Fragmented project context
Project information, files, activities, and communication can become separated across different tools.
An AI-powered digital workspace designed to bring productivity and project organisation into a unified application.
01 / Project overview
Kentra is a digital workspace direction for organising projects, files, activities, calendars, messages, notifications, and user profiles in one application.
The project is relevant as an exploration of how a productivity product can combine structured work management with AI-related experiences while keeping the workspace understandable.
Background
Digital work frequently involves fragmented information across project, file, calendar, and communication tools. Kentra was developed to explore a more connected workspace model.
The application format is documented as a progressive web application, making the product direction accessible through a web-based experience while retaining a product-like interaction model.
02 / Problem
The documented problem space is kept close to the available project context.
Project information, files, activities, and communication can become separated across different tools.
Calendars, notifications, and messages are harder to manage when they are not connected to the work they describe.
A productivity product needs understandable boundaries for workspaces, projects, and user activity.
03 / Objectives
Create a connected workspace for organising digital work.
Bring projects, files, activities, and schedules into a unified product direction.
Explore AI-related experiences within a structured application.
Deliver a responsive progressive web application format.
04 / Solution
Kentra approaches the problem as a workspace product rather than a collection of isolated utilities. The documented scope groups projects, files, activities, calendars, messages, notifications, and profiles around the workspace model.
The AI aspect is kept intentionally broad because the available project record does not specify the underlying integrations or models. The case study therefore describes the product direction without inventing AI capabilities.
05 / Features and modules
Provides the documented product direction for AI-related productivity experiences.
Organises digital work around structured workspace and project contexts.
Brings project files and work activity into the documented workspace scope.
Documents scheduling and communication-related product areas.
Provides a documented direction for keeping users aware of relevant activity.
Represents the user identity layer within the application scope.
Scope labels describe the available project documentation; they are not claims that every feature is publicly deployed or production-ready.
06 / Technology stack
Web application framework for the progressive web application direction.
Component-based interface layer for the workspace experience.
Typed application development for structured product features.
Interface styling direction for responsive workspace screens.
Backend platform documented in the project technology context.
Deployment platform documented for the web product direction.
07 / System architecture
The documented stack describes a Next.js and React progressive web application using TypeScript and Tailwind CSS, with Supabase and Vercel included in the project context.
Responsive workspace application layer.
Documented backend and data platform.
Documented web deployment platform.
The AI provider, model integrations, storage configuration, and messaging infrastructure are not specified in the available project information.
08 / My role and contributions
Defined the product concept and application structure.
Planned the workspace-oriented information architecture.
Developed interfaces for the documented web application direction.
Worked on the progressive web application experience and connected product surfaces.
09 / Development approach
Start with the relationships between users, workspaces, projects, files, and activity.
Give daily work a clear home across project, calendar, message, and notification areas.
Shape responsive interfaces around a web application that feels coherent across devices.
Describe AI as a product direction until the underlying integrations are verified.
10 / Engineering insights
Where implementation incidents are not publicly documented, this section records grounded engineering considerations instead of inventing debugging stories.
AI-related experiences are difficult to use when the surrounding work context is unclear.
The documented product scope places AI-related experiences inside a broader workspace model with projects, files, activities, and profiles.
The workspace becomes the organising layer while the exact AI implementation remains explicitly unclaimed until verified.
Product structure and user context are as important as the intelligence feature itself.
11 / Project outcomes
A developed digital workspace application exploring connected productivity experiences.
A progressive web application direction covering workspaces, projects, files, activities, and user-related areas.
AI implementation details and individual feature readiness remain subject to verification.
12 / Project gallery
Real product images can replace this placeholder after the relevant assets are approved.