Inconvo improves data analysis with Langgraph -based dialogue AI

Inconvo, a start -up of Y Combinator S23, uses Langgraph to facilitate natural language queries to convert the environment of data analysis. According to Langchain AI, this innovative approach allows non -technical users to fully perform data analysis.

Problems with data analysis

Many users have difficulty exploring complex business intelligence (BI) tools to extract simple insights from data. Inconvo solves these tasks by eliminating the need for technical knowledge by allowing users to raise questions in natural language. This approach not only saves time, but also improves decision -making ability.

Startups offer a simple API that allows developers to integrate conversation analysis into the application, allowing them to simplify the end -user’s data query process.

Innovative API for data interaction

Inconvo’s Agent Interface supports multiple data visualization methods such as bar charts, bar graphs and tables, providing users to check the interactive method. Once a natural language query is submitted, the API returns the results in the form of JSON so that the user can improve the query and get detailed insights.

This conversation experience allows users to democratize data analysis so that users can do complex tasks without having to learn SQL or other special BI tools.

The role of Langgraph in query processing

Langgraph is essential for the architecture of Inconvo and adjusts the entire data search process. Starting with the resistance of the database, you can understand the schema to determine the data and query methods that you can access. Langgraph manages conditional workflows, runs a variety of tasks based on user input, and ensures fast and accurate results.

The system produces a SQL query to follow the structured reasoning pattern, synthage natural language queries, map the database table and field, and provide the desired output.

conclusion

Using Langgraph, INCONVO has made great progress in destroying the barriers of data analysis. The solution democratizes access to data insights so that users in various sectors can efficiently determine the decisions based on information. This case study emphasizes the potential of the AI ​​-centered solution that improves user experience in data analysis.

For more information, visit the Langchain AI blog.

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