AI Quality Control System That Analyzes 2,000+ Calls Daily Instead of a QA Department

The client is one of the largest outbound sales operators in its niche in the US, with a multi-level team of more than 150 reps. They call prospects, schedule meetings, and pass leads further down the funnel. The primary acquisition channel is cold calling, along with several types of follow-up contacts (referral, LinkedIn follow-up, voicemail, etc.).

Daily call volume reaches 2,000+ calls. Recordings accumulated in the telephony system, but no one systematically analyzed them or checked them for script compliance. At this volume, organizing manual monitoring without creating a dedicated QA team was practically impossible.

At the same time, the company already had its cadence scenarios and scripts in place - what it lacked was a system capable of checking whether reps actually followed them in practice. Management needed an objective view of every call without having to hire and train a separate QA department from scratch.

The company had no systematic visibility into what was actually happening during its reps’ conversations, where deviations from cadences occurred, or at which stages customers were lost. As a result, management could not determine how many potential meetings and deals were being lost due to script non-compliance or mistakes during lead qualification.

As part of custom software development, we created a fully automated system that analyzes every call in several stages:

1. Call recording: The system receives the recording from the existing telephony system, ensuring that every call goes through a single review process.

2. Transcription and diarization: The recording is converted into text, identifying when the rep is speaking and when the customer is speaking. This makes it possible to analyze each party’s statements separately.

3. Automatic call type detection: AI identifies one of 8 cadence scenarios, eliminating the need to classify each call manually.

4. Scenario selection: Once the call type is identified, the system applies the specific scenario, criteria, and rules that correspond to that type of conversation.

5. AI scoring across 10+ parameters: The system compares the transcript with the reference script for that call type and evaluates the conversation based on the criteria relevant to it.

6. Final score: Based on the review, the system generates an overall script compliance score.

7. Dashboard: Managers can see each rep’s rating for every call type, while reps can see a breakdown of their own conversations against each point in the script.

Distinguishing misunderstanding from objection. AI differentiates between situations where a customer simply did not understand the offer and a deliberate objection - in the first version, these situations were confused.

Tracking mentions of details. The model counts mentions of required details - the company name, location, and referral - and compares them against the optimal range. What matters is not only whether the required detail is mentioned, but also how often it is mentioned.

Scenario builder. Sales managers can modify scenarios and evaluation rules directly in the interface without involving the dev team. Criterion weights can also be configured for each scenario. This makes it possible to adapt the QA logic when sales scenarios change without additional development.

1. Attempts and Solutions

The first version of the system relied on finding exact keyword matches with the script. In practice, this approach had three problems:

• It did not account for context. The presence or absence of a specific phrase alone did not show what was happening in the conversation.

• It confused misunderstanding with objection. The system did not distinguish between a situation where the customer did not understand the offer and one where they deliberately declined it.

• It unfairly lowered the score when a call ended prematurely. If the customer hung up, keyword-based logic could penalize the rep for a part of the script that the conversation never actually reached.

We therefore switched to LLM-based analysis combined with clear business rules. The language model takes the meaning of each statement into account rather than relying only on word matches. The rules distinguish between misunderstanding and deliberate objection. And if the customer ends the call prematurely, the rep’s score is no longer reduced as long as they followed the script up to that point.

2. Different Rules for Different Call Types

Applying the same criteria to all 8 scenarios would be incorrect. Once the call type is identified, the system uses the corresponding scenario, the criteria applicable to it, and the configured weights. For example, a voicemail should not be evaluated according to all the same rules as a full cold call.

3. The Biggest Engineering Challenge

The most difficult part was turning subjective human criteria into clear and reproducible rules. For example, we needed to formalize what counts as a good mention of the location and where to draw the line between “the customer did not understand” and “the customer declined.”

The goal was for similar calls to receive similar scores regardless of who performs the evaluation - a human or AI.

4. Why Custom Development

Most of the functionality was implemented as a SaaS solution tailored to the client’s specific scenarios rather than built on ready-made QA platforms. The reason was the specific business logic: 8 call types with different scenarios and rules, criteria that do not apply to every type, the ability to change rules without developers, as well as separate logic for prematurely ended calls and monitoring the frequency of mentions.

5. Key Components of the Solution

• Separating the voices of call participants - identifying the rep and the customer.

• A mechanism for automatically identifying one of 8 call types.

• A scoring module with configurable weights for each scenario.

• A module for counting mentions of required details and comparing them against the optimal range.

• A scenario and evaluation rule editor available to managers without involving developers.

• Feedback within minutes: Reps receive a score and a breakdown for each point in the script within minutes after the call ends. Previously, there was no such feedback at all.

• Consistent evaluation rules: 100% of calls are evaluated using the same system for everyone and with virtually no human involvement; the result does not depend on who would have reviewed the call.

• Faster time to target performance: Reviewing every call accelerated coaching and reduced the time it takes new reps to reach target performance.

• 100% coverage: The system automatically analyzes every call from every rep using consistent criteria.

• Ratings by call type: Managers can see each rep’s rating for every call type.

• Visibility into conversations: Management can see not only the number of meetings booked but also the stages of the conversation where reps deviate from the script.

• Budget savings: The system eliminated the need to build a separate QA team of 3–4 specialists. The estimated savings from avoided salaries are $150K–180K per year.