Overview
We expanded a fragmented reporting setup into a unified, AI-driven reporting layer that covers every dashboard data source, applies consistent timeframe filters, and exposes reliable PDF and CSV exports. We connected reporting actions to an AI assistant so users can ask questions, generate reports, and create snapshots conversationally, all while keeping the existing reporting plugins cleanly separated.
Challenges
- Reports and snapshots only covered a narrow slice of the business, leaving projects, contacts, payments, proposals, tasks, leads, and orders missing from reporting output.
- There was no clear inventory of which dashboards, models, and APIs fed reporting, so teams could not tell what data was in scope, what should be excluded, or where PII and volume limits applied.
- Date filtering behaved inconsistently between reports and snapshots, so the same timeframe could produce mismatched results depending on which path a user took.
- Snapshot creation silently dropped date filters because the pipeline never accepted or passed start and end dates through to generation.
- Users lacked simple preset timeframes like last 7 days, last month, or this quarter, forcing manual workarounds for routine reporting.
- Export coverage was uneven across report types, leaving gaps where expected PDF or CSV downloads simply were not available.
- Pulling broad 'all dashboard data' was a performance liability, with large datasets risking slow or failed generation and no guardrails on range size or row volume.
- Reporting and the AI assistant were disconnected, so users could not ask reporting questions or trigger report and export actions through natural conversation.
- Open-ended reporting requirements created scope creep risk, with no discipline around which data sources should and should not be added.
Solution highlights
- Expanded dashboard data coverage to include projects, contacts, payments, proposals, tasks, leads, orders, and other dashboard-backed sources in reporting and snapshots.
- Ran a full dashboard and widget data inventory to map source APIs, models, exclusions, PII considerations, and volume limits before building.
- Added new data categories and aggregated read-only endpoints to feed reports and snapshots without disrupting existing systems.
- Built global date-range filtering with preset timeframes for last 7 days, last month, this quarter, and custom ranges, wired end to end through backend and frontend controls.
- Extended snapshot creation to accept and propagate start and end dates and time-period presets, closing the gap where date filters were ignored.
- Delivered a PDF export path for every generated report and CSV export for snapshots and meaningful report data, with clear download and export actions in the reporting UI.
- Integrated the AI assistant for reporting Q&A and gave it commands to generate PDF reports, export CSV data, and create snapshots through a defined reporting context and action interface.
- Added asynchronous generation, chunking, row caps, and maximum range limits so large-data reporting stays performant and reliable.
- Reused the existing Reports and AI Report Agent plugins and extended their pipelines instead of merging them or building a separate reporting system, keeping the plugin boundary intact.
Outcomes
- Users can now report across the full set of dashboard data sources, with projects, contacts, payments, proposals, tasks, leads, and orders all in scope.
- Reports and snapshots respond to the same date-range behavior, so a chosen timeframe produces consistent, trustworthy results everywhere.
- Snapshots now honor the dates users select, eliminating the silent loss of date filters during creation.
- Preset timeframes for last 7 days, last month, and this quarter make routine reporting fast, with custom ranges available when needed.
- Every user-facing report and data path now exposes the expected PDF or CSV download, removing gaps in export coverage.
- Teams can drive reporting conversationally, asking questions and triggering report generation, CSV exports, and snapshot creation directly through the AI assistant.
- Large-data reporting stays stable thanks to async generation, chunking, row caps, and maximum range limits that prevent slow or failed runs.
- A documented dashboard inventory with defined exclusions keeps reporting scope controlled and prevents unbounded source creep.
- Existing Reports and AI Report Agent plugins remain cleanly separated while sharing aligned date filtering and export capabilities, protecting maintainability.