We designed and coded FlowSignal for teams that need better visibility across everyday workflows. The dashboard brings tasks, status updates, handoffs, and performance signals into one interface so managers can understand where work is slowing down.
The AI layer reviews operational patterns, flags repeated delays, summarises active issues, and suggests automation opportunities that can be prioritised by impact. Instead of replacing existing tools, it helps teams make better decisions from the information they already have.
Tech stack
FlowSignal was planned as a dashboard application with a TypeScript frontend, API-based data ingestion, and a backend that can normalise workflow signals from project management, ticketing, CRM, and spreadsheet systems. The interface focuses on status visibility, bottleneck detection, priority scoring, and clear recommendations rather than abstract AI reports.
The AI layer analyses task metadata, repeated handoffs, overdue items, workload patterns, and operational notes to identify where automation or process changes would have the biggest impact. The stack supports scheduled data syncs, role-based access, audit logs, notification hooks, and integrations with tools such as Slack, Asana, Jira, Airtable, or custom internal systems.
The result is a practical management tool for improving delivery, reducing manual follow-up, and identifying where AI automation can create the most useful operational gains.