Redbird supercharges analytics pipeline with AI agents, handles 90% of workload

Just as enterprises continue to adopt large language model-powered text-to-SQL as a way to ‘talk’ to their data assets, a new shift in the ecosystem has started emerging: AI agents. Today, New York-based Redbird announced a new chat platform that uses “specialist agents” to help enterprises handle most analytics value chain tasks, from data collection and engineering to data science and producing actual insights (reporting).

This means an enterprise user can give a natural language prompt to get insights from data in almost real-time and execute analytical efforts that pave the way for those insights. According to Erin Tavgac, the co-founder and CEO of the company, this represents more than 90% of an enterprise’s business intelligence efforts.

“For the past several decades the promise of truly self-serve analytics has fallen short for organizations, with the reality instead being complex data pipelines, dashboards, and shadow analytics that require technical skills. We have invested significant R&D into fusing the power of LLMs with Redbird’s robust end-to-end analytical toolkit in the form of AI agents that enable users to finally achieve self-serve, conversational BI that runs on their organization’s data,” he said in a statement.

Moving into the age of AI agents

While the age of AI agents is new, Redbird itself has been a long-standing player in the analytics domain. The company started in 2018 as Cube Analytics and provided enterprises with a no-code, drag-and-drop toolkit that enabled their users to create workflows aimed at automating and unifying all analytical tasks leading to dashboarding and insights. Earlier this year, the company expanded this work with the launch of a conversational interface, allowing users to ask business questions in natural language and receive insights and reporting outputs in real-time.

Now, as the next step, Redbird has added an ecosystem of specialized agents that operate on top of this end-to-end toolkit to orchestrate as well as execute multi-step analytical tasks to answer business-related questions.  

As Tavgac explained, admins setting up the chat platform have to choose a base LLM (like GPT, Llama etc) and load up their organization’s proprietary data ontologies, business logic and reporting blueprints (like business definitions, PowerPoint report templates, etc.) to customize it with relevant business context. Once the data is inputted, the AI agents using the LLM begin to use all the context and generate metadata from the information to do their work  — in response to user questions.

“User prompts are sent to Redbird routing agents, which identify the best specialist agents to execute the tasks for that prompt (like PowerPoint Reporting agent, Data Engineering agent, etc.) and figure out how to orchestrate the execution order of those agents. Each specialist agent then manages its own part of the overall task by identifying relevant datasets/ontologies and executing the needed task using the Redbird toolkit, which includes applications and functions to handle the mechanical steps of the pipeline,”  Tavgac noted.

Detailing the tasks, he noted Redbird agents can pull unstructured or structured data from over 100 data sources, including Snowflake, Databricks and Hubspot. It can run advanced processing on top of the collected data by performing data wrangling, AI-driven tagging and data science modeling. It can also generate robust reporting outputs (like presentations, Excel reports and email/Slack updates) while taking necessary actions based on those reports (like executing an ad buy/modifying a campaign).

“Once the task is executed, the chat platform responds to the user with not just a text answer but also any deliverables needed, like a PowerPoint report the agents built or the data that they collected from a SaaS system,” he said.

No-code workflow orchestration remains available

As enterprises double down on their data efforts, going beyond text-to-SQL — adopted by Dremio, Snowflake and many others – and streamlining the analytics pipeline end-to-end with AI agents could be a great way to save time and resources. 

However, as many may still have concerns over the reliability of AI agents, Redbird is not doing away with its original drag-and-drop interface for automating business intelligence workflows. Instead, the company has made no-code the secondary option for users. The agents will orchestrate the tasks while also creating a no-code version of the workflow, allowing users to audit and inspect everything in detail if required. 

“So far, existing AI solutions have primarily tackled the automation of a very small fraction of BI and analytics efforts (SQL querying). While Redbird values and solves for that use case (text-to-SQL), it is also applying the power of its AI agents to automate the other more difficult and more sizable parts of enterprise BI workflows… Our approach to solving this challenge has enabled us to onboard eight of the Fortune 50 brands and over 30 mid-to-large-sized enterprise customers in the last few months,” Tavgac added. This includes brands like Mondelez International, USA Today, Bobcat Company and Johnson & Johnson.

Currently, he said the company is offering its technology on a SaaS model with usage-based licensing fees and generating seven-figure revenue. However, he did not share the exact specifics.

As the next step, Redbird will continue its AI agent-driven work and take its new Chat platform to more enterprises. It also plans to add more advanced agents in the analytics value chain to enable even deeper AI-powered business intelligence coverage for non-technical users. 

“We also aim to expand beyond our primary focus on analytics / BI use cases and into a deeper ‘Large Action Model’ approach that leverages AI agents that can take more nuanced action based on the analytical results (i.e. purchase supplies, send invoices).