Just like a phoenix rising from the ashes for a fresh start, Phoenix brings a new life to your data insights. It goes beyond basic reporting, showing you deeper and more detailed patterns and trends that you might not spot on your own.
Phoenix transforms data into actionable insights through AI-led automation and AI-assisted decision-making. Its user-friendly, intuitive interface allows teams to directly engage with data, gain insights, and receive clear summaries without navigating complex reports. Phoenix also uncovers trends and anomalies, turning raw data into concise, understandable insights. By enabling enterprises to shift from traditional BI to AI-driven intelligence, Phoenix keep steams effortlessly informed and empowers real-time, data-driven decision making.
Automate data analysis to reveal trends and anomalies, driving strategic action.
Convert raw data into actionable insights
for informed, fast
decision-making.
Streamline reporting with concise summaries to
keep teams aligned and
well-informed.
Phoenix guides your attention to key insights, helping you identify the issues or opportunities that matter most. With its conversational interface, you can dive deeper for clearer and meaningful insights.
Traditional BI requires lengthy, complex steps; Phoenix simplifies insights with
AI-driven automation and efficiency.
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Phoenix is Onix’s conversational intelligence agent inside Wingspan 2.0, built to bring a new life to enterprise data the way its namesake suggests. It goes beyond basic reporting, surfacing deeper patterns and trends that are easy to miss in a standard dashboard. As AI-powered business intelligence, Phoenix automates the analysis layer that traditionally sits between raw data and a usable answer, so business users get insight without waiting on an analyst to build a report first.
Traditional business intelligence tools require lengthy, complex steps: data prep, dashboard building, and manual interpretation before anyone gets an answer. 40% of analyst time gets consumed by data prep and cleaning alone before analysis even starts. Phoenix replaces that pipeline with a conversational interface. You ask a question in plain language, and Phoenix answers it directly, grounded in the Semantic Twin’s business context rather than a static dashboard someone built weeks ago for a different question.
Phoenix isn’t positioned to replace every BI investment outright. It works alongside Power BI and artificial intelligence tooling you already have, adding a conversational layer on top of governed data so business users can ask questions directly instead of waiting for a new dashboard or report to be built. For organizations with significant Power BI deployments, Phoenix reduces the dependency on analysts to translate business questions into queries, while existing dashboards continue to serve their reporting function.
Executive decisions often stall waiting for the right number to surface from the right system, with the right context attached. Phoenix supports business intelligence and decision-making by retrieving governed metrics, tracing lineage back to source systems, and generating a narrative tied to canonical KPI definitions, all without analyst intervention. A boardroom question like why EMEA revenue dropped three percent last quarter gets answered with the underlying drivers attached, not just a number on a slide.
No. Phoenix is built specifically so business users don’t need SQL, query syntax, or analyst training to get an answer. The interface is conversational by design, states facts with exact numbers, applies business logic to the analysis, and recommends targeted actions where relevant. The goal is closing the gap between people who understand the business question and people who can technically query the data, so those don’t have to be the same person.
This is the central risk with most AI-driven BI tools: confident answers that aren’t grounded in anything verifiable. Phoenix avoids this because it’s powered by the Semantic Twin rather than reasoning over disconnected raw tables. Every insight traces back to governed business metrics and lineage, so answers are explainable rather than black-box outputs. Phoenix also works in coordination with Eagle for business context and Pelican for data quality monitoring, which means the accuracy of an answer is tied to verified data lineage, not a model’s best guess at what the data probably means.
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