I can answer questions about my experience with SQL Server as we are trying to capture reviews for SQL Server. We don't use the reporting services within SQL Server; we're using this for heavy-duty engineering inside an AI engine. We don't use SQL Server Data Warehouse or SQL Server Management. We just use SQL Server extensively. We use Office, but in terms of engineering products, the major use right now is SQL Server. We use TensorFlow, which is different from TensorLeap. We use Google Cloud extensively; we use their AI, STT, and TTS capabilities. We use these two products to be able to talk to our users, and then we have an AI meaning engine that back ends that. Once we get the speech, we can tell what it means. Accuracy with Text-to-Speech is important. If you want to talk to your computer, using Google is the way to do it, but it's not without problems. Natural intonation and pacing in communications have gotten hugely better over the last two or three years. We're following the research at MIT of Rosalind Picard about these kinds of issues, and we plan to implement it. This applies to both Text-to-Speech and Speech-to-Text because it's a conversation.
The main use cases involve clients handling various calls day-to-day who have a quality analyzer or auditor wanting to verify what representatives spoke with specific clients. This piece of technology comes into play because the auditor cannot go and listen to long audios for call recordings that span 10 to 20 or more hours. They won't be checking individual calls, but using Google Cloud Speech-to-Text, we can easily transcribe the call with respect to who has spoken what, with specific speaker diarization. We can ask any open-source AI, or even paid AIs such as ChatGPT AI, to provide the transcription and the context of representative conversations with clients. From that, we will get a complete overview of the call in a few seconds. We can transcribe multiple calls, and if we want to check our representative's productivity per day, we can easily transcribe all the calls and get an overall understanding of what has occurred in the calls. This is the broader scope of the Google Cloud Speech-to-Text solution I developed for my client.
For development purposes, my company uses Python in the back end, like the FastAPI framework, and then we utilize the clients of Google Cloud Platform.
We need IT corporate chatbots to help new people with some things. When you are new in a company, you need a lot of things, such as access. We want to make a chat for auto consulting. You can say, "Oh, hello, I'm new, I need an account in GitHub, please, and GitLab." And we want to do the chatbot with integration with all the systems for may access another common task for users automatically in the chatbot. We want to make a chatbot to resolve common problems for people. In that way, we will save time as it will allow support to help with other problems, harder problems. You can resolve common problems with a bot and non-common problems with humans.
Learn what your peers think about Google Cloud Speech-to-Text. Get advice and tips from experienced pros sharing their opinions. Updated: September 2025.
Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google’s machine learning technology.
I can answer questions about my experience with SQL Server as we are trying to capture reviews for SQL Server. We don't use the reporting services within SQL Server; we're using this for heavy-duty engineering inside an AI engine. We don't use SQL Server Data Warehouse or SQL Server Management. We just use SQL Server extensively. We use Office, but in terms of engineering products, the major use right now is SQL Server. We use TensorFlow, which is different from TensorLeap. We use Google Cloud extensively; we use their AI, STT, and TTS capabilities. We use these two products to be able to talk to our users, and then we have an AI meaning engine that back ends that. Once we get the speech, we can tell what it means. Accuracy with Text-to-Speech is important. If you want to talk to your computer, using Google is the way to do it, but it's not without problems. Natural intonation and pacing in communications have gotten hugely better over the last two or three years. We're following the research at MIT of Rosalind Picard about these kinds of issues, and we plan to implement it. This applies to both Text-to-Speech and Speech-to-Text because it's a conversation.
The main use cases involve clients handling various calls day-to-day who have a quality analyzer or auditor wanting to verify what representatives spoke with specific clients. This piece of technology comes into play because the auditor cannot go and listen to long audios for call recordings that span 10 to 20 or more hours. They won't be checking individual calls, but using Google Cloud Speech-to-Text, we can easily transcribe the call with respect to who has spoken what, with specific speaker diarization. We can ask any open-source AI, or even paid AIs such as ChatGPT AI, to provide the transcription and the context of representative conversations with clients. From that, we will get a complete overview of the call in a few seconds. We can transcribe multiple calls, and if we want to check our representative's productivity per day, we can easily transcribe all the calls and get an overall understanding of what has occurred in the calls. This is the broader scope of the Google Cloud Speech-to-Text solution I developed for my client.
For development purposes, my company uses Python in the back end, like the FastAPI framework, and then we utilize the clients of Google Cloud Platform.
We need IT corporate chatbots to help new people with some things. When you are new in a company, you need a lot of things, such as access. We want to make a chat for auto consulting. You can say, "Oh, hello, I'm new, I need an account in GitHub, please, and GitLab." And we want to do the chatbot with integration with all the systems for may access another common task for users automatically in the chatbot. We want to make a chatbot to resolve common problems for people. In that way, we will save time as it will allow support to help with other problems, harder problems. You can resolve common problems with a bot and non-common problems with humans.