An intelligent system that understands, measures, and improves customer feedback.

Millions of customer interviews are conducted every day.
However, most of these conversations are only recorded, not analyzed.
In fact, every call is a benefit for businesses. gold mineIt contains customer needs, problems, and even future behavior.

This is exactly where AI-Powered Speech Analytics it takes effect.
Developed by FGS AI Speech Analytics The platform listens to and resolves all calls, senses sentiment, identifies trends, and provides strategic insights to managers.

What is AI-Powered Call Analysis?

Speech-to-text (STT) analysis is the process of automatically transcribing audio conversations, and then analyzing these transcripts using natural language processing (NLP) and sentiment analysis techniques.

FGS's AI infrastructure extracts meaningful data from every interview:

• Customer satisfaction score

• Emotion (positive / neutral / negative) detection

• Agent performance indicators

• Frequently used word and topic analysis

• Automated quality assessment (AI QA)

Thanks to this system, thousands of hours of audio recordings can be analyzed in a matter of minutes. measurable and actionable It is transformed into information.

How does it work?

1️⃣ STT (Speech-to-Text)
AI-powered speech recognition engines (OpenAI Whisper, Google Speech, FGS Speech AI) transcribe spoken words into text with high accuracy.

2️⃣ NLP (Natural Language Processing)
The transcribed conversations are analyzed by artificial intelligence models. Emotions, intentions, and key themes are identified.

3️⃣ Sentiment & Intent Analysis
The system labels the customer as angry, satisfied, or undecided. The interaction is categorized as "risky," "positive," or "requires follow-up.".

4️⃣ AI QA Integration
The meetings are graded, and the representatives' performance is evaluated objectively.

5️⃣ Reporting and Dashboard
All data is monitored live on FGS's web-based control panel and can be exported in Excel or API format.

Values Obtained from Call Analysis

• Real-time insight: The meaning, tone, and outcome of the conversations are measured immediately.

• Objective quality measurement: Evaluation independent of human error.

• Operational efficiency: QA teams will experience less time loss.

• Enhancing customer experience: The causes of complaints are identified early.

• Strategic decision support: A data-driven management culture is formed.

Key Features of FGS AI Speech Analytics

1. Multilingual Support

The multilingual infrastructure, including Turkish, English, and Arabic, is compatible with call centers in different geographical locations.

2. Real-time monitoring

Managers can listen to ongoing discussions live and see analysis results instantly on the dashboard.

3. AI QA Integration

Specifically developed for quality teams, the AI QA module automatically updates quality scores by scoring each interview.

4. Emotion and Intention Analysis

The system analyzes not only the words themselves, but also the tone and rhythm of speech. This allows it to detect emotions such as "anger," "impatience," or "satisfaction.".

5. Advanced Reporting and KPI Tracking

The real-time dashboard provides the following indicators:

• Average conversation time

• Customer sentiment distribution

• Agent performance scores

• Topic-based call trends

• Daily/weekly analysis charts

How to use AI Speech Analytics?

FGS's solution offers easy integration thanks to its cloud-based structure.
By integrating it into the call recording system or switchboard infrastructure, automated analysis is initiated.

Usage Steps:

1. Calls are recorded in the system.

2. The AI engine converts speech to text.

3. The NLP engine interprets text.

4. Sentiment analysis results are generated.

5. The results are visualized on the dashboard.

Technical Infrastructure

Artificial Intelligence-based Automated Quality Assessment (AI QA)

Call analysis doesn't just generate data, it measures quality.
FGS's AI QA module, It scores each interview out of 100 and automatically assesses quality standards.

The criteria evaluated were:

• Greeting standard

• Tone of communication and empathy

• Script compatibility

• Resolution time

• Customer satisfaction rate

This allows quality teams to analyze hundreds of interviews in seconds.

Use Cases

1. Call Center Operations

Every call is automatically analyzed, risky conversations are identified, and notifications are sent to managers.

2. Customer Satisfaction Tracking

If the tone of the conversation is negative, the system generates a "risk of low satisfaction" warning.

3. Sales and Marketing Analysis

Sales representatives' communication style, customer response rate, and closing success are analyzed.

4. Training and Performance Management

Agent errors, lack of empathy, or communication weaknesses are reported. Training plans are prepared based on this data.

Sectoral Application Areas

• Finance and Banking: Customer complaint analysis and regulatory compliance.

• Telecommunication: Quality and sentiment analysis in operator calls.

• Health: Measuring patient satisfaction in communication processes.

• E-commerce: Tracking after-sales support calls

• Public Institutions: Quality control for citizen calls.

Data Security and GDPR Compliance

All of FGS's AI systems, KVKK and GDPR It fully complies with the standards.

• AES-256 bit data encryption

• Role-based access control

• Audit log system

• Databases hosted within Türkiye

Data can only be viewed by authorized personnel, ensuring both security and legal compliance.

Reporting and Visualization

FGS AI Dashboard simplifies complex data with its user-friendly interface.
Reports are updated in real time and can be exported in PDF or Excel format.

Indicator Examples:

• Positive/Negative call ratio

• Average resolution time

• Agent success ranking

• Total number of calls per day

• Emotional intensity heat map

Increase Business Value with AI Call Analytics

1. Shorter Resolution Times

Problems are identified quickly, reducing customer waiting times.

2. Objective Performance Evaluation

Human bias is eliminated, and evaluations are entirely data-driven.

3. Customer Loyalty

Problems are identified and resolved early; brand trust is strengthened.

4. Increased Productivity

Quality teams perform more analysis while listening to fewer calls.

5. Strategic Decision Support

Data-driven analytics guide product development and process improvement.