As enterprises increasingly turn to large language model (LLM)-powered text-to-SQL solutions to interact with their data, a new trend is emerging: AI agents. New York-based Redbird has launched a chat platform utilizing “specialist agents” designed to streamline the analytics value chain—from data collection and engineering to data science and reporting.
This innovative platform allows enterprise users to input natural language prompts to obtain insights from data in nearly real-time, significantly enhancing analytics efforts. Erin Tavgac, co-founder and CEO of Redbird, states that this advancement could alleviate over 90% of an enterprise’s business intelligence workload.
"For decades, self-serve analytics have fallen short due to the complexities of data pipelines and dashboards that require technical skills," Tavgac explained. "Our significant investment in R&D merges the power of LLMs with Redbird’s comprehensive analytical toolkit, enabling users to achieve self-serve, conversational business intelligence based on their organization's data."
The Rise of AI Agents
Although the concept of AI agents is relatively new, Redbird has been an established player in the analytics landscape since its inception in 2018 as Cube Analytics. Initially offering a no-code, drag-and-drop toolkit for automating various analytical tasks, Redbird recently introduced a conversational interface that allows users to ask business questions in natural language, generating real-time insights and reporting outputs.
Now, the company has enhanced its platform with an ecosystem of specialized agents that run atop this end-to-end toolkit, enabling the orchestration and execution of multi-step analytical tasks to address business inquiries effectively.
Admins can customize the chat platform by selecting a base LLM (such as GPT or Llama) and integrating their organization’s proprietary data ontologies and business logic. This personalization is crucial for contextualizing the agents' responses.
User prompts are routed to Redbird’s agents, which identify suitable specialist agents to execute the necessary tasks—whether it be a PowerPoint Reporting agent or a Data Engineering agent. Each agent manages its segment of the overall task, utilizing relevant datasets and executing necessary operations through Redbird’s toolkit.
Versatile Data Management
Redbird's agents are capable of extracting both structured and unstructured data from over 100 sources, including Snowflake, Databricks, and Hubspot. They perform advanced data processing, such as data wrangling, AI-driven tagging, and modeling, all while generating comprehensive reporting outputs like presentations and updates for email or Slack. Post-execution, the chat platform not only delivers a text response but also provides any necessary deliverables, such as generated reports or collected data.
Maintaining No-Code Workflow Options
As organizations amplify their data initiatives, moving beyond just text-to-SQL functionalities, Redbird’s AI agents present a promising avenue for refining the entire analytics pipeline. Despite some lingering concerns about AI reliability, Redbird maintains its original drag-and-drop interface as a secondary option for users. The agents orchestrate tasks while also creating a no-code version of the workflow for audits and inspections as needed.
Tavgac noted, “Current AI solutions have primarily automated only a small fraction of business intelligence (BI) and analytics tasks, mainly SQL querying. While we address that use case with text-to-SQL, our AI agents tackle the more complex aspects of enterprise BI workflows. This approach has attracted eight Fortune 50 brands and over 30 mid-to-large-sized enterprise customers recently, including names like Mondelez International and Johnson & Johnson.”
Redbird offers its technology as a software-as-a-service (SaaS) model with usage-based licensing, currently generating seven-figure revenue, though exact figures were not disclosed. The next phase for Redbird includes further deployment of its AI agent-driven platform and the addition of more advanced agents to enhance AI-powered business intelligence for non-technical users.
Looking ahead, Redbird aspires to broaden its focus beyond analytics and BI use cases, adopting a “Large Action Model” approach that utilizes AI agents to perform nuanced actions based on analytical outcomes—such as purchasing supplies or sending invoices.
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