Your finance and data systems aren't aligned. We fix that.

A large-scale technology organization was operating with fragmented and inconsistently maintained supplier and spend classification systems, limiting visibility into financial activity and creating friction across finance and operations.

Mijoti led the redesign of the classification framework, working closely with finance stakeholders to define a consistent structure and governance model. A machine learning-based approach was introduced to enable scalable classification of suppliers and transactions, reducing reliance on manual processes.

The result was a more reliable and maintainable system for understanding spend, improving reporting accuracy and enabling better decision-making across teams.

A growing organization was relying on a combination of ERP, HRIS, and internal tools to produce financial and operational reporting. Data definitions were inconsistent across systems, and reporting depended heavily on manual extraction, transformation, and reconciliation.

Mijoti led a diagnostic effort to map how financial and operational data moved across systems and where inconsistencies were introduced. Key issues included mismatched data definitions, unclear system ownership, and reliance on ad hoc processes.

A structured data model and reporting framework was introduced, aligning definitions across systems and reducing the need for manual reconciliation. Reporting workflows were redesigned to be more repeatable and less dependent on individual contributors.

The result was a more stable and trustworthy reporting foundation, reducing time spent on manual processes and improving confidence in financial and operational metrics.