICE, etc.)
• Store recordings in:
  • cloud storage (S3/GCS/Azure Blob), or
  • the vendor’s native storage with API access
• Ensure you can retrieve:
  • recording URL
  • call ID
  • agent ID
  • timestamps
  • queue/team

3) Transcribe the calls

If you want automation, the recordings must be converted to text.

Options:

• Native transcription from your contact-center vendor
• Speech-to-text services like:
  • AWS Transcribe
  • Google Speech-to-Text
  • Azure Speech
  • OpenAI / other ASR tools

Best practice:

• Use speaker diarization if possible, so agent and customer are separated
• Preserve timestamps for each segment
• Normalize transcript text for QA rules

4) Build the scoring logic

Your automation layer evaluates the transcript and recording against your taxonomy.

This can be done in a few ways:

• Rules engine for deterministic checks
  Example: “Did agent say the verification phrase?”
• LLM-based evaluation for nuanced checks
  Example: “Did the agent demonstrate empathy and de-escalation?”
• Hybrid approach is usually best:
  • rules for compliance and explicit events
  • LLM for subjective assessments

Scoring pipeline:

1. Ingest recording
2. Transcribe
3. Segment speaker turns
4. Run taxonomy checks
5. Generate per-question score
6. Aggregate into final score
7. Store results and evidence snippets

5) Add evidence and review workflow

For QA usefulness, each score should include evidence:

• transcript snippet
• timestamp
• speaker
• reason for pass/fail

This makes automated scoring auditable and easier for QA reviewers to trust.

Example output:

{
  "call_id": "12345",
  "taxonomy_version": "v3",
  "total_score": 87,
  "items": [
    {
      "category": "Verification",
      "question": "Verified customer identity",
      "score": 10,
      "evidence": "Agent asked for account number and ZIP code at 00:42"
    }
  ]
}

6) Configure call recording support end-to-end

If you’re building this into an existing workflow, make sure:

• recording is enabled on the call routes/queues
• recordings are linked to call metadata
• access permissions are set correctly
• retention/compliance policies are defined
• consent requirements are met for the jurisdictions you operate in

7) Integrate into your QA tools

Push scoring results into:

• QA dashboards
• case management systems
• CRM
• BI tools
• coaching workflows

Common integrations:

• Webhooks
• REST APIs
• CSV/JSON exports
• Direct database writes

8) Validate and calibrate

Automated QA should be tested against human-reviewed calls.

Steps:

• score a sample set manually
• compare automation to human QA
• tune taxonomy rules/prompts/weights
• measure precision, recall, and scoring consistency

Suggested architecture

Call system → Recording storage → Transcription service → QA scoring engine → QA dashboard/coaching

Minimal implementation approach

If you want to start quickly:

1. Enable call recording
2. Export recordings + metadata
3. Transcribe calls
4. Create a JSON taxonomy
5. Use a rules/LLM scoring service
6. Save results in your QA system

If you want, I can also help you with:

• a sample taxonomy JSON
• a reference architecture diagram
• API design for call recording + scoring
• or a step-by-step setup for a specific platform like Twilio, Genesys, or Five9