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Case study · E-commerce · AI · UX research

Allie

An AI shopping assistant you can steer, not just ask.

Store chatbots answer with walls of text, forget context, and never ask what matters to you. In a one-week sprint we designed Allie for a fictional app, Skaimart, around one idea: let shoppers tell the AI what they care about.

Full case study coming soon. This is a short version while I rebuild it with the full evidence.

See the full prototype
My role
Product Designer (collaborating): research support, interaction design, prototyping, testing
Team
Nishad Patne (lead) · Gaurav Basantani
Timeline
1 week · Sept 2025
Tested with
20 online shoppers, ages 20–40
Preferred Allie over Rufus
18 / 20

In 30 seconds

The problem

Store chatbots take control from shoppers: no memory, no preferences, walls of text.

The decision

Give shoppers visible controls: a saved AI Profile, in-chat comparison cards, and Allie on every listing.

The result

18 of 20 testers preferred Allie to Amazon Rufus. All 20 named the AI Profile the feature they wanted most.

01 · Challenge

How might an AI help people shop without taking over?

The goal was to guide, the way a good in-store associate does, while leaving the choice with the shopper.

Helpful or gimmick?

Are these bots actually useful?

Associate or obstacle?

Do they feel like help in a store, or do they frustrate?

Guide or gatekeeper?

Do they narrow choices and undermine control?

02 · Competitive audit

We tested 4 real store chatbots on the same 6 tasks.

AmazonRufusGold standard
WalmartSparky
Home DepotMagic Apron
SafewayAsk AI

Even the best one had gaps

  • Tables for product comparisons
  • Repeats the same recommendations
  • Keeps context across screens
  • Occasional hallucinations
  • Cites sources, admits uncertainty
  • Dense output, not really a conversation
No memory

Context is gone the moment you ask a follow-up.

No control

You can't tell the bot what actually matters to you.

Walls of text

Comparisons come as paragraphs, not something you can scan.

Siloed

The bot lives on one screen, not across the journey.

"Users have to cede control to the bot, rather than collaborating with it."Our audit summary

Go deeper: what 30+ articles and 4 papers saidThe secondary research behind our design direction

30+ articles and 4 papers pointed the same way.

Industry data

Personalization can narrow choice

AI-driven personalization can shift decision control away from the shopper, a risk worth designing against.

Academic paper

Great tech, room in the UX

Rufus uses retrieval, fine-tuning, and reinforcement learning. The experience still lags the technology.

Forbes

Retail is betting on agents

Walmart is investing in agentic AI, and shoppers now trust AI picks as much as influencers.

"Assistants must be helpful and steerable, not black boxes."The principle we designed around

03 · Design direction

Three principles.

1

Visible agency controls

Shoppers can steer the bot mid-conversation.

2

Human-attuned interaction

Fast answers, voice or text, and cards instead of walls of text.

3

Transparency over constraint

Explain why each pick fits, and always show alternatives.

04 · The AI Profile

Tell Allie what matters once. It remembers.

A saved, shareable profile that travels with you across sessions, and even across apps.

How to read thisThe preference sliders from the prototype. They move on their own here to show how a profile gets tuned.

Focus chips let shoppers invite surprises, like deals or sustainable picks.

Five-step sliders for price, quality, and brand turn vague wishes into something the AI can act on.

Every recommendation then says why it fits the profile, so shoppers can check the AI's reasoning.

05 · Solution

Three ways Allie keeps shoppers in control.

In-chat comparisons

Side-by-side cards inside the chat: specs, pros and cons, price, ratings. Scan, then tap in.

Ask Allie everywhere

A persistent icon on every listing opens a chat that already knows the item you're viewing.

Portable AI Profile

Set preferences once and carry them across apps, like a companion that knows your taste.

06 · A/B testing

18 of 20 shoppers preferred Allie.

20 online shoppers, ages 20–40. Task: find wireless headphones, first with Amazon Rufus, then with Allie.

In plain wordsEach person tried the same shopping task with two assistants, Amazon's and ours, then told us which one they preferred and why.

Preferred Allie (18)Preferred Rufus (2)
How to read thisEach figure is one participant in the A/B test. Rufus, we still respect you.
18 / 20

Preferred Allie's experience

100%

Chose the AI Profile as their most-wanted feature

1 week

From audit to hi-fi prototype to testing

"I loved that I could actually tell it what mattered to me. Amazon just guesses."Participant, age 26, A/B test, Sept 2025

07 · Learnings

Agency beats accuracy.

  • Letting people control AI behavior builds more trust than accuracy alone. Nobody wants a shopping assistant that "just guesses".
  • On small screens, comparisons need cards, not walls of text.
  • A focused one-week sprint can validate an idea faster than long ideation.

Your move.

Open to Product Designer roles anywhere in the US.

gaurav.inbox29@gmail.com