AI-Powered Contact Center Quality Assurance System

Manual call checking covers only 3–5% of recordings. A typical contact center with 100 operators spends up to 10 person-hours per day on manual QA for that sample, while 70% of errors go unnoticed. An AI system processes all 100% of dialogues in an hour, identifying standard violations in real time. We've encountered situations where operators ignored the script, customers became frustrated with long wait times, and QA managers couldn't see the full picture—the manual sample gave a false sense of control. We have implemented such solutions in 30+ contact centers, reducing QA costs by an average of 60%. Budget savings on QA can exceed 60% for large projects. The core technology includes NLP models, such as GPT-4, LLaMA, and our own fine-tuned models.

Why AI Evaluation Is More Accurate Than Human

A person evaluates subjectively: fatigue, mood, personal bias affect scores. AI applies the same criteria to every call—no "it's Friday" exceptions. Comparison: manual check – 10–15 calls per day; AI – 1000+ calls per hour. AI evaluation is 200 times faster than manual, and with proper calibration, accuracy reaches 95%, as confirmed by McKinsey Global Institute.

QA System Architecture

from dataclasses import dataclass from typing import Callable @dataclass class QACriterion: id: str name: str weight: float # weight in final score evaluator: Callable # evaluation function class CallQAEvaluator: def __init__(self, scorecard: list[QACriterion]): self.scorecard = scorecard async def evaluate_call(self, call_id: str, transcript: dict) -> dict: scores = {} total_weighted = 0 total_weight = sum(c.weight for c in self.scorecard) for criterion in self.scorecard: score = await criterion.evaluator(transcript) scores[criterion.id] = { "name": criterion.name, "score": score, # 0-10 "weight": criterion.weight } total_weighted += score * criterion.weight final_score = total_weighted / total_weight return { "call_id": call_id, "final_score": round(final_score, 1), "grade": self._score_to_grade(final_score), "breakdown": scores, "violations": [c for c in self.scorecard if scores[c.id]["score"] < 5] } 

What Each Criterion Evaluation Includes

Each criterion is implemented as an asynchronous evaluator function. For example, for greeting, we use GPT-4o-mini with a system prompt that checks five sub-criteria and returns a number from 0 to 10. This approach allows flexible logic tuning without rewriting code.

async def evaluate_greeting(transcript: dict) -> float: """Greeting evaluation (0–10)""" first_agent_text = next( (t["text"] for t in transcript["turns"] if t["speaker"] == "OPERATOR"), "" ) response = await client.chat.completions.create( model="gpt-4o-mini", messages=[{ "role": "system", "content": """Evaluate the operator's greeting from 0 to 10. Criteria: - Mentioned company name (+2) - Said own name (+2) - Greeted respectfully (+2) - Offered help (+2) - Tone friendly (+2) Return only the number.""" }, {"role": "user", "content": first_agent_text}] ) try: return min(10, max(0, float(response.choices[0].message.content.strip()))) except ValueError: return 5.0 async def evaluate_hold_notification(transcript: dict) -> float: """Did the operator notify about hold""" hold_keywords = ["please wait", "placing you on hold", "one moment"] agent_texts = " ".join(t["text"].lower() for t in transcript["turns"] if t["speaker"] == "OPERATOR") return 10.0 if any(kw in agent_texts for kw in hold_keywords) else 0.0 

Typical Checklist (20 Criteria)

Comparison: Manual vs AI

Order a custom checklist tailored to your business.

How Is the Evaluation Model Calibrated?

At the start, we parallel evaluate 500 calls manually and with AI. We compare results, adjust criterion weights and prompts. We use metrics: accuracy, recall, F1-score. Calibration takes 2–4 weeks. For calibration, we use real dialogue data: collect a sample of 500 calls with expert scores already assigned. Then we run several prompt variants and select the best based on metrics. This achieves 95% accuracy by the second week.

How to Implement a QA System in 4–6 Weeks

1. Audit current standards — gather checklists, scripts, recordings.
2. Develop criteria — adapt to your business (weights, thresholds).
3. Integration with ATC/CRM — connect to your telephony and CRM.
4. Launch and calibrate — first 2 weeks parallel evaluation with human, model adjustment.

What's Included in the Work

• Documentation: API specification, criteria configuration guide.
• Source code: repository with evaluator modules.
• Dashboards: Power BI or Grafana with breakdown by operator, categories, time series.
• Training: 2–3 workshops for QA managers and administrators.
• Support: 1 month post-release support.

Company Experience

Over 5 years in AI solutions. 30+ implemented QA projects for contact centers and retail. Team certified in NLP and MLOps (TensorFlow, PyTorch). We use best practices: RAG, fine-tuning, LoRA for model adaptation. Our engineers are proficient in PyTorch, Hugging Face, LangChain and can fine-tune models to your business specifics.

Timeline and Cost

Basic module with 10 criteria — 4–6 weeks. Full system with dashboards — up to 3 months. Cost is calculated individually. Contact us for a free project assessment.