ArkiTask

Designing an AI SaaS tool aimed at simplifying project planning

Secured early-stage funding for a Bay Area startup

BACKGROUND

In early 2023, I was part of a small team of 2 designers and 2 engineers to build ArkiTask, a new productivity platform tool from 0-1. We worked on this project for 3 months, developing an MVP and bringing the product to life. My involvement in this project was largely focused on the product ideation and creation of the visual design and high-fidelity wireframes, mockups, and prototypes of the core project planning flow/feature.

ABOUT THE PRODUCT

The newly founded Bay Area startup wants to support organizations and teams to save time on project planning with AI-powered templates and cost estimates. The tool aims to serve as an intelligent companion, enabling businesses to make smarter decisions regarding task allocation and task management.

CHALLENGE

Designing a brand-new product while making sure it was feasible to build and launch within tight time and technical limits.

TYPE

AI · B2B SaaS · Productivity · Early-stage Startup

MY ROLE

UX/UI, Product Strategy, Visual Design, Prototyping

TEAM

2 Product Designers, 2 Engineers, 1 Founder

TIMELINE

2.5 months

PROBLEM STATEMENT

Organizations are facing challenges with managing their time effectively due to poorly written, estimated, and missing tasks.

Especially for small startups, unclear tasks mean wasted meetings, missed deadlines, and lost momentum.

THE SOLUTION

Use company historical data from similar completed projects to inform and streamline the planning process.

By utilizing ArkiTask's data-first approach to project planning, users can effectively unlock the value of their organizational planning data and streamline the project planning process ultimately reducing the time it takes to compose projects.

View Full Prototype →

Demo of the ArkiTask project creation flow

INITIATE

Understanding the Product

Since ArkiTask was still early-stage, there wasn’t existing research to lean on. I relied heavily on in-depth conversations with the founder and subject matter experts to understand the core pain points and imagine how users would actually engage with the product.

The founder initially shared a set of rough sketches to communicate his vision, which became a starting point. Through weeks of ideation, feedback loops, and iteration, I expanded on those early concepts, evolving both the design and functionality far beyond the original sketches into a more focused, usable product.

Working with Constraints

Because the client needed a polished prototype to pitch to investors and early customers, I had to design with clear boundaries in mind.

  • Time Constraints: With only two months to design and build, I prioritized addressing the core problems first, leaving “nice-to-have” features for later iterations.

  • Technical Constraints: The client’s existing infrastructure limited what we could realistically deliver. By working closely with the founder and engineers, I scoped features that balanced ambition with feasibility, ensuring the prototype was both inspiring and buildable.

Planning for MVP

With only two months to deliver, we needed an MVP that would feel valuable to early users while compelling enough to attract investors. To stay focused, we prioritized task management as the core flow — the heart of the product’s value.

The MVP included essentials: creating projects, adding tasks, and leveraging AI for task clarity, estimates, and assistance. By focusing in on these features, we delivered a flow that was simple, testable, and showed investors the potential for scale.

Mapping the Experience

I created user flows that outlined the core journey: starting a project, adding tasks, and using historical data with AI assistance.

These flows clarified edge cases early, helped align with the founder on scope, and gave us a blueprint for designing a seamless MVP experience.

Key Design Challenges

AI Integration

The challenge was embedding AI seamlessly so I kept it lightweight and contextual, supporting tasks without adding clutter.

Managing Complexity

With complex features, I simplified the flows around task creation and editing so the platform felt intuitive despite the depth.

Technical Constraints

As an early-stage product, we had to balance ambition with feasibility. By scoping features closely with the founder and engineers, I ensured the design pushed the vision forward while staying realistic to build.

DESIGN

Exploring Early Concepts

To establish a shared vision quickly, I started with rough sketches and low-fidelity wireframes. These artifacts made it easy to spark discussion and gather feedback before investing in detail.

Weekly check-ins with the founder and engineers helped us surface technical constraints early and refine ideas collaboratively. Each round of feedback shaped the wireframes into stronger, more feasible designs.

Sketches from the design ideation phase

WIREFRAMES

Iterating to define behaviors and core functionality

As the design matured, I moved from mid- to high-fidelity wireframes and visual mockups in Figma. At each stage, I refined not only how the product looked, but how it behaved.

A key challenge was balancing complexity with clarity: the platform needed advanced functionality without feeling overwhelming. Through multiple iterations, I streamlined navigation so users could find what they needed in fewer steps, while also shaping the AI interactions to feel approachable and trustworthy.

Early wireframes of key screens

ITERATIONS

Balancing user and business goals

Initially, the client wanted every task to require an estimated number of hours before being added. While this would generate useful data for improving accuracy, it quickly became clear the experience was tedious — users had to stop and select hours for every single task.

I advocated for a more balanced approach: tasks and bundles could be added instantly, with estimates generated automatically and editable later. This kept the business need for data intact while making the experience far quicker and more flexible for users.

Other changes and refinements of the Task Selection screens are annotated below:

DESIGN PIVOT

Right-Side Panel Boosts Efficiency 💡

In the final week before handoff, engineers found a way to support a right-side panel — an idea we had explored earlier but initially ruled out due to technical limits. This unexpected opportunity gave us the chance to quickly iterate and integrate it into the final design.

The new panel improved the experience in two key ways:

  • Increased efficiency - Users can access details and actions without leaving their current screen.

  • Better organization - Right-side panel can be used to display additional project and task details, allowing users to better organize their project information.

With the addition of the right-side panel, two screens (previously) consolidate into one single-screen view.

VISUAL DESIGN

UI, Branding & Starting a System

When building the brand and UI for ArkiTask, we prioritized simplicity, intuitiveness, and a native feel for users. A limited color palette was used to avoid visual clutter, aligning with the brand while maintaining a clean interface.

At this early stage, we created a basic style guide covering typography, colors, and UI components to streamline the design process while ensuring consistency. Components were designed to be flexible and scalable, allowing the system to adapt and grow with the product while maintaining a cohesive user experience.

 

FINAL PRODUCT

Streamlining Projects with AI-Powered Efficiency ⚡

ArkiTask brings everything together into an efficient, AI-powered workflow — from planning to execution. By surfacing relevant tasks and generating accurate time estimates, the platform streamlines planning and helps teams move faster with confidence.

01. Starting a New Project with AI Assistance

Users can kick off projects with AI support that suggests relevant past work and helps draft descriptions. These recommendations are fully customizable, giving teams a head start without losing flexibility.

02. Building Tasks from Proven Patterns

Once a project is created, AI suggests tasks drawn from past projects, making it easy to build on proven patterns. Users can add tasks from scratch or quickly pull from tailored recommendations.

03. From Task Creation to Task Selection

Instead of typing tasks one by one, users can select from suggested bundles or individual tasks. Each can be adjusted as needed, with time estimates automatically updating to reflect changes.

04. Exporting to Third Parties

Projects can be exported directly into tools like Asana, Jira, or Monday.com, making it simple for teams to plug ArkiTask into their existing workflows.

IMPACT

Growth & Funding

The final design exceeded expectations: by showcasing a clear, investor-ready workflow, the client doubled their waitlist and secured their first round of funding.

View Full Prototype →

CONCLUSION

Project Takeaways

  • Collaborating closely with engineers is essential for balancing ambition and feasibility.

  • Early-stage constraints—time, technology, and scope—can spark creativity and smarter trade-offs.

  • Rapid iteration within limits sharpened my problem-solving skills and design adaptability.

Next Steps

  • Conduct usability testing to validate task flows and AI interactions.

  • Expand AI capabilities to deliver smarter recommendations and deeper contextual insights.