Coreworks AI Raises $5 Million to Build Autonomous “SuperAnalyst” for Business Reporting

Enterprise analytics startup Coreworks AI has announced a $5 million seed funding round led by Together Fund,

alongside the launch of an early waitlist for its AI-powered “SuperAnalyst” platform designed to automate business reporting and presentations.

The company was founded by Prashant Kumar and Pavan Sondur, the founding team behind UNBXD, an enterprise search platform for e-commerce that achieved a $100 million exit and powers nearly 5% of e-commerce search traffic in the United States.

Coreworks aims to tackle a persistent challenge faced by executives, finance teams, and operators: transforming scattered business data into finalized reports and board-ready presentations.

Automating the Final Mile of Business Intelligence

Coreworks’ platform connectsectly to enterprise systems such as ERP platforms, CRM software, financial models, and spreadsheets. From there, the system analyzes the data and generates complete outputs—including financial reports, written analyses, and presentation slides—basedectly on the source data.

Unlike traditional AI tools that produce early drafts, Coreworks is designed to generate finalized deliverables that organizations can use in high-stakes contexts such as board meetings or quarterly reviews.

The system creates reports and presentations that include structured charts, tables, and formulas, while maintaining links back to the original data sources. This allows teams to verify each number and trace how conclusions were generated.

The platform can also regenerate outputs automatically when underlying data changes, eliminating the need for teams to manually update spreadsheets or presentation decks.

From Dashboards to Autonomous Reporting

The emergence of tools like Coreworks reflects a broader shift in enterprise software. For decades, business intelligence platforms have focused primarily on dashboards and visualizations, leaving the work of interpreting data and assembling reports to analysts.

A new generation of AI systems is attempting to automate this final layer of analytical work. By connectingectly to operational systems and producing written insights and presentation materials, these platforms aim to turn raw data into finished outputs with minimal manual effort.

If widely adopted, this approach could reshape how organizations conduct financial reviews, operational reporting, and strategic planning.

Implications for Enterprise Decision-Making

Automated reporting technologies have the potential to significantly shorten the time between data generation and decision-making. Many organizations still rely on manual workflows that require teams to consolidate information across departments before producing reports for executives or boards.

Systems capable of generating traceable reportsectly from live data could accelerate these processes, allowing organizations to respond more quickly to changes in performance or market conditions.

At the same time, the rise of AI-generated reporting introduces new governance considerations. Businesses will need clear mechanisms to verify outputs, maintain audit trails, and ensure decision-makers understand how conclusions were produced.

As enterprises continue integrating AI into their operational workflows, technologies that combine data integration, analysis, and automated reporting may become an increasingly important layer in the modern software stack.