IdeaboxAI
Conversational agents for automating business workflows
IdeaBoxAI is a B2B platform that lets teams create no-code AI agents for improving operational efficiency
- using conversational interfaces, knowledge bases, and API integrations
Role
Product Design
Design System
Visual Design
Team
Founder
Technical Architect
6 Engineers
2 Designers
Timeline
Nov 2024 - Aug 2025
Tool
Figma
Jira
Impact
Shipped the MVP in one year, securing 2 enterprise clients.
Product Overview
Problem
“Automation tools exist, but they’re not built for the teams who actually run operations”
Operational teams across sales, customer support, and supply chain still rely on repetitive manual tasks across multiple tools. While automation exists, most solutions require technical setup, making them difficult for non-technical teams to use.
Example
A 'supply chain' manager might manually email vendors to check shipment status, update spreadsheets, and coordinate deliveries.
A 'sales manager' may need to pull data from multiple systems just to answer a simple question like, ‘What were our sales orders last week?
Opportunity
Make automation accessible through conversational AI
Make automation as simple as a conversation. Instead of configuring workflows, teams can describe what they need and let the system handle the complexity.
System Design
Digging into the backend to design the frontend
To design the platform experience, I worked with product owners and engineers to understand how the underlying system operated - including agent orchestration, knowledge retrieval, and integrations with external tools and APIs.
This helped me translate the backend architecture into a clear user-facing system where teams could build AI agents, connect data sources, and automate workflows without writing code.
Core Workflows / Solutions
How teams build and run AI agents
I collaborated closely with product and engineering teams across multiple sprints to define how users create, configure, and interact with AI agents. As the platform evolved, I continuously refined interaction patterns to align user needs with emerging technical capabilities.
Build and interact with custom AI agents
Teams can create agents tailored to their workflows and immediately interact with them through a conversational interface to automate tasks and retrieve insights.
Connect multiple knowledge sources to a single agent
Agents can be powered by multiple knowledge bases, combining information from documents, Google Drive, YouTube, databases, and other sources to provide richer and more accurate responses.
Automate operational workflows
Users can configure automation workflows that allow agents to trigger actions across systems and streamline repetitive operational processes.
1. Add Trigger
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2.Configure Scheduler
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3. Connect data source & proceed with iteration
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4. Assign AI Agent
Design Decisions
Improving usability and scalability through design decisions
Strengthening the design system and accessibility
When I joined the project, the product owner asked me to re-evaluate the design system as the existing colors felt muted and lacked visual hierarchy.
I introduced a refreshed color system with stronger contrast and designed both light and dark modes to improve accessibility. I also established reusable UI components—from navigation to layout patterns—to ensure visual consistency as the product scaled.
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Improving transparency in conversational AI responses
Usability testing revealed that users struggled to trust the agent’s responses because the reasoning steps were high levell and lacked context about where the data was coming from.
I redesigned the reasoning experience to expose the sources behind each step. Users can now see which tools, database tables, or knowledge sources the agent consulted, and inspect the underlying details when needed.
Before
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After
Standardizing API tool configurations
Engineers provided the technical parameters and requirements for each integration. My role was translating that complexity into a consistent, user-friendly configuration experience and creating reusable design patterns that could scale across the product
Reflection
Designing IdeaBoxAI reinforced that AI products are not just interface problems - they are system design challenges that must remain understandable to users.
Great AI experiences aren’t built by hiding complexity, but by making it understandable when it matters.
Working closely with product owners and engineers showed me how system architecture directly shapes the user experience. It reinforced the importance of understanding the logic behind the system and translating complex workflows into intuitive, trustworthy interactions.
Let's Build
Together
© 2026 Shimona Roy