Founding & Staff Designer · For Startups & Enterprises

15 years designing intelligent systems. I code. I ship to 1M+ users who trust it.

3× faster workflows · 60% faster screening · 40% fewer support tickets · 550k+ users impacted

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Arpit Maheshwari. I code, I read SQL, I ship production. Built data-intensive systems across AI, FinTech, AdTech, EdTech, and Telecom that impacted 1M+ users. Looking for founding or staff designer roles at startups and enterprises building intelligent products where you need designer #1 or #2 with deep technical fluency.

15
Years deep
8+
Industries
550k
Users impacted

Now · Currently Shipping

What I'm shipping right now.

Updates monthly

Looking for: Founding & staff designer roles
Startups and enterprises building intelligent, data-intensive products. First or second designer. I code production UI alongside Figma. Talk to me →
Building: AI Design Patterns library
A growing catalog of patterns from 15 years of ML/AI work — confidence scores, failure states, explainability surfaces. Coming to this site soon.
Writing: Creative Clarity
Monthly notes on designing intelligent systems — patterns, methodology, and lessons from data-intensive products across AI, FinTech, AdTech, and beyond. Subscribe →
Recent: How I forbid AI from hallucinating
Designing safeguards into AI interfaces to prevent hallucination and maintain user trust. Read on Substack →

About

Arpit Maheshwari — Product designer for AI and data-intensive products, based in Indore, India

Founding designer for
intelligent systems.

Most designers treat intelligent outputs as isolated UI problems. I think in systems — understanding how products evolve from standardized processes → sensing/monitoring → intelligent decision-making — then design each layer accordingly.

I code in HTML/CSS/JS. I read SQL. I sit in model reviews and translate metrics into product decisions. I've shipped data-intensive systems from raw complexity to 1M+ users. That hybrid — designer who ships production code and reads data architectures — is exactly what 0-to-1 founders need on day one. Based in Indore, India, fully remote across US, UK, and EU time zones.

Intelligent-Systems Thinking
Designing for machine outputs, algorithmic trust, and data-driven interfaces
Data-Fluent
Turning complex data and algorithmic signals into clear, actionable UX
Accessibility-First
Universal design — including intelligent systems — for all users
Systems Thinker
Products that scale with both users and system intelligence

Past Work · Credibility List

15 years of shipped intelligent systems.

All projects below are under NDA — I cannot show pixels. What I can show: the design challenge, my specific role, and measurable outcomes. For visible work, see the Now section above.↗ Connect on LinkedIn for detailed case walkthroughs
NDA Protected
AdTech · Programmatic Platform

Why media buyers stopped trusting their gut — and started trusting an algorithm

I designed an AI-driven programmatic platform that uses machine learning to aggregate inventory, automate campaign planning, and optimize media buying across the US and UK outdoor advertising market. I built it for non-technical media buyers — making AI-powered decisions feel intuitive.

Design Challenge Solved:

Made ML recommendations transparent for non-technical buyers • Designed confidence-score UI patterns showing *why* the algorithm ranked campaigns high

Outcome: 3x faster campaign workflows across US + UK markets

US+UK
Markets
3x
Faster workflows
My role: Sole product designer. Owned information architecture, interaction design for ML-powered recommendations, confidence-score UI patterns, and campaign builder flows. Collaborated directly with ML engineers on model output formatting and edge-case handling.
NDA Protected
FinTech · AI-Powered Private Equity

How I got PE analysts to defer to an AI their boss didn't trust

I designed an intelligent screening platform that uses AI to evaluate and rank potential investment targets for private equity firms. I simplified complex financial data, risk signals, and market indicators into actionable dashboards for deal teams.

Design Challenge Solved:

Built ML explainability into investment decisions • Designed risk-signal visualizations and AI reasoning layers showing deal teams *why* a company scored high

Outcome: 60% faster deal screening with transparent AI reasoning

60%
Faster screening
AI
Explainability layer
My role: Sole product designer. Designed the screening dashboard, risk-signal visualisation, and AI explainability components — showing users why the model ranked a company high or low.
NDA Protected
Product Strategy · Enterprise SaaS · Organizational Design

Designing the operating system for a company with no managers

I designed a unified platform for managing operations across sales, finance, people, and projects — for an organization with zero hierarchy, transparent salaries, and a remote-friendly model.

Design Challenge Solved:

Designed for transparency without hierarchy • Built workflows for open salary visibility and peer-driven accountability • Created multi-module information architecture enabling autonomy at scale

Outcome: 8 interconnected modules enabling decentralized decision-making

8 modules
Sales, Finance, People, Projects
My role: Sole product designer. Built information architecture across 8 interconnected modules, designed workflows for salary transparency, project allocation, and equity management. Key challenge: Creating interfaces that empower individual autonomy while maintaining organizational visibility — no managers, only clear data and peer accountability.
NDA Protected
EdTech · PTC University

Designing for screen readers in 11 languages — when nobody on the team spoke any of them

I led UX strategy for a multi-locale education platform across 11 geographies. I integrated accessibility standards and consolidated scattered content types into a cohesive learning experience.

Design Challenge Solved:

Managed complexity at scale across 11 locales • Consolidated scattered content types into unified navigation • Built WCAG AA compliant screen-reader patterns

Outcome: 550k+ global users with accessible, cohesive experience

550k+
Global users
WCAG AA
Compliant
My role: Design lead (team of 2 designers). Ran user research across 4 locales, redesigned navigation and content architecture, implemented screen-reader-compatible patterns, and authored the team's accessibility design guidelines.
NDA Protected
Telecom · O2 Priority & My O2

How redesigning one screen killed 40% of support tickets

I designed the reward and loyalty experience for O2 Priority, increasing daily engagement for 1M+ subscribers. Separately, I rearchitected the My O2 self-service app — streamlining billing, plan management, and support flows.

Design Challenge Solved:

Designed engagement patterns from loyalty data • Redesigned self-service flows to reduce support friction • Built for 1M+ concurrent subscribers

Outcome: 40% fewer support tickets, 1M+ active subscribers

40%
Fewer support tickets
1M+
Subscribers
My role: Product designer within a 4-person team. Owned the rewards feed, offer detail screens, and billing section redesign.

Want to discuss work like this in detail?

Book a 30-min walkthrough
3×
Faster campaign workflows
40%
Fewer support tickets
60%
Faster deal screening
550k+
Users, WCAG AA compliant

People I've Worked With

What they say matters.

Full names and details available on LinkedIn.

"Arpit has a rare ability to take messy, complex ML outputs and turn them into interfaces that non-technical stakeholders can actually use. He thinks in systems, not screens."
Engineering Lead — Sahaj Software
"His accessibility work on our learning platform was exceptional — he didn't just check compliance boxes, he genuinely improved the experience for every user across all our locales."
Product Manager — PTC University
"One of the few designers I've worked with who can sit in a data review meeting, understand model performance metrics, and translate that directly into design decisions the same week."
Data Scientist — Sahaj Software
"Arpit brought clarity to a product that had been confusing users for years. The My O2 redesign significantly reduced our support burden and improved satisfaction scores."
Product Owner — Telefonica UK

Design
Philosophy

How I think about AI products

Design the Failure State First
AI is wrong 30–70% of the time in real usage. I design what happens when a model is uncertain, when data is missing, when confidence is low. The happy path demo is 30% of the work. The failure state is the other 70%. This gets designed *first*, not last.
Show the Why, Never Hide It
Users don't trust black boxes. When an AI recommends something, I design explainability into the interface — showing data provenance, confidence levels, what the model weighted, why it could be wrong. Transparency isn't a nice-to-have. It's the product.
Never Make Users Confirm What They Know
If an AI is right 99% of the time, making the user click "confirm" 99% of the time is broken design. I design systems that only ask for confirmation *when the AI is uncertain* — respecting user time and building actual trust through earned authority.
Accessible by Default
An AI dashboard that only works for sighted mouse users is a dashboard that fails half the audience. I build WCAG compliance into the first sprint, not the last — because retrofitting accessibility into data-dense interfaces is ten times harder than designing it in from day one.

My Approach to Data-Intensive Design

1
Make the Invisible Visible

AI models, data flows, and algorithms live in black boxes. Visualize them — showing users the data behind decisions, the logic in recommendations, the confidence in predictions.

2
Design for Non-Technical Stakeholders

Complex data shouldn't require a PhD. I design dashboards and tools for busy people making real decisions under time pressure — not for data scientists in labs.

3
Prioritize Clarity Over Cleverness

Beautiful data visualization is nice. Useful data visualization is essential. I optimize for understanding first, aesthetics second. A clear bar chart beats a confusing animation.

4
Test with Real Complexity

Figma mocks don't reveal what breaks under load. I prototype with real data, real edge cases, and real user flows — stress-testing before launch to thousands of users.

How I Work

Design for
intelligent products.

I work at the intersection of AI, data, and human experience — designing intelligent systems that are usable, trustworthy, and commercially viable.

Start a Project
AI Product Design
Designing interfaces for ML-powered products — from AI screening tools and recommendation engines to intelligent dashboards that keep humans in control.
UX for Intelligent Systems
Making AI outputs legible — confidence scores, risk signals, explainability layers, and graceful fallbacks when models are uncertain.
Data-Dense Interface Design
Complex dashboards, analytics platforms, and data-heavy enterprise tools — designed for clarity at scale.
Design Strategy
Finding the right problem before solving it. Aligning AI capabilities with real user needs and business outcomes.
Frontend Prototyping
HTML/CSS/JS prototypes that live in browsers — testing AI interactions with real data, not just Figma mocks.
Accessible AI
WCAG-compliant design for AI-powered products — ensuring intelligent interfaces work for screen readers, keyboard navigation, and all abilities.

Tools & technical skills.

Design
FigmaFigJamSketchAdobe XDPrincipleWhimsical
Prototyping & Code
HTML / CSS / JSReact (basic)StorybookCodePenGitHub
AI & Data
Jupyter NotebooksSQL (read)LookerMixpanelConfidence-score UX
Research & Testing
UserTestingMazeHotjarDovetailA/B Testing
Accessibility
axe DevToolsNVDAVoiceOverWCAG 2.1 AAColour Contrast
Collaboration
JiraConfluenceNotionMiroSlackLoom

Remote collaboration.

Based in Indore, India (GMT+5:30). 6+ years of distributed team experience across US, UK, and EU time zones.

Timezone Flexibility
4–5 hours daily overlap with US East Coast, full overlap with UK/EU. Async-first workflows with structured handoffs so nothing blocks.
Documentation-Driven
Detailed Figma annotations, Loom walkthroughs, and decision logs in Confluence/Notion. No context is lost between sessions.
Embedded in Engineering
Daily standups, sprint planning, PR reviews for UI. I work like a teammate, not a contractor handing off static mocks.

Process

First six weeks
working together.

No two engagements are identical. But this is the cadence I default to — listen first, prototype against real data, ship something narrow, then iterate against actual user behavior. Founders see momentum within 14 days.

01
Week 1: Listen + Map
I shadow your team, read your model docs, run my own SQL queries on your data, talk to 5+ users. Output: a written diagnosis of where AI UX is breaking.
02
Weeks 2–3: Prototype + Pressure-Test
I build clickable prototypes — HTML/CSS for production-bound, Figma for exploratory. Output: 2–3 testable directions with explicit failure-state designs.
03
Weeks 4–6: Ship First Cut
I work alongside engineering and write CSS/JS for components I designed. Output: production code for the first user-facing AI surface.
04
Ongoing: Iterate Against Reality
Weekly review of model performance + user behavior. Design changes follow data, not opinion. Output: monotonic improvement in trust + activation.

FAQ

Questions founders
always ask.

Save us both a 30-min intro call. If your question isn't here, it's the first thing we'll talk about.

Why is all your past work under NDA? +

Most of my work has been at agencies and consultancies serving regulated industries — finance, telecom, defense-adjacent enterprise. Standard NDAs cover everything I've shipped. The honest workaround: see what I'm shipping now in public, plus speculative redesigns coming soon.

Do you actually write production code, or just prototype? +

HTML/CSS/JS in production for components I design — buttons, forms, data displays, AI surfaces. React for prototypes and component scaffolds. I won't own backend or complex state architecture, but I ship the front-end of features I designed, in collaboration with senior engineers reviewing my PRs.

Are you really fully remote? Time zone reality check. +

India (GMT+5:30). 4–5 hour daily overlap with US East Coast (1–6pm ET your time = my evening). Full overlap with UK and EU. I've worked async-first for 6+ years. Standups, PR reviews, and design crits all happen in overlap; deep work happens in async.

What if I'm pre-Series A and can't pay senior salary? +

For founding-designer roles I'm equity-flexible — meaningful equity + market-rate cash, or below-market cash + larger equity. For 0-to-1 partnerships (3–6 months) I bill at standard senior contractor rates. Tell me your range; I'll tell you honestly if it works.

How fast can you start? +

2 weeks notice for full-time. 1 week for fractional. Same-week for advisory calls. Currently available — let's talk.

What's your hiring process? +

30-min intro → paid 1-week trial on a real (not contrived) problem → 1-hour debrief → offer. I want you to see how I think and ship, not how I wireframe in interviews. The trial is mutual — I'm evaluating you too.

Can I see live work even though it's NDA? +

Speculative redesigns of public AI products (Cursor, Linear AI, Anthropic Console) are coming to /work within 6 weeks. Until then, I can walk you through case study details under mutual NDA on a call — including artifacts I can show but not host publicly.

Thoughts & Writing

Subscribe
AI · Digital Entrepreneurship · Product

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 our industry.

Creative ClarityMay 5, 2026
AI · Startups · Cofounders

AI as Your Startup Cofounder

Why your next partner is already online. The Synthetix Cofounder: How AI is becoming the missing brain in early-stage teams, and what that means for founding designers.

Creative ClarityApril 30, 2026
AI · Product Design · UX

How I Forbid AI from Hallucinating

By interrogating it first. Designing safeguards into AI interfaces to prevent hallucination and maintain user trust. A practical approach to building reliability into ML products.

Creative ClarityApril 17, 2026

Let's Build Together

Primary Focus

Founding & Staff Designer for Intelligent Systems

I'm looking for founding or staff-IC designer roles at startups and enterprises building data-intensive, intelligent systems. The kind of role where I'm the first or second designer, working directly with founders/CTOs, shipping production code, and shaping products that handle complexity at scale.

  • • Take 0-to-1 intelligent products from prototype to production
  • • Design for complexity — explainability, confidence scoring, failure handling across industries
  • • Code in HTML/CSS/JS — ship the design, don't just hand off Figma
  • • Read SQL, understand data architectures, translate metrics into product decisions

Setup: Fully remote from India (GMT+5:30) · 4–5 hr daily overlap with US East Coast · Full UK/EU overlap

Not ready to talk?

Get my design notes — patterns, methodology, and lessons from shipping intelligent systems. ~1 email/month.

Book a 30-min intro call

Engagement Models

Three ways to work together. Same designer, different commitments.

Email LinkedIn X / Twitter