How Thunai Uses Confluent to Create Real-Time AI Analysis

Do your AI models have to wait for slow, overnight batch processing to get new information.

Building a real-time AI system can seem very complex. But it doesn't have to be!

Thunai does this by using Confluent for its real-time voice transcription and data pipeline, and here’s how.

What is Confluent?

Confluent is a data streaming platform built on the open-source technology Apache Kafka.

This data streaming platform is designed to help companies deal with and analyze data. This helps companies do this in real-time, instead of in batches.

In fact, this software is also capable of moving large amounts of data very quickly. In doing this, Confluent makes Kafka easier to work with and manage.

Additionally, the platform adds tools for security and data management. As a result, companies can set up real-time applications.‍

Using Confluent's Data Streaming for Instant AI Analysis and Real-Time Voice Transcription

The main part of Thunai's AI is built on real-time data. We use Confluent to manage Kafka. This lets us process information as it is created.

This data, like a live customer support call, user actions, or system logs, is streamed instantly. This lets our AI transcribe, understand, and act on events in the moment, not hours later.

• Live Call Transcription: As a call happens, the audio is streamed via Kafka. This lets our AI perform live transcriptions. It turns spoken words into text with real-time voice transcription.
  
• Real-Time Analysis: With the live transcript, our AI can immediately give an analysis. It can detect sentiment, identify keywords, and make suggestions during the call.
  
• Feeding the Thunai Brain: All this processed, the real-time voice transcription and data is continuously streamed into the Thunai Brain. This keeps our central real-time AI model up-to-date with the latest information.

How Thunai's Real-Time Voice Transcription and Data Pipeline Works

• Step 1: Ingest via Kafka: All data points, like a live customer call, are captured as events. Confluent manages this high volume of data using Kafka. This makes sure no information is lost.
  
• Step 2: Process in Real-Time: This data stream is processed immediately. It is not saved to a database to be checked later. This is where our AI performs live transcription and immediate analysis.
  
• Step 3: Moved to the Thunai Brain: The processed data and insights are then streamed directly into the Thunai Brain. This is our central knowledge and intelligence hub. It gets smarter with every new event.
  

Benefits of Using a Confluent-Based Real-Time Voice Transcription and Data Pipeline

1. Get True Real-Time AI Actions (Not Just Fast Batch Processing)

Many AI systems run on data that is hours or even days old. By using Confluent, our real-time agent assist can act on events as they happen.

Agents can see live transcriptions and analysis during a call, not after it is over. This means problems are solved in the moment.

This helps by:

• Giving instant insights and suggestions to support agents.
• Detecting customer sentiment changes mid-conversation.
• Supporting immediate, context-aware AI responses.
  

2. Create a Constantly Learning AI Brain

An AI is only as smart as its data. By streaming all interactions directly into the Thunai Brain, we make sure it is always learning.

With this, the AI's central knowledge base is constantly updated. It is not just synced once a day.

This method helps the AI agent become skilled quickly by:

• Allowing the AI to learn from every single customer interaction.
• Making new information and terminology available to the AI instantly.
• Improving the accuracy of all future AI analysis.
  

3. Build a Foundation That Can Grow and Resist Failure

Customer interactions create a large, spiky, and unpredictable amount of data. A traditional database would crash.

Confluent and Kafka are built for this. They handle millions of events without losing data or slowing down. This makes sure our AI is always on and always fast, no matter the load.

What are the Limitations of Traditional Batch Processing for AI Voice Transcription

• Delayed Insights: AI models only get new data after an overnight job. Analysis is always 12-24 hours out of date.
  
• Inability to Act In the Moment: It is impossible to give live transcription or agent assists when the data is not processed until after the call ends.
  
• Wasted Resources: Running large batch jobs does not use resources well. It is an all-or-nothing process. It processes data that may no longer be relevant.
  
• Stale AI Models: The AI is always learning from old news. This leads to less accurate or irrelevant responses.
  
• Poor Growth: Batch windows get longer and longer as data grows. They often fail to complete before the next day begins.
  

Using Thunai's Real-Time AI Assist for Better Business Outcomes

Stop making important business decisions based on yesterday's data. In the age of AI, real-time AI is the only time that matters.

Thunai's system, which uses Confluent, is built from the ground up with real-time agent assist. This frees your team to act on insights now, not tomorrow. With Thunai's real-time AI assist and AI agents, you get:

• Live Transcription and Analysis: Resolve customer issues faster with an AI that transcribes and understands calls as they happen.
  
• Real-Time Agent Assistance: Give your agents live suggestions and analysis based on the current conversation.
  
• Thunai Brain: Support your entire company with a central AI knowledge base that learns from every interaction, instantly.
  
• Real-Time Dashboards: See business KPIs and customer sentiment change minute-by-minute, not day-by-day.

Ready to see what real-time AI assist can do for you? Try Thunai for free and see the change in your business operations!