Product & Design Leader · AI & Data-Intensive Products
Your model is right. Your users still won't bet on it.
The half-second of doubt is the only thing I design. Fifteen years, five industries, one problem.
Shipped for Telefónica O2 — 4M+ users · PTC — NASA, Boeing, Toyota & Airbus among clientele · AI products since 2019
Where does your AI product lose people?
You said
See how I fixed itThe receipts — four industries
What the design was worth.
A. Maheshwari
product & design leader · est. 2010
Receipt — campaign planning, 2 wks → 3 hrs
Receipt — deal screening, 60% faster
Receipt — $1M / yr, and a business model shifted
Receipt — 4M+ users, drawn then coded
Who you'd be hiring
Arpit Maheshwari
By Friday of week one I've read your evals and sat in on your customer calls; by launch, the interface I drew is the code I shipped.
I read the model at the eval layer. I shape what gets measured. I ship the front-end.
On my first accessibility project I spent a week with my monitor switched off, navigating by screen-reader — designing for someone who isn't you starts by becoming them. That project started as my documented miss →
Fully remote · GMT+5:30 · US-East overlap
Candidate Facts
Allergen notice: contains one documented failure — disclosed in full in the PTC case. Contains no: logo walls, invented baselines, confident guesses.
Selected Work
The AI work is the headline. The rest is the range under it.
Six products, one job running through all of them: get a professional to trust a system enough to act on it. The first three are AI — two of them LLMs reading unstructured documents. Two of the rest are public — PTC and O2 — which means you can check them yourself; OrgOS is under NDA. Where a client is under NDA I walk the real artifacts and numbers on a call.
How to read the six cases below
Every one of them runs the same four beats.
A model produces a score. A person doubts it. The design turns that doubt into an action they’re willing to take — and the action shows up in the business. The cases differ in industry and stakes; the shape never changes.
Beats: doubt, action. The action beat is always mine.
Programmatic Advertising Platform
Traders watched an algorithm beat them on the scoreboard and still played their own hunches. The fix wasn't a better model — it was a score tied to one action, reasons named out loud, and an override that taught next week's predictions.
Walk-through →Beats: score, action. The action beat is always mine.
AI-Assisted Private Equity Investing
Analysts get paid to doubt confident numbers, so I held the launch until the score could argue its own case. Once it could, they stopped auditing it and started leaning on it.
Walk-through →Beats: score, action. The action beat is always mine.
Technical Due Diligence Platform
Partners stake millions on technical claims they'll never personally verify. Extracting the signals was the model's job; getting partners to lean their reputation on the extraction was mine.
Walk-through →Beats: action, impact. The action beat is always mine.
PTC University — Learning Connector
The brief said redesign the UX. I argued the contract was the broken interface, and asked PTC to kill four of its five products. Public numbers, full case — including the accessibility miss I got wrong first.
Walk-through →Beats: action. The action beat is always mine.
Telefónica MyO2 & Priority Moments
Every screen drawn by me, then coded by me — two O2 UK products on mobile web, at a scale where rounding errors have populations.
Walk-through →Beats: doubt, action. The action beat is always mine.
OrgOS · Transparent Org Tooling
Two hundred people, no managers. Eight modules doing an org chart's job — coordination that doesn't smuggle a boss back in through the side door.
Walk-through →The contract behind every screen I ship
A language model has no edges. So I draw them.
Ask one anything and it answers — fluently, in or out of its depth. That is the whole danger: a user’s trust does not erode slowly, it dies in a single confident wrong answer. And most products bury their scope in a terms-of-service page and hope nobody tests it.
So before launch I write a capability contract — a one-pager the whole team signs. What the system is for, stated narrowly. Where it taps out, named specifically enough that a person can plan around it. And what it hands back to a human when it does. Then it goes in the interface, at the point of use, where the work actually happens.
An honest “I don’t handle that” is not a weakness. It is the thing that makes “I do handle this” believable. A model that knows its own edges reads as a colleague; one that answers everything reads as a slot machine. Two rules I hold to: state the boundary once, where the user first meets the capability — a disclaimer on every screen reads as a product unsure of itself. And never write a contract you don’t enforce, because a stated limit the system quietly exceeds burns more trust than saying nothing at all.
live — the contract, workingsigned
It can’t: price illiquid assets.
Hands back: anything else → your analyst.
answers openly canned — the contract is the demo, not a model
How I Lead
Hire me and week one looks like this: I'm reading eval results before opening a design file, sitting silent on customer calls, and writing the diagnosis nobody assigned. By week two we're arguing productively.
The errata — a time I was wrong
My first accessibility pass was textbook-diligent and wrong. I loaded the interface with long, descriptive ARIA labels — the text a screen reader speaks aloud — convinced that more description meant more help.
Then I ran a usability study with about ten blind users and watched my diligence fail. They don’t listen through sentences. They skim — jumping by headings and landmarks at speed, the way you scan a page with your eyes. My careful labels were slowing down the exact people they were built to serve.
So I switched my monitor off for a week and navigated by screen reader alone, then re-wrote the front-end with what that week taught me: terse labels, honest landmarks, headings that carry the structure. The full account, in the PTC case →
That’s the whole job — the machine checked, a human took the responsibility. No takebacks.
Quality Control
№ 15
checked by machine · awaiting human
years of service: fifteen
- Human-in-the-loop design
- The exact pixels where a person decides the model deserves their click, or doesn't.
- The design language
- A component system the next designer can run without me in the room, because it's documented, not memorized.
- The ML/UX contract
- A written promise between the model team and the user: here's what this system can do, here's where it taps out.
- With engineering
- Eval design before interface design, always. The front-end I own ships in the PR — commented and ready for review. When we disagree on feasibility, the cheapest experiment goes first and settles it.
- With product / founder
- I'll contest the roadmap when the numbers contradict it, and I sign up for outcomes rather than deliverables. The design doc is mine to write; the spec is yours.
- With customers
- Five calls in my first week, one a week forever after, and I read the raw support tickets myself. No AI feature ships until I've personally watched someone fail to use it.
- No score without a verb and its reasons. I held the PE release until the model could say “I’m not sure” out loud — weeks late, on purpose.
- No AI feature without a designed failure state. Usually one honest sentence in the interface — an hour of design, not a sprint.
- No doing two jobs as neither. At founding stage I do all of it, happily; once a team exists, its ops and weekly shipping split — or both rot.
Over the past four years at Talon, Arpit has been instrumental in shaping four distinct products from the ground up. His user-focused designs are remarkably intuitive yet adept at handling complex workflows… If you need a designer who excels at combining strategic vision with practical execution, Arpit is the person to call.
Arpit has worked with me for years and I value his honesty and hard work. He's been an integral part of my staff… involved in all facets of the team, from design to development to hiring and onboarding of new members.
Arpit teams up with designers very well, not only does he flawlessly execute the UI implementations but he pushes back on design decisions using his UX expertise… I'd recommend Arpit to any team looking to improve their final product.
Arpit consistently demonstrated exceptional speed, creativity, and attention to detail… What stood out most was his ability to present multiple design options along with clear pros and cons, which made it much easier for different stakeholders to make informed decisions and align quickly.
Process
Every product is a series of bets someone else has to accept. The method exists to make each bet smaller, better-evidenced, and easier to say yes to.
Desirable · feasible · viable — the overlap is the bet worth making. At PTC: four of five products killed to fund the one that worked.
Listen → Structure → Prove → Land, in loops. Act / Review / Ignore was born in a Listen loop — watching traders override a model that was beating them.
Shipping is the first honest data. I argued for a three-option card; the A/B made the one-option version permanent — the ship taught me what the mock couldn't.
Pre-ship checklist
run before every release · no exceptions
a release that fails one item waits · weeks if it must
Writing
Three recent pieces from Human in the Loop, my newsletter on making AI products people actually act on.
More on Human in the Loop →The Agentic MVP: Why Your Next Launch Will Be Lovable, Not Just Viable
Every founder knows the pit in their stomach on Launch Day. How the rise of agentic systems is rewriting what "minimum viable" means — and why lovability is now the bar.
The AI Fight Club: Weaponizing Claude and Gemini for Bulletproof Products
Pitting AI systems against each other to strengthen product robustness. A practical method for stress-testing your AI features before users do it for you.
The New Renaissance: How AI is Transforming Us from Software Operators to Digital Artisans
Ushering in a new era of digital entrepreneurship. How AI is changing what it means to build, and what designers must understand about the tools reshaping the industry.
Contact
One seat. Full-time. Yours to offer.
Founding product & design lead at an AI product company of 5–40 people — or a staff / director role where human-in-the-loop design is the actual job description. Available — 4 weeks' notice.
A real conversation, no pitch deck. If you would rather read first: the one-page hiring brief, the pattern library, or my technical screen, already answered.
Prefer email? use the form or the call link
No portfolio survives the first real week on the job. This one is just here to earn it.— Arpit
I reply within ~48 hours.
Not hiring but building something in AI? The patterns library and the founder checklist are free — take them.