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Despite significant advancements in artificial intelligence (AI) and large language models (LLMs) over recent years, enterprise organizations have faced challenges in applying chat-based technologies for business intelligence (BI) in a way that is accurate, secure, and tailored to their specific needs. While consumer-facing tools like ChatGPT have proven effective for general information tasks, they fall short when it comes to the complex data analytics enterprises require for deeper insights into their data ecosystems.
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Redbird is addressing this gap with the launch of its AI chat platform, designed to perform advanced data analytics while securely integrating with an organization’s existing data infrastructure. Redbird’s AI agents allow users to interact via natural language without needing technical expertise, enabling true self-serve analytics—something legacy tools like Tableau, Looker, and PowerBI have promised but failed to deliver due to the rigidity of traditional dashboards.
“For decades, the promise of self-serve analytics has fallen short, burdening organizations with complex data pipelines, dashboards, and technical requirements,” said Erin Tavgac, Co-Founder and CEO of Redbird. “We’ve invested heavily in R&D to fuse LLM capabilities with Redbird’s analytical toolkit, creating AI agents that finally enable conversational BI, running directly on the organization’s data.”
Redbird’s proprietary AI agents are trained for specific analytical tasks, akin to what specialized human teams currently handle. These tasks include data collection, engineering, SQL analysis, data science, and domain-specific analytics. The platform allows AI agents to access an organization’s business logic, data ontologies, and existing assets such as presentations or documents, ensuring accurate, context-driven results.
To address the security challenges of enterprise AI deployments, Redbird offers on-premise solutions that enable organizations to run LLMs within their own cloud environments, ensuring that all enterprise data remains secure and never used to train external AI models.
In 2023, many enterprises observed the development of LLM technology but hesitated to fully integrate it. In 2024, companies are beginning to test solutions and allocate budgets for AI implementations, though in-house builds have proven costly and complex. Third-party tools like Microsoft Copilot have also fallen short, offering only surface-level assistance. Redbird’s AI platform is gaining traction as a deeper, more effective alternative, quickly being adopted by leading enterprise brands.
Since its 2022 seed round, Redbird has seen rapid growth, increasing its customer base sevenfold, tripling its team size, and expanding its AI ecosystem on top of its core analytics platform. Redbird is now working with eight Fortune 50 companies and is onboarding some of the largest U.S. government organizations.
Founded by data analytics and AI experts Erin and Deren Tavgac, Redbird serves enterprise customers across multiple industries. The team has rapidly expanded with key AI engineering talent to further enhance Redbird’s AI product development.
Redbird is excited to bring its AI-powered platform to market, helping enterprises unlock the potential of conversational BI and take a significant step toward democratizing data analytics.
