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How Large Language Models are Changing Analysis

The New Era of LLMs Changing The Analysis

Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals or by humans. Example tasks include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.

A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content.

AI is the driving force behind any effective data analytics strategy. It is a powerful, efficient, and approachable way to process data. Over time, AI has migrated from the frontend of bespoke models to the backend of a new generation of Analysis tools. A generation of no-code AI-powered Analysis tools includes:


General Description

Tableau is a data visualization and business intelligence tool. The query language that the Tableau platform runs on is called VizQL, which translates drag-and-drop dashboard and visualization components into back-end queries. It also requires little need for end-user performance optimization.

AI Functionalities

Tableau offers various AI features which include LLMs (Large Language Models) in their internal architecture like (GPT models, HuggingFace models, etc.) to help users gain insights from their data.

Key AI Features
  1. Ask Data: A natural language processing (NLP) feature that allows users to ask questions in plain language and receive visualizations in response.
  2. Explain Data: This feature provides AI-powered explanations for specific data points, helping users understand the driving factors behind the numbers.
  3. Data clustering: Tableau uses machine learning algorithms to cluster data points based on similarities, which can help users uncover patterns and trends.
  4. Forecasting: Tableau offers time-series forecasting capabilities, allowing users to predict future trends based on historical data.

YouTubeWhat is Tableau? | A Tableau Overview

Tableau is a data visualization and business intelligence tool that offers various AI features to help users gain insights from their data.

Advantages in Space
  1. Supports complex computations, data blending, and dashboarding.
  2. Quickly create interactive visualizations.
  3. Ease of implementation
  4. Handles large amounts of data.
Other Key Features
  1. Informative Dashboards.
  2. Supports numerous data sources.
  3. Connectivity with Live and In-Memory Data.
  4. Provides Great Security.
  5. Easy Collaboration & Sharing.
  6. Provides a Mobile Version.
  7. Advanced Visualization Capabilities.
  8. Availability of Maps.

Power BIMicrosoft Power BI

General Description

Microsoft Power BI also enables users to build machine learning models and utilize other AI-powered features to analyze data. It supports multiple integrations, such as a native Excel integration and an integration with Azure Machine Learning. If an enterprise already uses Microsoft tools, Power BI can be easily implemented for data reporting, data visualization, and building dashboards.

AI Functionalities

The recent versions of Microsoft tools include GPT Models which are also Large Language Models. The most popular one is ChatGPT developed by OpenAI which gives answers to the prompts given by humans and the answers are very accurate. It can also perform some tasks like summarization, writing a mail, writing an article, etc. It can be integrated in Power BI. Integrating ChatGPT can be helpful. ChatGPT is a pre-trained Large Language Model that can understand and respond to human language, which makes it an excellent tool to help users write DAX queries. Power BI is a suite of business analytics tools by Microsoft that allows users to analyze and visualize data.

Key AI Features
  1. Q&A: A large language model based NLP feature that enables users to ask questions about their data in plain language and receive visualizations or answers.
  2. Key Influencers: This feature identifies the factors that have the greatest impact on a selected metric, helping users understand the driving forces behind their data.
  3. AI Insights: Power BI integrates with Azure Machine Learning, allowing users to access and use pre-built or custom machine learning models within their reports.
  4. Anomaly Detection: Power BI automatically detects anomalies in time-series data, helping users identify unusual data points and potential issues.

YouTubeWhat is Power BI? | A Power BI Overview

Advantages in Space
  1. Integrates seamlessly with existing applications.
  2. Creates personalized dashboards.
  3. Helps publish secure reports.
  4. No memory and speed constraints.
  5. Dashboards can ingest decades of Microsoft Office (Excel and Word) archived reports to generate analyses over a huge breadth of time.
  6. Can eventually be updated to interact with ChatGPT
Other Key Features
  1. Range of Attractive Visualizations. Visualizations i.e. the visual representation of data plays a central role in Power BI.
  2. Get Data (Data Source).
  3. Datasets Filtration.
  4. Customizable Dashboards.
  5. Flexible Tiles.
  6. Navigation Pane.
  7. Informative Reports.
  8. Natural Language Q & A Question Box.


General Description

Polymer prides itself on being the only tool that makes a user’s spreadsheets “searchable, intelligent, and interactive instantly.” The tool is used by a wide range of professionals, including data analysts, digital marketers, content creators, and more.

AI Functionalities

Polymer is a data loss prevention and compliance tool that focuses on protecting sensitive data within cloud applications. Its AI features are primarily centered around data classification and protection which were not as easy as now because of Large Language Models which are faster to train and fine tune.

Key AI Features
  1. Machine Learning-based Classification: Polymer uses machine learning algorithms to classify sensitive data such as personally identifiable information (PII), financial data, and health records.
  2. Context-aware Data Redaction: The platform leverages AI to detect and redact sensitive data based on the context of the data, ensuring that only the relevant portions are redacted while retaining the rest of the information.
Advantages in Space
  1. Robust AI tool that transforms data into a database.
  2. Doesn’t require any coding.
  3. Analyzes data and improves users’ understanding.
  4. Makes spreadsheets searchable and interactive.
Other Key Features
  1. Ad hoc analysis.
  2. Filtering Data.
  3. Data Mapping.
  4. Real Time data.
  5. Pareto Analysis.


General Description

Akkio is an AI platform designed to make machine learning accessible to non-technical users. Its main AI features are centered around creating, deploying, and managing machine learning models. Akkio uses 80 percent of the uploaded data as training data, and the other 20 percent is used as validation data. Rather than predicting results, the AI tool based on LLMs offers an accuracy rating for the models and pulls out false positives.

AI Functionalities

Akkio’s platform allows users to build machine learning models without coding or data science expertise. The platform automatically selects the best algorithm for the given data, trains the model, and evaluates its performance. Users can create AI models through a user-friendly interface, without needing to write any code. It enables users to deploy their trained models as APIs or integrate them with other applications such as Salesforce, Google Sheets, and more.

Key AI Features
  1. Automated Machine Learning (AutoML): Akkio’s platform allows users to build machine learning models without coding or data science expertise. The platform automatically selects the best algorithm for the given data, trains the model, and evaluates its performance.
  2. No-code AI: Users can create AI models through a user-friendly interface, without needing to write any code.
  3. Model Deployment: Akkio enables users to deploy their trained models as APIs or integrate them with other applications such as Salesforce, Google Sheets, and more.

YouTubeWhat is Text Classification with Machine  Learning? | Overview

Advantages In Space
  1. No-code machine learning platform.
  2. Great for beginners looking to get started with data.
  3. Build a neural network around selected variables.
  4. Accuracy rating for the models.
Other Key Features
  1. API.
  2. Cost Analysis.
  3. Dashboard Creation.
  4. Data Connectors.
  5. Data Import/Export.
  6. Data Mapping.
  7. Data Storage Management.
  8. Data Verification.

AI chatbotZenarate’s Call Analyzer

Calls Analytics Reports

There are plenty of calls that are recorded and monitored by an organization but if they have to analyze them they have to manually go and listen to the calls and mark them with certain tags.

For example:
  1. Number of calls in which customer is willing to pay and number of calls in which customer is unwilling to pay.
  2. Interesting facts like: system failure reported, bad practices used like harassment, unexpected circumstances occurred and reported before the customer escalates it.
  3. Communication skills studies and customer satisfaction metrics etc.

Data in Excel sheets and raw audio files are not interesting. To make them interesting Zenarate has a dashboard which represents the gist of all the data by date ranges.

Reports That Show Agent Skill Group Activity

The agent’s performance metric depends totally on how they use their skills to deal with customers in different situations.

For example:
  1. Initial opening statements.
  2. Verification process.
  3. All facts discussed.
  4. Etc.

The above situation is for a bank or financial organization. We can only judge agents’ performance if we have information about how they have attempted skills in the present and all the history of their activity.

Detecting Important Calls Sections

AI just mimics whatever humans do. AI models can be used to mimic the analysis process with any data.

The Large Language Models made it easy to train sequences very fast which can also be parallelized with multiple GPUs or TPUs. According to [2] “A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.”

Zenarate’s Call Analyzer uses LLMs which can detect audio sections and phrases where the conversation is “interesting” (highlights important facts related to customer’s experience and customer’s satisfaction). For example, harassment related statements or abusive words or some other interesting facts which a customer wants to detect.

Agent’s Performance Metrics

When we observe different call sections based on different strategies like ( skill missing, skill incorrect, skill attempted, skill not applicable, etc ), we can highlight agents’ calls with the customers which makes it more interesting. Just imagine if you have an AI Bot which can improve you day by day, by sharing your performance metrics and the solution, you will improve at a much faster pace.

In agent’s performance metrics there are several metrics which need to be built using complex manual observations and queries. Call Analyzer AI tool can solve that problem with its home-built AI-powered tool which can analyze multiple calls and generate reports.

Future of AI Analysis

Models like GPT and T5 are very successful in generating texts easily with a broader understanding of the context in which the data resides. The models are trained on a HUGE dataset with a corpus based on Humans’ Texts all over the world. The GPT models, especially ChatGPT, are not only able to generate multiple important examples of hypotheticals based on prompted scenarios, they also perform actions based on a sequence of prompts.
Analysis models can be improved by just sorting and converting raw data into some statistical formats {graphs and charts and excels} automatically. The observations of human activities or procedures for Analysis can be learnt by the AI models using Active Learning Methods. ChatGPT models were trained using Reinforcement Learning from Human Feedback (RLHF) [3].agentenvrew
Reinforcement Learning from Human Feedback (RLHF) [3]


The rapid growth in the application of Artificial Intelligence (AI) has been called the most significant event in recent human history, with the potential to change virtually all aspects of human life. However, expectations about the capabilities and potential of AI (artificial intelligence) are mostly very unrealistic. All existing AI applications such as autonomous driving, spam filtering or conversational AIs (Artificial Intelligence) like Alexa & Siri are based on the first development stage of artificial intelligence, weak artificial intelligence. Here, AI is usually only used for a specific, predefined task. This is partly due to the fact that weak artificial intelligences only take the required information from specific data sets and are thus bound to them. In their respective fields, however, the applications already act in real time and often surpass human efficiency in their work.

In the coming future agent’s training and performance evaluation is going to be totally automated by artificial intelligence. Zenarate provides the best agent training plan in which each agent has its own interface connected to a chat session through which they interact with AI bots as if they are customers. The bots respond to the agents based on the rules made by their supervisors. It’s the best way to simulate the actual agent’s call and let them make mistakes before they go into the actual environment. After all this, Zenarate enables coaches to access coaching as a low-threshold offering in a low-risk environment on their own schedule.

The manual training method has become outdated. If people have to compete in agent’s training they must use a tool like Call Analyzer and AI Coach. These are very smart tools and give a human coach the ability to regularly track changes in a user’s behavior, as it can more easily detect these deviations in behavior.

Contact us today to schedule a demo to learn more about how you can incorporate Zenarate AI Coach into your agent training program. We will answer your questions and show you how you can help your organization develop confidently prepared agents while delivering exceptional experiences to the ones that matter most – your customers.


Robert Janssen

Extensive background designing enterprise software. Leads our machine learning design solving our Customers toughest problems.

Valeriu Tocitu

Senior machine learning engineer with over six years of progressive experience specializing in machine learning, data science, and natural language processing.


A conscious human being and having experience of 2 years in software engineering and dealing with AI model development and Data Processing along with Machine Learning Operations. Focused on integrating cutting edge AI frameworks.

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