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AI for Observability

Designed AI-powered experiences for Actian’s Observability Platform, building scalable components to simplify workflows, improve trust, and establish a foundation for future development.

Actian Website

Project

Scalable AI UX patterns, AI components

My role

UX Design Intern

Timeline

June 2025 - Aug 2025 (12 Weeks)

New Bike

by Nov, 2025

Analyzed

AI Platform

Up to

Faster onboarding time

Built a reusable AI component system

ensuring consistency and scalability across products

Project Context

As part of my internship at Actian, I worked on a 12-week project focused on designing AI-powered experiences for the company’s Observability Platform— a tool that helps users monitor incidents, analyze logs and metrics, and resolve issues. . The project aimed to create intuitive, consistent, and scalable AI components that streamline workflows and help users resolve issues more efficiently.

As ProArt expanded its product lines, the website needed to scale and support new categories. Through stakeholder interviews, competitive reviews, and usability testing, we identified both user pain points and business challenges.

Data steward 33%

Henry, Data quality owner

I make sure the data just works, because it’s clean, documented, and ready to use.

Goal

Ensure that all data is high-quality, governed, and easy for others to find and understand.

Technical skill: Moderate to high

Low

High

Business user 19%

Christophe, Decision maker

I need a clear, simple path to the data that matters — no SQL, just answers I can trust.

Goal

Access data to inform decisions, and improve communication with business and technical teams.

Technical skill: Low to Moderate

Low

High

Data analyst/Data scientist 48%

Abby, Technical power user

I enable my colleagues. My reports and visualizations are accurate and insightful.

Goal

Produce insights and predictive models for stakeholders using clean, well-documented data.

Technical skill: Moderate

Low

High

Data steward 33%

Henry, Data quality owner

I make sure the data just works, because it’s clean, documented, and ready to use.

Goal

Ensure that all data is high-quality, governed, and easy for others to find and understand.

Technical skill: Moderate to high

Low

High

Business user 19%

Christophe, Decision maker

I need a clear, simple path to the data that matters — no SQL, just answers I can trust.

Goal

Access data to inform decisions, and improve communication with business and technical teams.

Technical skill: Low to Moderate

Low

High

Data analyst/Data scientist 48%

Abby, Technical power user

I enable my colleagues. My reports and visualizations are accurate and insightful.

Goal

Produce insights and predictive models for stakeholders using clean, well-documented data.

Technical skill: Moderate

Low

High

Target Users

We focused on three primary user groups:

Business Users

They want quick, actionable insights with minimal technical effort.

Data Scientists

They value accuracy and transparency when analyzing large datasets.

Data Stewards

They need clarity and consistency to maintain data quality and compliance.

By deeply understanding their goals, needs, and behaviors, we were able to uncover their pain points and translate them into actionable design opportunities.

Data steward 33%

Henry, Data quality owner

I make sure the data just works, because it’s clean, documented, and ready to use.

Goal

Ensure that all data is high-quality, governed, and easy for others to find and understand.

Technical skill: Moderate to high

Low

High

Business user 19%

Christophe, Decision maker

I need a clear, simple path to the data that matters — no SQL, just answers I can trust.

Goal

Access data to inform decisions, and improve communication with business and technical teams.

Technical skill: Low to Moderate

Low

High

Data analyst/Data scientist 48%

Abby, Technical power user

I enable my colleagues. My reports and visualizations are accurate and insightful.

Goal

Produce insights and predictive models for stakeholders using clean, well-documented data.

Technical skill: Moderate

Low

High

Data steward 33%

Henry, Data quality owner

I make sure the data just works, because it’s clean, documented, and ready to use.

Goal

Ensure that all data is high-quality, governed, and easy for others to find and understand.

Technical skill: Moderate to high

Low

High

Business user 19%

Christophe, Decision maker

I need a clear, simple path to the data that matters — no SQL, just answers I can trust.

Goal

Access data to inform decisions, and improve communication with business and technical teams.

Technical skill: Low to Moderate

Low

High

Data analyst/Data scientist 48%

Abby, Technical power user

I enable my colleagues. My reports and visualizations are accurate and insightful.

Goal

Produce insights and predictive models for stakeholders using clean, well-documented data.

Technical skill: Moderate

Low

High

Data steward 33%

Henry, Data quality owner

I make sure the data just works, because it’s clean, documented, and ready to use.

Goal

Ensure that all data is high-quality, governed, and easy for others to find and understand.

Technical skill: Moderate to high

Low

High

Business user 19%

Christophe, Decision maker

I need a clear, simple path to the data that matters — no SQL, just answers I can trust.

Goal

Access data to inform decisions, and improve communication with business and technical teams.

Technical skill: Low to Moderate

Low

High

Data analyst/Data scientist 48%

Abby, Technical power user

I enable my colleagues. My reports and visualizations are accurate and insightful.

Goal

Produce insights and predictive models for stakeholders using clean, well-documented data.

Technical skill: Moderate

Low

High

Problem

Challenges

Incident resolution workflows were fragmented, time-consuming, and lacked intuitive AI guidance.

Strategies

Design AI interactions that feel trustworthy, transparent, and supportive, aligning with user goals.
Reduce workflow friction by embedding AI features directly in the user journey.
Build reusable, scalable components to ensure consistency across the platform.

Why It Matters

Actian aimed to introduce AI features to make workflows faster and more intuitive, bridging the gap between complex data and actionable insight.

Research & Discovery

Evaluated workflows, interaction patterns, and design trends to understand how leading platforms integrate AI.

Methods

Conducted in-depth research and competitive across 12+ AI platforms

Testing

Sent identical prompts to multiple platforms to observe consistency, accuracy, and output clarity.

Concept Development

Defined core entry points

To translate research insights into design direction, I first mapped the AI user journey across the Observability Platform. Through this process, we identified key entry points where users naturally engage with AI assistance—such as during incident investigation, data exploration, or chart analysis.

I broke down the flow into different AI trigger points and interaction hierarchies, exploring where and how users might access or re-engage with AI features without disrupting their current workflow.

Design Process

After defining the user flow and component hierarchy, I began designing modular AI components that could scale across the platform. We introduced three main entry points for AI interaction:

Primary Ask AI Button – a global access point for general queries.
Contextual Triggers – secondary AI buttons within tables, charts, or incident details, tailored to the user’s current context.
Side Panel Access – an expanded interaction space for deeper investigation and follow-up prompts.

Primary Ask AI Button

A global access point for general queries

Contextual Triggers

Secondary AI buttons within tables, charts, or incident details, tailored to the user’s current context

Side Panel / Drawer

An expanded interaction space for deeper investigation and follow-up prompts.

Primary Ask AI Button

A global access point for general queries

Contextual Triggers

Secondary AI buttons within tables, charts, or incident details, tailored to the user’s current context

Side Panel / Drawer

An expanded interaction space for deeper investigation and follow-up prompts.

Primary Ask AI Button

A global access point for general queries

Contextual Triggers

Secondary AI buttons within tables, charts, or incident details, tailored to the user’s current context

Side Panel / Drawer

An expanded interaction space for deeper investigation and follow-up prompts.

Primary Ask AI Button

A global access point for general queries

Contextual Triggers

Secondary AI buttons within tables, charts, or incident details, tailored to the user’s current context

Side Panel / Drawer

An expanded interaction space for deeper investigation and follow-up prompts.

Iterations

During the iteration process, I refined these components through microinteraction design and visual language alignment.

Applied gradient animations and iconography to visually cue AI presence.
Ensured consistency with Actian’s existing design system, maintaining harmony with other product components.
Simplified transitions between popover and side panel states for a smooth, intuitive experience.

Final Design

The final design brings together a set of AI-driven components that work seamlessly across the Observability Platform to make workflows faster, clearer, and more intuitive. Each feature was carefully designed to feel human, contextual, and responsive, while staying consistent with Actian’s design system.

Key Features - 1

Microinteractions

Subtle animations and gradient color transitions bring the AI to life. The flowing gradient — shifting between purple and blue — signals system activity and provides real-time feedback when AI is processing or loading. These microinteractions draw attention without distraction, making the experience more engaging, responsive, and trustworthy.

Key Features - 2

Contextual-Driven Prompts

Each prompt is generated based on the user’s current context — whether viewing a chart, exploring an incident, or selecting a data field. Instead of forcing users to start from scratch, the system suggests relevant follow-up questions or actions tied to what they are already looking at. This approach helps users stay in flow and reduces effort, making AI feel like a natural extension of the workflow.

Key Features - 3

Follow-Up Interaction

When a user selects specific content, a Follow-Up Button appears inline to encourage deeper exploration. Once clicked, the system highlights the related context with a small inline indicator, clearly showing what the follow-up refers to. This transparent linking between context and response helps users understand the relationship between their actions, the AI input, and the generated outcome.

Reusable Components

As part of building a consistent and scalable AI experience, we developed a set of reusable AI components that could easily integrate into Actian’s existing design system. The goal was to create flexible modules that could adapt to different use cases across the Observability Platform and serve as a foundation for future AI features.

We proposed three layouts, testing each with stakeholders for user experience and technical performance.

Why Layout C?

Showcased a wider range of collaborations, offering diverse content.
Used a carousel gallery for a dynamic, user-friendly experience.
Delivered smoother animations and faster load times across devices.

Delivery

To ensure consistency and scalability, we developed a modular design system:

Core Styles: Defined typography, color palettes, and grid system aligned with ProArt’s brand identity.
Reusable Components: Built a flexible library of cards, buttons, carousels, and testimonial modules.
Scalability: Enabled rapid iteration and seamless integration across multiple pages.

In the delivery phase, I focused on turning our design work into clear, actionable assets that could be easily implemented and scaled by the engineering team. This process included three key parts — Component Library, Design Specs & Documentation, and Design Handoff & Collaboration

Component Library

I built a Figma component library containing reusable AI components aligned with Actian’s design system. Each component was built using shared tokens and defined variants for different interaction states and contexts, ensuring visual consistency and scalability across the platform.

Design Specs & Documentation

Handoff & Collaboration

Component Library

Design Specs & Documentation

Handoff & Collaboration

Component Library

Design Specs & Documentation

Handoff & Collaboration

Component Library

Design Specs & Documentation

Handoff & Collaboration

Reflection

Looking back, this internship was an incredible learning journey that helped me grow both as a designer and collaborator. I learned how to balance detail-oriented design work with big-picture thinking, adapt design trends into complex product environments, and communicate effectively across teams. Working on AI experiences taught me the importance of designing for clarity and trust, especially in fast-evolving technologies. Most importantly, I gained confidence in turning research into practical, scalable design systems that make a real impact.