AI Quality Assurance System for Customer Service

Development of AI Call Quality Assurance System

AI QA system automatically listens to and evaluates 100% of call recordings against a checklist — replacing selective manual review of 3–5% by a quality manager. Scales quality control without staff growth.

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]
        }

Typical Checklist (20 criteria)

Timeline: basic QA module with 10 criteria — 4–6 weeks. Full system with dashboards — 3 months.