Despite general advances in AI and LLMs in recent years, enterprise organizations have largely struggled to successfully use chat-based approaches for business intelligence in a way that is accurate, secure, and customized to their business. While consumer-centric tools like ChatGPT have shown great promise for more surface-level tasks rooted in general information from the internet, there has been a gap when it comes to applying the same technology to the deeper data analytics that enterprises need to run on their complex data ecosystems.
With the launch of its AI chat platform, Redbird is filling this void through AI agents designed to perform advanced data analytics on top of tooling that securely integrates with an organization’s data ecosystem. Users can engage these AI agents in natural language through chat interactions that don’t require technical knowhow. This enables the true self-serve analytics that legacy dashboarding tools like Tableau, Looker and PowerBI have promised but ultimately failed to deliver on given the limitations of a more rigid dashboarding approach.
“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 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 organization’s data.”
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Redbird’s AI platform leverages proprietary AI agents trained to do specific analytical tasks equivalent to what specialized human resources currently do. For example, Redbird has developed AI agents that can do data collection, data engineering, SQL analysis, data science, reporting, and domain-specific data analytics. These AI agents have access to Redbird analytical tools and can orchestrate as well as execute multi-step analytical tasks to answer user questions. Redbird AI has access to an admin layer where domain experts within an organization can load business logic, definitions, data ontologies, and existing assets like presentations or documents that provide the context needed for the AI to produce accurate results.
Redbird also solves for the infrastructure and security challenges involved with enterprise AI implementations through turnkey on-prem deployments that can run LLMs within contained environments on the enterprise’s own cloud. This means that all enterprise data is securely contained within that enterprise’s AI ecosystem and never used to train an LLM for use by other enterprises.
SOURCE: GlobeNewswire