Teams compare performance without rebuilding reports by hand

Kastling owned discovery, architecture, hands-on implementation, deployment and technical direction, with two engineers supporting delivery. The team worked directly with the marketing and performance-marketing stakeholders and coordinated with the internal teams responsible for each source system.

Discovery showed that collecting the data was only part of the job. Reports that looked identical could calculate the same measure differently. Comparing the platform's figures with the existing Power BI reports exposed differences in aggregation logic, so the work included reconciling metric definitions and checking that every transformation preserved the underlying numbers.

The platform connected the Google, Meta and Snapchat advertising feeds, customer and sales sources, internal inputs, Google Sheets and services across Azure and Google Cloud. Automated pipelines collected and prepared the information in PostgreSQL, which served as both the storage layer and the shared data warehouse (one database that every authorized application reads from). Refresh behavior matched each source: near real-time feeds, scheduled jobs and controlled human input each had a place.

Behind the interface, the platform kept the original source records, standardized incoming data and prepared consistent datasets for analysis. Row and column fingerprints (short signatures that change when the underlying data changes) detected what had moved, so incremental refreshes updated only the changed records and preserved the rest. Large workloads ran as smaller recoverable jobs: a failed unit was reported and retried while completed work stayed intact. Validation, duplicate checks and monitoring made exceptions visible.

Users could compare periods, filter by the business dimensions that mattered to them, examine budgets and spend, enter operational data through validated forms and export focused views. Heavy filtering and pagination ran on the server, the interface handled the lighter interactions, and caching kept frequently requested views responsive.

People kept control of operational inputs and business decisions. Authorized staff entered data through constrained forms, administrators managed access, and managers interpreted the results and decided what action to take. Role and division restrictions determined what each user could see. This was an analytical and operational platform, not an autonomous spending or approval system.

The architecture left room for more sources and business domains. Forecasting, fraud detection and further AI capabilities sat on the roadmap, with the trusted data foundation built first. Individual reporting automations reached different stages of completion, and none is presented here as a universally deployed feature.