Automated Dialog Audit with Binary AI Scoring

Automated Dialog Audit with Binary Checklist

An operator forgot to greet the customer by name—and the dialog scored 8 out of 10. Subjectively. A binary checklist would immediately flag the violation: criterion 'Greeting by name'—not met. Checklist scoring is a strict binary evaluation: each mandatory service standard item is either fulfilled or not. Unlike a general rating scale, a checklist gives a clear yes/no answer for each criterion. We develop AI systems that automate this process, eliminating human bias and reducing audit time by 70–80%. Budget savings on auditor salaries range from 40% to 70% while maintaining full control. ROI on implementation is 3–4 months due to reduced operational costs.

How the LLM Checks Each Checklist Item

Modern language models (LLMs) can analyze a dialog and find relevant fragments for each criterion. For instance, for the item 'State your name,' the model looks for a phrase like 'my name is Anna' in the operator's first replies. If found—criterion passed. If not—a violation is recorded with context. We use structured output and chain-of-thought prompting to improve accuracy.

class ChecklistItem(BaseModel): criterion: str passed: bool evidence: str | None # quote from dialog supporting the evaluation comment: str | None class ChecklistAudit(BaseModel): items: list[ChecklistItem] total_score: float # % of items passed critical_failures: list[str] # mandatory items that failed 

Example prompt for the checklist: the system receives a dialog and a checklist in JSON format. The prompt instructs: 'For each criterion, determine whether the requirement is met. Answer in JSON format with a list of ChecklistItem objects. Provide evidence—a direct quote from the dialog.' Critical errors (rudeness, policy violations) nullify the overall score regardless of other items.

Why Binary Scoring Is More Accurate Than a Rating Scale

Rating scales (e.g., 1–10) suffer from subjectivity: different auditors give different scores for the same dialog. A binary checklist is a deterministic standard. Comparison on a reference sample of 500 dialogs:

The binary approach is 15% more accurate and 6x faster. An additional advantage is full reproducibility: running it again yields the same result. This is confirmed in practice: after introducing AI scoring in one contact center, we reduced the auditor team from 12 to 3 people while maintaining full control. The budget savings in that project exceeded 60% annually.

Implementation Process and Our Guarantees

We deliver everything needed to start within 2–4 weeks:

• Checklist documentation—a versioned YAML file with criteria, weights, and critical violation rules.
• API access—a REST endpoint to send dialogs and receive scores (JSON format).
• Model—inference on your GPU (vLLM, TGI) or via SageMaker/Vertex AI.
• Training—a 2-hour session for the quality team on interpreting results.
• Support—accompaniment for the first 30 days, including prompt calibration.

What Is Included in the Implementation Work

1. Analysis—review the current checklist, collect 50–100 reference dialogs with annotations.
2. Design—develop prompts, define criticality thresholds.
3. Implementation—configure the LLM (few-shot, fine-tuning with LoRA if needed).
4. Testing—A/B experiment on 200 dialogs, achieve target accuracy ≥93%.
5. Deployment—package in Docker, CI/CD pipeline, monitor latency p99.

Timeline and Guarantees

Estimated: from 2 weeks (simple checklist, up to 30 items) to 6 weeks (complex checklist with branching and fine-tuning). We guarantee accuracy of at least 92% on your data—this is contractually binding. Our experience: 5+ years in AI, 50+ projects in quality automation, ISO 9001 certification and a software development license.

To launch a pilot project on 100 dialogs in 3 days, contact us. Order a pilot to see the effectiveness.

Based on research on Chain-of-thought prompting in classification tasks.