Despite significant advancements in AI and large language models (LLMs), enterprise organizations have struggled to leverage chat-based approaches for business intelligence effectively. While consumer-focused tools like ChatGPT excel at surface-level tasks, a critical gap remains in applying similar technology for deep data analytics necessary for managing complex data ecosystems in enterprises.
Redbird’s AI Chat Platform
With the launch of its AI chat platform, Redbird addresses this gap through specialized AI agents designed for advanced data analytics. These agents integrate securely with an organization’s data ecosystem, allowing users to engage in natural language chat interactions without needing technical expertise. This approach aims to deliver the self-serve analytics promised by legacy dashboarding tools like Tableau, Looker, and PowerBI, which have often fallen short due to their rigid nature.
Leadership Insights
Erin Tavgac, Co-Founder and CEO of Redbird, stated, “For the past several decades, the promise of truly self-serve analytics has fallen short for organizations, resulting in complex data pipelines and dashboards that require technical skills to execute. We have invested significant R&D into merging the power of LLMs with Redbird’s robust analytical toolkit through AI agents, enabling users to achieve self-serve, conversational BI that runs on their organization’s data.”
Capabilities of Redbird’s AI Agents
Redbird’s platform utilizes proprietary AI agents trained for specific analytical tasks, replicating what specialized human resources do. These agents can perform tasks such as data collection, data engineering, SQL analysis, data science, reporting, and domain-specific analytics. They can orchestrate and execute multi-step analytical tasks, providing accurate responses to user inquiries. Additionally, Redbird allows domain experts within organizations to input business logic and data ontologies into an admin layer, enhancing the AI’s contextual understanding.
Security and Infrastructure Solutions
Redbird addresses infrastructure and security challenges in enterprise AI implementations with turnkey on-premises deployments. These solutions enable LLMs to run in contained environments on the enterprise’s cloud, ensuring all data remains securely within the enterprise’s AI ecosystem and is not used to train LLMs for other organizations.
Market Response and Growth
Throughout 2023, many enterprises observed developments in the LLM space from a distance, uncertain how to integrate the technology into their operations. In 2024, organizations began testing various approaches, seeking viable AI solutions. However, in-house development efforts have proven costly and inefficient due to the complexities of integrating LLM technology with unique enterprise data. Third-party solutions, such as Microsoft Copilot, have also fallen short, focusing on surface-level assistance rather than deeper analytical capabilities. Redbird’s AI product is gaining traction among some of the largest enterprise brands as a practical alternative to complicated in-house builds or simplistic third-party options.
Recent Achievements
Since raising its seed round in 2022, Redbird has increased its customer count by 7X, tripled its team size, and expanded its AI ecosystem atop its core data analytics automation platform. The company is currently collaborating with 8 of the Fortune 50 brands and is onboarding major government organizations in the U.S.
Founders’ Background
Founded by Erin and Deren Tavgac, data analytics and AI experts with extensive experience in large enterprises, Redbird serves clients across various verticals. The company has rapidly expanded its team to include key AI engineering hires, accelerating the development of its AI product.
Redbird is thrilled to introduce its AI product to the market, enabling enterprises to unlock the potential of conversational BI. This launch is a significant step in its mission to democratize data analytics.
