Riddhima Sharma, UX Designer: agentic UX, behavioural design and illustration

01
Overview

About Riddhima

7 years of design, translating complex challenges into high-craft digital experiences.

02
Commerce Project

Personalized Retail

Multi-billion-dollar client win • Personalized agentic wardrobe

03
Fintech Project

Agentic Onboarding

B2B Platform • Compressing Enrolment by 80%

04
Enterprise AI Project

RFP Agent

Helping firms answer requests for proposals • In build

Riddhima Sharma

Toronto Canadian permanent resident

UX Designer

@ Deloitte

2022–Present

Product Management Mentor

@ MyCaptain

2025–Present

Behavioural Design

Product Building

Agentic UX

Storytelling

Illustration

Riddhima Sharma

Toronto Canadian permanent resident

  • UX Designer

    UX Designer

    @ Deloitte

    2022–Present

  • Mentor

    Product Management Mentor

    @ MyCaptain

    2025–Present

  • Rated 4.6 as mentor
    MyCaptain: Captain Rating 4.6, Workshop Rating 4.6, five stars
  • Personalized Retail

    Personalized Retail

    Won a multi-billion-dollar client

  • Agentic Onboarding

    Agentic Onboarding

    About 80% less effort, now in build

  • RFP Agent

    RFP Agent

    Every claim tied to a source

My Wardrobe • Pitch case study

Personalized Retail

My Wardrobe, a personal shopping agent that asks before it acts, won a heritage retailer as a Deloitte client: a multi-billion-dollar account.

01 · Before the brief

Moodboards feel personal. Online stores feel like billboards.

In August 2025, a product manager and I kept asking one question: why does a moodboard feel so personal? It says: this is made for you. A store’s homepage says the opposite, with the same banners for everyone.

We turned the question into a blue-sky concept: a homepage made of cards, each run by an AI agent that learns one shopper. We prototyped it with an internal AI team.

Two ideas from it carried into this pitch: agents the shopper can see, and the shopper staying in charge of them. You chose which cards to keep, and a card you removed could come back for 30 days.

02 · Research

Gen Z wants a store that knows them, and trusts people over brands.

Then a heritage fashion retailer asked for a pitch: it was losing Gen Z to stores that feel made for them, and we had two weeks. A team of five spent the first days on the retailer’s brand and five competitors. Customer-feedback data from HundredX showed where shoppers struggled.

Taylor’s group chat

Persona · nine friends, 16 to 20, US

They dress for each other and ask each other first, but every store shows them the same thing. They’re overwhelmed by generic curation.

  • Ask the group chat first
  • Swipe without thinking
  • “Why is this store for everyone?”

What the data said

  • 84%expect a store customized to them
  • 76%trust peers and creators over brand ads
  • 92%are fluent in swipe interfaces

Sources: HundredX customer feedback and secondary research, Oct 2025

Where shoppers struggled, and where it led

  • Sizes are inconsistentso people order two and send one backFit Twin
  • Recommendations are generic“You may also like”, for everyoneSwipe to teach
  • Shoppers trust shoppersmore than the brand’s own photosReal people wearing it
  • Stock is hard to trackyour size sells out before you decideDrop Watch

Key insightPersonalizing the whole experience, not just the product recommendations, was the opportunity nobody had taken.

Three rules we designed by

  1. Personal, never creepy.It learns only from what you do in the store, shows you what it learned, and forgets on request.
  2. Teach it by doing.Swipes, keeps and returns teach it. No profile forms.
  3. Suggest freely, act only with permission.The more it does for you, the more it asks first.
How we researched: brand, competitors, market
  1. Brand auditStory, values, visual assets
  2. Competitor auditFive brands, side by side
  3. SWOTThe retailer’s position
  4. PESTELForces shaping retail
  5. Porter’s five forcesCompetitive pressure
  6. Customer dataHundredX feedback
Competitive pressurePorter’s five forces, as rated in our analysis. Hover a row for the reason.
ForcePressure
Rivalry between brandsHigh
SubstitutesHigh
Buyer powerHigh
New entrantsMedium
Supplier powerLower*

*Not rated in the original analysis; described as reduced by a large supplier base.

03 · Where it lives

Decision 1 of 3

A feature first, not a new homepage.

Our blue-sky concept replaced the whole homepage. For a retailer with years of loyal shoppers, that was too big a first step: we hadn’t yet seen a single shopper use it. So we put it inside the store they already know, as one feature, and left the homepage to follow if shoppers kept coming back.

Held back

A personal homepage

Every shopper’s homepage becomes cards run by agents, as in the blue-sky concept. A big bet before anyone had used it, and it takes away the store regulars know.

Ruled out

A shopping chatbot

Quick to add, but it can’t see fit, returns or stock, and it can’t act. Shoppers would have to type everything it should already know.

Chosen

My Wardrobe, inside the store

One corner of the store shoppers already use, a tap from the homepage and the tab bar. Easy to try, easy to measure, and room to grow.

9:41●●●
⚲ ♡

New
seasonShop the drop →

HomeShopBag
Why it has to live in the store: what each can work with
CapabilityGeneral chatbotTidewell agents
Your fit, from what you kept and returned✕ No✓ Yes
What’s already in your wardrobe✕ Only what you type✓ From your orders
Live stock in your size✕ No✓ Yes
Acting for you: hold an item, add to bag✕ No✓ With your OK

04 · How it learns

Decision 2 of 3

Teach it by doing, and always show what it learned.

No profile forms. Shoppers teach Style Buddy the way they already shop: swiping, keeping, sending back. In return it keeps nothing hidden. Every preference it holds is on one screen, and each one can be forgotten.

9:41●●●
Swipe & decide9 / 12
Boxy hoodie, sage

Boxy hoodie, sage$58

Retro runner

Retro runner$72

Coach jacket, cream

Coach jacket, cream$74

Buddy is learning…
Swipe to teach

Borrows: swipe culture

Before browsing, shoppers swipe on up to 12 products. Each swipe trains the agents, using a gesture Gen Z already has muscle memory for.

05 · How much it may do

Decision 3 of 3

The more it does for you, the more it asks.

We sorted every feature by how much the agent does on its own. At the bottom it only answers. At the top it holds an item for you, inside rules you set, and still never buys. Each step up adds one permission and one more way to say no.

May
Understand a full sentence and show what matches.
Never
Change anything you own or saved.
You stay in control
You read the results and choose. It shows how it read you.
9:41●●●

Understood as

CozyGig nightRain-ready≤ $60
Windbreaker, khaki
Heavy hoodie
Trail runner
Colour-block tote
Search like you talk

Borrows: texting

Search understands full sentences and feelings, not just keywords. Maya searches the way she thinks, and the store works out what she means.

When it gets it wrong Added after the pitch

  • The size was offThe S Fit Twin suggested came back too tight. One tap on “Not quite” tells it why, and its next suggestion says what changed.
  • It sold out during a holdDrop Watch says so straight away and keeps watching inside the same rules. It never swaps in something “similar” without asking.
  • It misread the request“Comfy” came back as gym wear. The “Understood as” chips show its reading, so Maya removes one chip instead of typing it all again.
See the whole system: one buddy, small agents, and clear limits on what each can touch

Style Buddy is the only agent shoppers talk to. It hands each job to a smaller agent, and every agent can read only what its job needs.

Style BuddyTalks to you. Hands each job to the right agent.
  • Search
    Reads
    The catalogue
    May
    Show what matches
  • See yourself
    Reads
    Your photo, on this phone only
    May
    Show you in each colour
  • Fit Twin
    Reads
    What you kept and sent back; each garment’s measurements
    May
    Suggest a size, with a reason
  • Combinator
    Reads
    Your orders, new arrivals, the forecast
    May
    Put outfits together
  • Drop Watch
    Reads
    Stock in your size
    May
    Hold an item for 20 minutes
  • Agents you make
    Reads
    Only what you point it at
    May
    One job, like “Jackets for Taylor”

No agent canpay, buy, share your photo, or post for you.

How the agents work together: Style Buddy at the centre hands jobs to six smaller agents, each listed with what it can read and what it may do. No agent can pay, buy, share your photo, or post for you.

06 · Outcome

The retailer became a Deloitte client.

The research, three decisions and working prototypes came together in a 2-week pitch, and the retailer signed.

What I took awayThis project wasn’t born for the client. It started as a design question, became a blue-sky concept, and won because we’d done the thinking before the brief arrived. That’s curiosity compounding.

Deloitte Outstanding Performance Award, Certificate of Appreciation for Riddhima Sharma. Project name blurred.Open certificate ↗

Recognition · 1 of 2

Outstanding Performance Award

From the product manager I started this thought experiment with

For building version after version of the concept, then taking it back to the drawing board and designing it from scratch, while mentoring a new designer.

“A creative sounding board and an inspiring designer.”

Deloitte U.S. Firms · project name blurred for confidentiality

Applause award certificate for Riddhima Sharma, for collaborating for measurable impact. Client name blurred.Open certificate ↗

Recognition · 2 of 2

Applause award: Collaborate for measurable impact

From the pitch team

For leading the design effort and presenting the reimagined experience that helped the team win the client’s business.

“Your vision, creativity, and design leadership helped bring a compelling experience to life.”

Deloitte USI Applause · August 2026 · client name blurred for confidentiality

Next case studyAgentic Onboarding

The problem

84%of Gen Z expect a store made for them. Most stores are billboards.

A heritage fashion retailer was losing its Gen Z shoppers.

Sam and Rae, two of the group chat, taking a selfie in the back of a car
Tidewell (client renamed)
1 / 5
Plumbline • B2B fintech

Agentic Onboarding

About 80% less effort to set up a retirement plan, and people still sign off on every answer.

PLUMBLINEPlan onboarding

01 · The problem

A year of forms, for answers they already have.

A company and its advisor fill in 26 forms and about 275 fields by hand, across 6 stages. Most of those answers are already sitting in the plan document, the investment line-up and the payroll files.

Drafting the forms was the easy part. The hard part was taking the typing away without taking the judgment away, because one signature here covers thousands of people’s savings.

“If you’ve ever read a plan document, it’s definitely a snooze fest.”

Retirement plan advisor, in research
  • $400Ma year on plan onboarding
  • 14 millionhours a year on onboarding and service
  • 12 monthsto onboard one plan

Industry estimates from the project’s market research.

01 · The people

They’re fine with agents. They just want to see the working.

These people have set up plans by hand for years. They’d happily hand off the typing, but their signature carries legal weight, so they need to see where every answer came from.

  • A professional woman in her late forties, in glasses, in her office
    Plan sponsor
    The employer

    Sets up the plan for their staff. Holds the documents and fills in the forms with the advisor.

    This is going to help all of us and make plan sponsors really happy.Retirement plan advisor, on sponsors
  • A man in his fifties with a grey beard and glasses, reading a document
    Plan advisor
    The advisor

    Guides the sponsor through every stage and signs off. Explains plan provisions on long calls.

    I just got off a 90-minute call for this.Retirement plan advisor
  • A woman in her forties reading a thick binder with a pen
    Recordkeeper
    The onboarding team

    Works at the recordkeeper, the firm that runs the plan. Onboards many plans at once, in teams of 25 to 200 people.

    If we can push a button to make the plan live, I would give this a 20/10.Recordkeeper, in research

Quotes from the team’s 15 hour-long interviews with advisors and recordkeepers. Photos are illustrative.

02 · How it works

You upload once. Agents draft, people check.

One agent reads the documents and fills in the forms. Another checks the drafts against the rules. Before it fills anything, it shows which files it picked and asks the advisor to confirm.

Look for
  1. Files the agent picked, for the advisor to verify
  2. Three mapping methods, in plain words

02 · Decision 1 of 3

Agent or person? You can tell at a glance.

Every field has a coloured line showing its status, and filters take you straight to what needs a person. Advisors used to track all this in their heads.

Who filled what

Look for
  1. A status line on every field
  2. Filters for what needs review
  3. A clear action on the conflict

02 · Decision 2 of 3

Sources stay hidden until you want them.

Flip one toggle and the exact line from the document appears under each answer. Click a source, and if two documents disagree, you see both side by side.

Proof when you ask

Look for
  1. The “Show source” toggle
  2. The file and the exact line each answer came from
  3. Both documents, side by side
  4. The agent’s recommendation, and why
  5. Nothing changes until the advisor applies it

02 · Decision 3 of 3

When documents disagree, it tells you.

The agent explains the conflict in plain language and suggests a fix. The advisor decides, and nothing changes until they apply it.

Admits when unsure

Look for
  1. An honest confidence score
  2. The conflict, explained in plain language
  3. Insights the advisor can take or leave

03 · How it evolved

It took a few rounds to get this right.

Hover to peek. Click to look closer.Tap a version to look closer. Earlier rounds are redrawn as rough sketches.

04 · How it’s built

One rule underneath: make signing off easy.

Show everything and advisors drown. Hide the reasoning and it’s a black box. So each approval shows three things, and the full trail is there if someone wants it.

At every approval point, show three things

  1. What the agent recommends
  2. Where that came from
  3. One clear action

05 · Outcome

It won the challenge, and now it’s being built.

What took 12 months by hand becomes a guided review. Advisors hand off the typing and keep control of the approval. Engineers build straight from the live design system, through Figma’s MCP server.

  • In buildwith one of the largest US retirement providers, a top-tier recordkeeper
  • ~80%less effort to onboard a plan (estimated)
  • 8‑figureARR opportunity; won an internal innovation challenge
Applause award certificate for Riddhima Sharma, for collaborating for measurable impact. Project name blurred.Open certificate ↗

Recognition

Applause award: Collaborate for measurable impact

Awarded for leading design on the product: building the whole 0-to-1 experience from scratch, and owning UX, UI and prototyping.

“You think like a product owner, not just a designer.”

Deloitte USI Applause · June 2026 · project name blurred for confidentiality

Next case studyRFP Agent

The problem

12 monthsto set up one retirement plan, with 26 forms filled in by hand.

They’re copying in answers that are already in their own documents.

1 / 5
Lantern • Enterprise AI

RFP Agent

An RFP is a request for proposal: the document a company sends out when it wants firms to bid for its work.

Lantern gets a proposal lead to the point of one in under a minute, not ten, using the firm’s own past bids.

In a dark hall of dim screens, one paper lantern is lit above one screen, and a woman walks towards it
Lantern, lighting the line that matters
Role
UX and interaction design
Timeline
4 weeks

01 · The problem

Give both the same RFP, and her afternoon goes very differently.

We gave a general AI assistant and Lantern the same RFP and asked the same thing. Here’s what Nadia would get back.

Blanks, not guesses

Nadia, a proposal lead, in an oxblood suit, holding a tablet
NadiaShe’s a proposal lead with a few bids on the go, so she wants the point first and the proof close by.
Compared onA general AI assistantLantern
What it works from✕The RFP and the open web✓The RFP, our past bids and the client record
Where a claim comes from✕A web link, at best✓A firm source on every claim, one click away
Time to the point✕About 10 minutes of reading✓Under a minute: three lines up top
Team, fees, architecture✕Filled in, without checking with her✓Left blank and flagged for her
Before it goes out✕Nothing✓Six reviewers try to poke holes in it

02 · Decision 1 of 5

It gets going the moment the RFP lands.

Our first version only found sources for a deck someone had already written. Now she doesn’t need a deck to start.

02 · Decision 2 of 5

You can check any claim it makes.

Click a source and the exact line opens right beside the work.

02 · Decision 3 of 5

It shows its thinking before it recommends.

She sees how Lantern got there first. Then she picks an approach, or builds her own.

The why comes first

02 · Decision 4 of 5

It runs a pressure test before the client sees it.

Six reviewer agents read every draft the way the client, a competitor, compliance and the delivery lead would. Nadia picks which fixes to take.

Picked apart first

02 · Decision 5 of 5

One chat, as broad or as narrow as she needs.

Instead of pasting copy, or explaining what it’s about, she selects a section and talks through anything in it with Lantern. That saves her the typing and the explaining every time she asks.

Context in one tick

03 · How it evolved

We went through four versions to get here.

Hover to peek. Click to look closer.Tap a version to look closer. Earlier versions are redrawn as wireframes.

04 · How it’s built

One rule underneath: show where it came from.

Every answer Lantern gives is tied to one of five kinds of source. If it can’t point to one, it calls the claim an estimate.

RFP · Evaluation
Client document

Anything the client sent: the RFP, their templates, their answers.

Opens the document at that section, with the line highlighted.

Regional CIB win, 2024
Firm asset

Our past proposals, wins, accelerators, prototypes, and the bids we lost.

Shows a preview, why Lantern used it, and where it lives.

Harborline press release
Market and public data

Press releases, filings, news.

Opens the original page.

Client records
Internal record

Our own systems: client history, people, pipeline.

Opens the record where it lives.

Phase lengths
Lantern’s estimate

Anything it worked out for itself.

Shows what the estimate is based on.

  • Name the thing, not the type. The icon carries the type, so the label can be specific.
  • Always visible. Sources sit next to the claim, never behind a hover.
  • No source, no chip. Anything without an origin is marked as an estimate.
  • Sources, replies and Ask Lantern stay apart. Opening one closes the others.

05 · Outcome

It tested well, and now it’s being built.

Tested with the people who’ll use it

We put v4 in front of subject-matter experts and future users, with realistic RFPs.

What they liked

Checking any claim in one click, and seeing the blanks instead of made-up answers.

Now in build

It’s going into the firm’s workspace.

Next case studyPersonalized Retail

The problem

10 minof reading to find the point in a chatbot’s answer to one RFP.

You get pages of text, nothing to check it against, and no idea what the firm has done before.

An RFP is a request for proposal: the document a company sends out when it wants firms to bid for its work.

Nadia, a proposal lead, in an oxblood suit, holding a tablet
Nadia · proposal lead, one week to the deadline
1 / 5