Information Architecture for a 0→1 AI Product

Information Architecture for a 0→1 AI Product

Audit findings that shaped a new product's information architecture, navigation, and feature scope

Audit findings that shaped a new product's information architecture, navigation, and feature scope

Client

Validity

Industry

AI MarTech

Timeline

2.5

Role

Lead designer
Lead researcher

Robot stack

CHALLENGE

Litmus had no structured IA review in over 10 years.

My study surfaced structural problems that hurt usability, discoverability, and customer satisfaction.

Midway through the project, company priorities shifted to Validity Engage, a new AI-powered email testing platform.

The research had been grounded in Litmus, but its findings carried over. They gave me a clear starting point for Engage's information architecture, navigation, and feature scope.

APPROACH

With 1 month and no budget, I designed the study for maximum signal; leaning heavily on our internal team and leveraging AI to speed up analysis and prototyping.

With 1 month and no budget, I designed the study for maximum signal; leaning heavily on our internal team and leveraging AI to speed up analysis and prototyping.

With 1 month and no budget, I designed the study for maximum signal; leaning heavily on our internal team and leveraging AI to speed up analysis and prototyping.

  1. Week 1

    Product audit

    Where does the current structure break down?

  2. Weeks 2-3

    Internal interviews

    How do our marketing team members work around the current structure?

  3. Weeks 2-3

    Internal card sort

    How do our internal team members group features?

  4. Week 3

    Internal survey

    What do customer-facing teams see customers get stuck on?

  5. Week 4

    Lo-fi testing

    Can 5 real customers find what they need in the proposed structure?

KEY FINDINGS

Litmus's IA was never designed to scale.

The existing structure couldn't scale new features without compounding existing problems, so every addition made the product harder to navigate.

Users can't predict how navigation works
Users can't tell where they are
Users don't know which way to go
Users don't speak the product's language

GUIDING PRINCIPLES

Evolving the IA

The existing structure couldn't scale new features without compounding existing problems, so every addition made the product harder to navigate.

One consistent nav pattern, used everywhere
Breadcrumbs and active states on every page
A single clear path to every task
Language customers already use

INTRODUCING VALIDITY ENGAGE

Pivoting to a new platform

Company priorities shifted, and the planned changes to Litmus were discontinued. We applied what we'd learned to Validity Engage instead, starting from one question: knowing what we know now, what would we do differently?

NEW

Foundational patterns

Foundational patterns

We applied the research findings to the new platform's core patterns.

We applied the research findings to the new platform's core patterns.

Clear hierarchy

Consistent navigation, header, and breadcrumb structure to orient users

Built to scale

Left navigation accommodates room for complex nav structures

Responsive shell

Page framework stays consistent across all widths and viewports

MENTAL MODEL MISMATCH

Collaboration in context, not a destination

The collaboration options in Litmus sat isolated at the end of a 6-step workflow.
Survey responses and card sort patterns surfaced the need to have these throughout the workflow, rather than a standalone destination.

A

6-step workflow

, now just 2

now just 2

I built and tested this prototype with 5 customers using Claude Code, validating the navigation structure and the collaboration features before Engage's build timeline was set. The embedded collaboration actions held up, task success rate, time on task, and navigation depth all performed well against the new structure.

I built and tested this prototype with 5 customers using Claude Code, validating the navigation structure and the collaboration features before Engage's build timeline was set. The embedded collaboration actions held up, task success rate, time on task, and navigation depth all performed well against the new structure.

KEY FINDINGS

Outcomes

When company priorities shifted, I carried the findings into the new platform, establishing a structurally sound IA and outlining a navigation structure built to hold up as new functionality was layered in.

As the platform designer, my focus was the customer experience across the ecosystem. Onboarding and PLG were my next priorities to move people through the product smoothly, alongside further developing the design system to standardize the product as it grew.

My employment ended before release, so I can't speak to performance.

5-part research study delivered in one month
Foundational navigation patterns built and shipped in MVP
Collaboration framework validated, de-risked, and scoped for a post-MVP sprint
Carried findings over across pivot, avoiding rework.

Open to new oppotunities

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