Getting Started with Speech-to-Text & GPT Analysis

Today’s insights are brought to you by Krystian Bergmann, AI Consulting Lead at Netguru.

Last week, we explored how speech-to-text and GPT-powered sentiment analysis are transforming customer support. Now, let’s talk about implementation.

How do you integrate these AI capabilities into your helpdesk without building models from scratch? The good news: enterprise-ready AI tools make it easier than ever.

Step 1: Define your use case

Before jumping into implementation, outline your primary goals:

1. Do you want real-time transcription for live support?

2. Are you looking for sentiment analysis to flag negative interactions?

3. Do you need multilingual transcriptions for global customers?

Each objective influences the tools you’ll choose.

Step 2: Choose the right AI tools

Pre-built AI solutions streamline the process:

• OpenAI Whisper – High-accuracy, multilingual speech-to-text.
• Azure AI Speech – Enterprise-grade transcription with speaker diarization.
• Google Cloud Speech-to-Text – Scalable AI with domain adaptation.
• Twilio Speech Recognition – Real-time transcription for voice calls.

Step 3: Set up your workflow

These tools provide APIs and SDKs for seamless integration, enabling businesses to enhance their helpdesk operations effortlessly.

By connecting AI-powered transcription to CRM or helpdesk software, companies can streamline workflows and improve accessibility to conversation data.

Real-time sentiment analysis offers instant feedback on customer interactions, helping supervisors and agents address issues proactively.

Additionally, automating call summaries and performance reports ensures more efficient monitoring and continuous improvement of customer support quality.

Step 4: Test & Optimize

Once integrated, it's crucial to analyze the output for optimal performance.

Assess transcription accuracy to ensure it captures speech effectively, making adjustments for accents and background noise if needed.

Evaluate sentiment analysis to confirm it's flagging the right interactions, fine-tuning thresholds for better precision.

Finally, determine whether agents are benefiting from the insights by monitoring their responses and adapting workflows based on their feedback for continuous improvement.

With the right tools and setup, you can enhance customer experience, reduce supervisor workload, and boost agent performance—all with AI.

Would you like a detailed step-by-step breakdown of implementation? Hit reply, and I’ll be happy to share more.

Best,

Krystian