GPT Auto Data Analytics empowers data professionals by leveraging GPT models to automatically inspect any CSV dataset. It performs data quality assessments, identifies anomalies, generates data dictionaries, computes descriptive statistics and correlations, and produces visual charts. It then creates narrative insights and recommendations. Available as a CLI tool and Python SDK, it integrates seamlessly into Jupyter notebooks or pipelines, accelerating data understanding and decision support without manual scripting.

LuminAI Data Analyst is a Python-based AI assistant tailored for data exploration and reporting. Built on OpenAI GPT and LangChain, it seamlessly ingests structured data files (CSV, XLSX, JSON), performs automated cleaning, computes descriptive statistics, and visualizes key metrics with Matplotlib or Plotly. Users interact via command-line or API, posing questions in plain English to generate customized charts, summary tables, and narrative insights. The agent can export results as PDF or HTML reports and integrate with BI tools. It streamlines repetitive analysis tasks, turning raw datasets into actionable recommendations and saving time for analysts, developers, and business stakeholders. Advanced users can extend its modules or customize prompt templates for domain-specific workflows.

Tomat AI is a versatile no-code data analytics tool tailored for users who work extensively with CSV and Excel files. It offers a seamless, step-by-step interface that allows users to open and analyze large files without uploading them to the cloud. Key features include adding visual steps to filter, sort, or group data, merging multiple files, automating repetitive tasks, and exporting results or building reports. Additionally, Tomat AI leverages advanced AI capabilities to handle complex tasks such as data cleaning, extraction, summarization, and sentiment analysis.

Knowledge-Discovery-Agents provides a modular set of pre-built and customizable AI agents designed to extract structured insights from PDFs, CSVs, websites, and other sources. It integrates with LangChain to manage tool usage, supports chaining of tasks like web scraping, embedding generation, semantic search, and knowledge graph creation. Users can define agent workflows, incorporate new data loaders, and deploy QA bots or analytics pipelines. With minimal boilerplate code, it accelerates prototyping, data exploration, and automated report generation in research and enterprise contexts.