While consumer-focused tools like ChatGPT have excelled at basic tasks involving general web-based information, applying similar technology to the complex data environments within enterprises has proven difficult.

Redbird’s newly launched AI chat platform addresses this gap with AI agents designed to handle advanced data analytics while securely integrating into an organisation’s data infrastructure. These agents allow users to interact via natural language, removing the need for technical expertise. This brings true self-service analytics to the forefront, something legacy dashboarding tools like Tableau, Looker, and PowerBI promised but struggled to deliver due to the rigidity of traditional dashboards.

“For the past several decades the promise of truly self-serve analytics has fallen short for organisations, with the reality instead being complex data pipelines, dashboards, and shadow analytics that require technical skills to execute” said Erin Tavgac, Co-Founder and CEO of Redbird. “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 organisation’s data.”

Redbird’s AI platform utilises specialised AI agents, each designed to handle analytical tasks typically performed by expert human teams. These agents manage everything from data collection and engineering to SQL analysis, data science, reporting, and domain-specific analytics. By tapping into Redbird’s suite of analytical tools, these AI agents can coordinate and execute multi-step processes to provide users with accurate answers. The platform also features an admin layer, allowing domain experts within the organisation to load business logic, data definitions, ontologies, and assets like presentations or documents, ensuring the AI delivers precise, context-aware results.

Addressing enterprise infrastructure and security concerns, Redbird offers turnkey on-premises deployments that run LLMs within the company’s own cloud environment. This setup guarantees that all enterprise data remains securely contained within the organisation’s AI ecosystem, preventing data from being used to train models accessible to other companies.

Throughout 2023, many enterprises observed the rise of LLMs, uncertain of how to apply them internally. By 2024, businesses have begun testing various AI solutions and allocating budgets to find tools that fit their needs. However, in-house AI projects have often been expensive and ineffective due to the complexities of integrating LLMs with messy enterprise data. Third-party products, such as Microsoft Copilot, have also fallen short, offering only basic assistance rather than deep analytical capabilities. Redbird’s AI platform has gained momentum as a more robust alternative, attracting major enterprise clients frustrated with surface-level solutions.

Since raising its seed round in 2022, Redbird has expanded its customer base sevenfold, tripled its team, and built a comprehensive AI ecosystem on top of its core data analytics automation platform. Redbird now works with eight Fortune 50 companies and is onboarding several large US government agencies.

Founded by Erin and Deren Tavgac, seasoned AI and data analytics professionals with extensive experience across major global brands, Redbird has rapidly expanded its team to include key AI engineers who are driving product development.

Redbird is enthusiastic about launching its AI product, which aims to unlock the potential of conversational business intelligence for enterprises, marking a significant step in its mission to democratise data analytics.