Omni Analytics Inc. today announced that it has closed a $120 million funding round at a $1.5 billion valuation, more than double what it was valued at last year.

ICONIQ led the Series C investment. It was joined by Alphabet Inc.’s GV venture capital arm, Redpoint Ventures and others.

Building a business intelligence dashboard historically required workers to manually prepare a dataset and write SQL queries. San Francisco-based Omni replaces that workflow with a chatbot interface. According to the company, users can generate a data visualization by providing a description of the information they wish to review. They can then enter follow-up questions to zoom into the findings. 

Omni also makes it possible to interact with datasets in other ways. There’s a SQL editor for manually writing queries and a spreadsheet interface. According to Omni, the latter feature has multiple advantages over Excel.

The spreadsheets that knowledge workers use for analytics tasks usually contain data from an external source such as a MySQL environment. When the original version of the data changes at the source system, users must manually sync the changes to their spreadsheets. Omni automates that step. It can sync a spreadsheet to data sources such as Snowflake and refresh it without manual input when records change.

Analytics-related prompts such as those that Omni’s chatbot interface is designed to process sometimes contain ambiguities. If an employee at a retailer asks about store sales, the AI may struggle to determine whether it should retrieve data about the company’s physical stores or its e-commerce website. Omni uses a so-called semantic model to address the challenge. It’s a collection of definitions that describe how the platform’s AI models should interpret business terms.

“When someone asks, ‘How did my team do last quarter?’ Omni’s AI uses shared definitions from the semantic layer,” Omni Chief Executive Officer Colin Zima wrote in a blog post. “It runs as the user to respect granular security. It shows its reasoning.”

A context modeling feature enables users to make similar data definitions available for external AI models. Workers can also provide other details such as the best way to run SQL queries on a dataset. According to Omni, those details make it easier for third-party neural networks to analyze business datasets.

The company’s feature set also covers other use cases. Developers can integrate Omni-powered data visualizations into their custom software and align the graphs’ design with the host application’s interface. AI agents, meanwhile, can interact with the platform via an MCP server.

Omni says that today’s funding round follows a year in which its revenue tripled. The company counts Perplexity AI Inc., dbt Labs Inc. and other prominent tech firms among its customers. 

Image: Omni