How to build a Call QA and Compliance Auditing Agent

TL;DR

• Upload a call recording; the system transcribes it and audits the conversation for quality and regulatory compliance.

• You get a scored report with summary, concrete violations, and recommendations—standardized across every call.

Common Pain Points of Manual QA

• Manual QA is slow, subjective, and only samples a fraction of calls.

• Inconsistent scoring rubrics across teams and regions.

• Missed disclosures or mishandling of sensitive data create compliance risk.

• Little actionable feedback for agents to improve.

• Fragmented outputs (notes in one place, transcripts in another).

What the Agent Delivers

• Automated transcription and consistent compliance scoring (0–100).

• Checklist-based detection: greeting/ID, disclosures, script adherence, sensitive info handling, tone.

• Clear report: executive summary, detailed findings, violations with evidence, and next-step recommendations.

• Standardized, export-ready Markdown/HTML you can store or share.

• Easy integration to save reports to Drive/SharePoint or push to QA dashboards.

Step-by-Step Build (StackAI Nodes)

1) Audio Input (audio2text-0) – Audio → Text

• Type: Audio-to-Text (Deepgram or equivalent)

• Role: Upload or record a call; get a transcript (timestamps optional).

• Why it matters: Removes manual transcription and starts the audit.

2) OpenAI Analysis (llm-0) – Transcript → QA & Compliance Review

• Type: LLM (OpenAI, gpt-4o-mini)

• Analyzes for:

  
  

  • Greeting & identification

  • Regulatory disclosures

  • Script adherence

  • Handling of sensitive info (PII/PCI/PHI)

  • Professionalism & tone

  • Concrete violations with evidence (quotes + timestamps if available)

  • Outputs: Summary + compliance score (0–100) + recommendations

Prompt skeleton (example):

System: You are a Call QA & Compliance auditor. Be precise, evidence-based, and consistent.

User content:
Transcript:
{audio2text-0}

Instructions:
1) Provide a COMPLIANCE_SCORE (0-100) with 2–3 bullet rationales.
2) Write a 4–6 sentence EXECUTIVE_SUMMARY.
3) List DETAILED_FINDINGS with evidence. For each finding include:
   - Category: {Greeting/ID, Disclosures, Script Adherence, Sensitive Info Handling, Tone}
   - Status: {Pass/Fail/Needs Review}
   - Evidence: direct quote(s) + timestamp(s) if present
4) List VIOLATIONS (if any) with severity {Low/Med/High} and remediation guidance.
5) Provide RECOMMENDATIONS: 3–5 prioritized actions for the agent.

Return JSON with keys:
{ "compliance_score": number,
  "rationales": [ ... ],
  "summary": "...",
  "findings": [ { "category": "...", "status": "...", "evidence": [ ... ] } ],
  "violations": [ { "rule": "...", "severity": "...", "evidence": "...", "remediation": "..." } ],
  "recommendations": [ "..." ]

3) Audit Report Template(template-0) – Format → Report

• Type: Template (Markdown)

• Role: Renders the structured LLM JSON into a readable report.

Template example (Markdown):

# Call QA & Compliance Audit

**Compliance Score:** {llm-0.compliance_score}/100

## Executive Summary
{llm-0.summary}

## Rationales
- {llm-0.rationales[0]}
- {llm-0.rationales[1]}
- {llm-0.rationales[2]}

## Detailed Findings
{% for f in llm-0.findings %}
**Category:** {f.category} — **Status:** {f.status}  
**Evidence:** {", ".join(f.evidence)}
{% endfor %}

## Violations (if any)
{% if llm-0.violations|length == 0 %}
None detected.
{% else %}
{% for v in llm-0.violations %}
- **Rule:** {v.rule} | **Severity:** {v.severity}  
  **Evidence:** {v.evidence}  
  **Remediation:**

4) Output(out-0) – Display

• Type: Output

• Role: Shows the final formatted QA & Compliance report to the user and enables copy/export.

Summary Table