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19 years in UX.The last 2 rebuilding it with AI.

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About Surinder
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Pricing

Book a scope call

About

19 years in UX.The last 2 rebuilding it with AI.

Pages

About Surinder
Process
Experience
Certifications

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Work
Pricing

19 years in UX.The last 2 rebuilding it with AI.

About Surinder
Process
Experience
Certifications

WorkRestaurants list for less. Diners order nearby.

Email me WhatsApp me

Comiida · restaurant listing app for Colombia, client work

Restaurants list for less. Diners order nearby.

48 screens and a linked prototype, in 8 weeks

Nearby first, on equal cards.

Relevance: time and distance on every card, for diners nearby.
Comparison: the same facts for all, so small places can compete.
Effort: Sort and cuisine chips sit above the list, 1 tap each.

123

A whole shop on 1 page.

Certainty: status, time and rating sit on top, before any scrolling.
Reach: Reserve Table is a tab like Order, so seats sell too.
Effort: Veg only cuts a long menu in 1 tap, with search beside it.

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Every extra has a price.

Clarity: each dish shows its price, then its add-ons, before any total.
Reward: a coupon field sits above payment, with offers beside it.
Choice: cards, net banking or cash on delivery, so payment never stalls.

123

The wait becomes a number.

Trust: each restaurant sends its own driver, so the map shows a face.
Progress: Arriving in 10 Minutes and a bar, so nobody has to ask.
Honesty: a tag says No Live Tracking, before you order.

12

3

Reviews an owner can act on.

Effort: stars, then choices to tap, so a rating needs no typing.
Specificity: from cleanliness to value, an owner sees what to fix.
Return: Reorder sits beside Rate Food, so 1 meal leads to the next.

12

3

No screen ends in a dead end.

Recovery: No Results and an empty cart end in Browse Restaurants.
Clarity: 1 line says why the screen is empty, then 1 button.
Consistency: 1 drawing style across states, a red line on rings.

3

123

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A file built for developers.

Findability: screens numbered and named in flow order, so any one is found fast.
Specs: colour ramps with hex codes and 10 named text styles, no guessing.
Reuse: 16 core screens use 38 components, so 1 fix reaches all.

Colours, by job

Primary · main buttons#ED1D1D
Secondary · second buttons#535B72
Title/Highlight Text#222222
Body Text#424242
Sub Text#757575
Inactive#9E9E9E
Border#E0E0E0

2 ramps, 10 steps each

Type, by job

Poppins across the 16 core screens, with 321 of 392 text layers on a named style.

Restaurant name · 20pt SemiBold
Section title · 18pt Medium
Group title · 16pt Medium
Dish name · 14pt Regular
Detail line · 12pt Regular

“He consistently delivers on time often ahead of schedule and his professionalism is unmatched.”

Carlos AriasFounder, Medellin.co

In short

Comiida was designed to give restaurants in Colombia their own way to sell. A diner finds a place close by, then orders or books a table. They pay, follow the delivery and rate the meal. A web portal and social designs came with the same project.

Handed to developers for productionNot a measured result: The case stops at handover

The problem: 1 delivery app held the market, with high fees to list and to deliver.
What changed: Restaurants pay little to list and deliver for themselves
2 journeys designed end to end, the owner's and the diner's
Real owners tried it, and actions, wording and flows changed
The result: Finished on a tight deadline, with marketing under way during the build · Digital product designer: research, UX and UI

Surinder ThakurAI Product Designer and Builder between Dubai and India, for UAE, US and EU teams

Start with 30 minutes.

You talk to me, the one who leads your build. Tell me the tool, the users and what it should move.

Book a scope call1 slot open for Q4 2026

Or write to hey@surinder.design

years in UX and product design: 19
YoY revenue at Puffy: +67%

Design manager for 2 years at Puffy, a $1B brand.

What happens after you book

You fill a short intake formSo the call starts with your problem, not with questions.
We talk for 30 minutesYour tool, your users and what it should move.
You get scope and priceIn writing, within 24 hours. No pitch.

On time every time

“If he says he will do something, there's no question that it will get done.”

Avin KlineCEO, Lucyd

Work and insights

10 projects and 16 insights, on one board. Open any card to see it in full.

The board

Dutch B2B commerce · Blueprint prototype, 2 weeks
Agents take the order. People keep the decisions.
An urgent order in minutes, not an hour
Buy SaaS when
The job is common, like payroll or email
Your process can bend to the tool
A monthly fee beats paying for a build
Build when
The work is how you are different
People bridge gaps by hand every week
Approvals and exceptions are yours alone
2 questions decide it: is the job common, and who fills the gaps today.
Building an AI toolShould we build a custom AI tool or buy SaaS?Buy SaaS when your process matches how the tool works. Build when the work is yours alone: your approvals, your exceptions, your mix of systems. Then watch the gaps. If people copy data between tools every week, those hours are the budget. A custom tool earns its cost when it removes them.
Scope call
Free, 30 minutes
Blueprint
$3,500, 2 weeks, credited
Build
From $20,000, most $30,000 to $60,000
Retainer
$8,000 a month, month to month
Every price, in order. Each one is fixed in writing before the work starts.USD, founding rates for the first 5 clients, as on the homepage price ladder.
Building an AI toolWhat does a custom AI tool cost?A custom AI tool or agent costs from $20,000 to build here. Most builds land between $30,000 and $60,000. It starts with a 2 week Blueprint, $3,500 at the founding rate. Its fee is credited to the build. The Blueprint ends with a fixed price in writing. You know the full cost before you commit.
AI SaaS · Built solo, live at proux.design
Agents do the research. Every answer cites its source.
Every answer cites its source, across 350+ UX principles
Week 1
How the work runs today, and what it costs
Demo meeting
What the agent does, its rules, where a person decides
Week 2
A prototype your team clicks
Proposal
A fixed price and timeline
2 weeks, 2 meetings, and 3 things you keep.
Building an AI toolWhat do you get from a 2 week Blueprint?In 2 weeks you get a business case, a working prototype and a fixed build price. The case shows what the tool saves and what it costs. Your team clicks through the prototype. The proposal fixes the price and timeline. It costs $3,500 at the founding rate, credited to the build. You keep the case and the prototype.
- Finding out in the Blueprint$3,500
- Finding out after a build$30,000
What it costs to learn the answer, before the build and after it.The founding Blueprint price, and the low end of a typical build, from the homepage price ladder.
Building an AI toolWhat if the build does not pay off?Then you find out in the Blueprint, before the build spend. It takes 2 weeks and costs $3,500. You get a business case in numbers and a prototype you can click. If the numbers say not to build, you stop there. You keep both. Learning it cost $3,500. A build would have cost $30,000.
Puffy · design manager, 2 years
Repositioned as attainable luxury.
+67% YoY revenue
An agency
An account manager takes the brief
A project manager runs the plan
A designer hands off to developers
1 senior
Designs and builds it
Shows you a live demo every week
Fixes the price before the build
The layers between you and the person building.
Building an AI toolWhy hire 1 senior instead of an agency?Count the layers between you and the person building. An agency quote pays for account managers, project managers and handoffs before anything works. 1 senior designs and builds it, so every hour goes into the tool. I hired both as a design manager at a $1B brand. The layers made the difference.
Research
AI reads the tickets and docs
Rank
I pick what costs money
Design
20 directions drafted
Choose
1 survives comparison
Build and ship
A live demo every week
AI widens at every step. A person narrows and decides.
Building an AI toolHow do you use AI to design and build a tool?AI runs inside every step, from research to shipped code. Claude for synthesis and reasoning, Claude Code and Cursor for the build, Figma for design. The rule never changes. AI widens the options and speeds the work. I make the decisions, because judgment is the part that does not delegate.
Mobile appLivOn4everOpens with a password
Built
Stalls when it fits no real job
Used every week
Stalls when it lives in its own tab
Trusted to act
Stalls when nobody sees what it did
Trusted with money
Stalls on the first wrong number
In real use
Each stall designed out
The 4 places an AI pilot stalls, in the order a tool meets them.
Getting your team to use itWhy do AI pilots stall before real use?AI pilots stall because a demo only proves the tool can work. Real use needs a team to choose it every week. Pilots stall in 4 places. The build never fit a real job. The team keeps the old spreadsheet. Nobody trusts the agent to act. 1 wrong number in a money flow ends it.
What AI ships fast
The screens exist
The flow runs
The agent calls the API
What gets it used
The team trusts it with real work
It fits the week they already have
A person decides where it matters
AI ships the tool fast. The decisions around it get it used.
Getting your team to use itWhy hire a senior when AI can build the tool?Because building was never the hard part. AI can write the code and draw the screens in hours. I use it every day. What it cannot do is make people use the result. Will the team trust it with real work? Will it fit their week? You hire a senior for those decisions.
Purpose · learning app concept, 30+ screens
Built for the phone. Nobody learns alone.
From first run to live class, in light and dark
Weekly use
Week 1Week 8
In its own tab
Inside the daily task
Novelty fades after week 1. A feature inside the work keeps its users.An illustration of the shape, not measured data.
Getting your team to use itWhy do users stop using AI features after the first week?Because the first week runs on novelty, and novelty is not a job. The demo moment amazes, then the blank prompt box starts asking the user to work. A feature survives when it attaches to something the user already does every week. Most AI features live in their own tab, waiting. Habits do not form in tabs nobody opens.
In the path
Starts where the task starts
First step filled
The agent drafts, a person checks
Work shown
Every step and its rule
Easy to correct
1 click, and it remembers
Weekly use
Tracked after launch
5 design moves that turn a launch into weekly use.
Getting your team to use itHow do you get a team to actually use a new AI tool?Design the first week of use as carefully as the tool. Put it where the task already starts, so nobody has to remember it. Fill the first step in for them. Show what the agent did and let people correct it. Then track weekly use after launch, and fix what people skip.
MyHouz · design manager at RGG, US real estate
Map-first search. 1% back on every home.
MVP live in 90 days, 150 buyer inquiries in month 1
- AI apps, annual plans21.1%
- Other apps, annual plans30.7%
- AI apps, monthly plans6.1%
- Other apps, monthly plans9.5%
Subscribers still paying after 12 months, AI apps against the rest.Source: RevenueCat, State of Subscription Apps 2026.
Getting your team to use itWhy do AI apps lose subscribers faster than normal apps?Because the wow moment is not a habit. RevenueCat's 2026 data puts AI app annual retention at 21.1%, against 30.7% for everything else. The demo converts, then daily life asks a harder question: is this still worth opening. Most AI apps never design for that question. They monetize the novelty and lose the month 2 user.
Invoice arrives
The agent reads it
Rules check
Payee, limit, budget
Outside the rules
A person approves
Sees what, why, and the rule that stopped it
10 minute wait
Anyone can undo it
Paid
Logged with its rule
The money rule. Inside your rules it pays, with a 10 minute undo. Outside them, a person decides.
Trusting AI with moneyHow do you stop an AI agent making a mistake with money?You limit what it can do alone. The agent pays only inside the rules you set: approved payees, a limit, a monthly budget. Each payment waits 10 minutes before it goes out, so you can undo it. Anything outside the rules waits for a person. Every step is logged, with the rule it followed.
Pursely · money management app, design concept
Spend, budget and invest. All in 1 app.
40+ screens, light and dark, 1 design system
- How much a gain weighs1x
- How much an equal loss weighsAbout 2x
People weigh a loss about twice as heavily as an equal gain.Source: Tversky and Kahneman, 1992. Their estimate is 2.25.
Trusting AI with moneyWhy does 1 wrong AI answer break trust in my product?Because trust in software is asymmetric. 10 right answers build it slowly. 1 confident wrong answer spends it all at once. Loss aversion runs the math. A wrong answer is a loss, and losses weigh about twice an equal gain. The fix is not a better model. It is designing the recovery, the moment after the mistake.
ID uploaded
Checked for glare and blur
Agent reads it
Name, number, expiry filled
A mismatch or an expired ID
Your team reviews
With what the agent found, and why
Customer confirms
No typing
First transaction
Where onboarding ends
The agent does the typing. People only review the flags.
Trusting AI with moneyHow do you fix drop-off in fintech onboarding and KYC?People quit KYC when a step asks for trust it has not earned. Compliance sets the rules, so the fix is the order, the reasons and the effort. Say why before you ask. Let an agent read each document and fill the form. The customer only checks what it flags. Let people stop and resume.
Findout · travel listing app, design concept
Find a place. Know it before you go.
32 screens for hotels, restaurants, events and places
Google reads
The rendered page
Structured data, for rich results
Headings, links and speed
AI assistants read
The visible text, quoted as written
The first 2 or 3 sentences under a question
llms.txt, the plain summary
2 kinds of reader. Both reward a clear answer in plain words.
Being found by AIHow do ChatGPT, Claude, Perplexity and Google read a product site?They read the words people can see. Google crawls, renders and ranks, and uses structured data for rich results. AI assistants like ChatGPT, Claude and Perplexity fetch the page text and quote it. Most never see code hidden in scripts. So the answer has to live in the visible copy, worded the same on every page.
Agents need structure
Clean product data
Clear prices and stock
Policies a machine can read
People need trust
Proof near the price
A way back from a mistake
A checkout built for thumbs
2 funnels, 1 store. The same discipline runs both.
Being found by AIWill AI shopping agents kill my store's conversion?Not kill. Reroute. AI agents already comparison-shop, fill carts and complete checkouts. The buyers behind them still decide who earns the sale. 2 funnels now matter: people who need trust and agents who need structure. Stores designed for both convert both. Stores designed for neither lose quietly to whichever competitor answered the agent first.
Signal · news app concept, 30+ screens
The day at a glance. The full story in 1 tap.
30+ screens and 80+ components, day and night
Comiida · restaurant listing app for Colombia, client work
Restaurants list for less. Diners order nearby.
Handed to developers for production

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