Financial reporting, handled by machines that understand context
cuwebua builds AI-driven systems that turn raw financial data into structured, audit-ready reports - without the manual overhead that slows most teams down.
What this service actually does
Most financial reporting tools generate output that still needs significant human correction - misclassified line items, inconsistent period comparisons, formatting that breaks downstream. cuwebua was built specifically to close that gap.
The system ingests structured and semi-structured financial data, applies contextual reasoning to categorise and reconcile figures, then produces reports formatted for specific use cases: board presentations, regulatory submissions, or internal management review. The output is consistent and traceable.
Context-aware categorisation
The model distinguishes between revenue types, cost centres, and exceptional items based on account naming patterns and transaction history - not just chart-of-accounts position.
Period reconciliation built in
Comparative periods are aligned automatically. Where data gaps exist, the system flags them explicitly rather than silently filling with zeroes or prior-period values.
Aoife Brennan
Lead AI Systems Analyst
Designs the data ingestion and classification pipelines. Previously worked on regulatory reporting automation at a mid-tier Irish accountancy firm for six years.
Tadhg Ó Floinn
Financial Data Engineer
Manages source integrations and output schema design. Focused on making sure reports produced by the system match the exact format each client's downstream process expects.
Sorcha Dempsey
Client Solutions Specialist
First point of contact for new engagements. Handles requirement scoping, regional compliance questions, and ongoing communication throughout the reporting cycle.