Agents4Everything

A business analyst used to file a ticket and wait on the data team for a Q3 performance breakdown. In Agents4Everything, the analyst types the question directly into the workspace. Talk2DB generates the SQL, queries the database, and populates a table and chart widget in the same session in under a minute. The data team's backlog stops growing with every question someone can now ask themselves. 

Extracting findings from a batch of PDFs used to mean opening each file and reading it manually. A user uploads the batch and asks the Multimodal RAG agent to extract key findings instead. The agent processes text, tables, images, and graphs across every file at once, and structured results surface directly into a report widget. Nobody opens an individual document to find the answer

Building a forecasting pipeline used to require a data scientist to write code and configure a job before a single number appeared. In Agents4Everything, a forecasting request typed in plain language is enough. The ML Agent selects the appropriate pipeline, runs it, and renders an interactive chart with historical data overlaid. The forecast is ready before the ticket is assigned.

Assembling a dashboard that combines document summaries, database metrics, and forecasts used to mean stitching together outputs from three separate tools. With Agents4Everything, a dashboard can be built with prompts. RAG-sourced summaries, database-queried metrics, and ML-generated forecasts arrange themselves into separate widgets, populated and ready to share.