E-commerce marketers were drowning in data across Meta, Google, Amazon, Shopify, and GA4, and still couldn’t decide what to do next before the quarter ended.
✓What I shipped
I shipped a decision-first AI analytics platform: one IA, one design system, and a Smart Insights module that summarized 20+ data sources into three lines and a recommendation.
What I ownedUX strategy · Research · IA · Flows · Wireframing · Visual design · Design system · Hi-fi prototyping · Usability testing · Stakeholder alignment · Dev handoff · QA
✶ Live prototype
Try it
Tap into the live Strique app prototype, decision-first dashboard, Smart Insights, integrations across Meta, Google, Amazon, Shopify, GA4. The full flow, embedded inline.
↯ Click into the prototype above, fully interactive.
The opportunity
When I joined Strique in 2023, the performance-marketing stack looked like this: one tab for Meta Ads, one for Google, one for Amazon, one for Shopify, one for GA4, and a shared Google Sheet trying to hold it all together. The founders, Vatsal and Poojan, wanted to replace that chaos with a platform built by marketers, for marketers. Not another dashboard. A decision system, with AI doing the heavy lifting underneath.
The bigger story was already underway. Ad platforms were getting opaque. Attribution was getting harder. AI-driven insight was about to go from buzzword to baseline. We weren't just building a SaaS, we were designing for the moment marketing would shift from reading your data to asking it questions. Strique's pitch from day one: AI insights from every marketing data source, surfaced as decisions, not dashboards.
The problem
E-commerce teams weren't short on data. They were drowning in it.
Through early audits, stakeholder interviews, and sitting with marketers in our own office, the same pattern kept showing up:
No single source of truth across platforms
Reporting that was manual, slow, and error-prone
Metrics-heavy dashboards that buried the insight
Heavy reliance on outside agencies to interpret the data
Scaling campaigns meant spending more without knowing why it worked
The deficit wasn't data. It was clarity, confidence, and control.
The people I designed for were analytical but time-constrained, data-rich but insight-poor, under constant pressure to justify spend and show results. Their job wasn't to explore dashboards. It was to decide what to do next before the quarter ended. The product needed to think with them, and AI was the only way to do that at the scale they operated.
How I worked
1. Research, early signals, audit, benchmarks
My first instinct was to open Figma. I didn't. Two weeks of secondary research came first: performance-marketing tools, agency workflows, Reddit threads where marketers complained about their stack. The synthesis became the spine the product was built on.
Early research, secondary scan of the performance-marketing landscape, agency workflows, marketer pain pointsEarly research, Reddit + community threads where marketers describe what their stack actually looks like at 9am MondayEarly research, interview synthesis, themes clustered against the existing Strique product surfaceUser insights, what marketers actually do at 9am on Monday, their decisions, and the data missing from them
I audited the existing Strique surfaces and the platforms our users were juggling, Meta, Google, Amazon, Shopify, GA4, plus the homemade Google Sheets stitching it all together.
Audit, existing Strique surfaces + the cross-platform stack our users were juggling
✶ Inline prototype
Walk through the old system
Click into the walkthrough of the pre-redesign Strique surfaces, the cluttered dashboards, fragmented integration flows, and metrics-heavy reporting that I designed against.
↯ Click into the prototype above, fully interactive.
Then I benchmarked Strique against the category leaders, Polar Analytics, MadgicX, Revealbot, Agency Analytics. They had power. Most had cluttered interfaces, steep learning curves, or weak flexibility, and none were leading with AI.
Benchmarking, Strique vs Polar Analytics, MadgicX, Revealbot, Agency AnalyticsBenchmarks, feature × tool matrix, with the gap that became our product thesis highlighted
That gap became the product thesis: a decision-first, AI-driven, human-centered approach to analytics. Less "here are 200 metrics." More "here's what you should do tomorrow, and here's why."
Persona, the time-constrained, data-rich, insight-poor e-commerce marketer Strique was built for
2. Synthesis, notes, themes, problem statements
The research notes didn't go in a slide deck. They went on a working wall, themes clustered, recurring quotes elevated, the five repeating problems made into design briefs of their own.
Brainstorming with the team, synthesis wall, recurring quotes elevated, problem statements written
3. End-to-end user flows, logic before screens
I mapped every flow before drawing a screen: onboarding, integrations, AI-summary generation, reporting, the catalog. Dependencies between features, data sources, model outputs, and user actions. Edge cases, when the AI is wrong, when data is sparse, when integrations break, stress-tested before they ever became code.
User flow, master flow from onboarding through reporting, including the AI-summary loopUser flow, integrations, sync states, and error handling for the cross-platform stitchingUser flow, Smart Insights generation + recommendation surface, with confidence and override branches
4. Information architecture, built around how marketers think
I structured Strique around mental models, not tool categories. Onboarding, integrations, dashboards, reports, and catalog management got organized to surface insight early and minimize cognitive load. Navigation was built for quick scanning, customization, and modular expansion, because I knew AI features would keep shipping.
Information architecture, top-level structure organized around marketer mental modelsInformation architecture, module-by-module breakdown including dashboards, reports, integrations, and the AI surfaces
5. Wireframes, locking the structure
Lo-fi wireframes locked the dashboard pattern: most important metric first, AI-recommended action right beside it, supporting detail one click deeper. Power users took a minute longer to find customization. New users had something to act on in the first 30 seconds, the call I'd make again.
Wireframes, decision-first dashboard layout with AI-recommended action beside the headline metricWireframes, Smart Insights surface and reports view, with confidence + override patterns sketched in
6. Visual identity + design system
By mid-2024 Strique had outgrown its original identity. New clients, a new pricing tier, more AI features in flight. The visual system wasn't keeping up. I led the rebrand atomically: tokens first, then components, then templates, rolled out incrementally behind the scenes so the roadmap never paused.
Atomic design system principle, tokens, components, templates, organized so a single token swap re-skins the surfaceDesign system, final shipped system: color, typography, spacing, components, templates
7. UI, the shipped product
The dashboards were the hardest bet. Every competitor I benchmarked showed you a wall of numbers. The standard BI-dashboard pattern would have been safe and fast to ship. It would also have been wrong. Our users weren't analysts; they were marketers with budgets due Monday.
The hardest design problem on the platform was Smart Insights, Strique's AI-summarized reporting module. Years before the rest of the category caught up to AI summaries, we shipped a feature that took data from 20+ marketing platforms and distilled it into three lines and a recommendation. Designing it meant solving for AI-trust at the interface level: how confident should the model sound, when should it flag uncertainty, what does an AI-generated insight look like next to a human-set goal. The principle I held the line on: the AI summarizes and recommends; the marketer decides. The interface had to make that hierarchy obvious every time.
Strique UI, home dashboard, briefing-first, AI-recommended action beside the headline metricStrique UI, Smart Insights surface, the AI-summary that distills 20+ data sources into three lines + a recommendationStrique UI, integrations + cross-platform stitching, sync states, and the workspace-level connector trayStrique UI, reports + drill-down, with AI-generated narrative running alongside the chartStrique UI, catalog management + product analytics, the SKU-level surface that completes the decision loop
8. Mentoring Shreya: the multiplier nobody talks about
Halfway through my time at Strique, we brought on Shreya Jain as a design intern. I owned her onboarding, her design reviews, our weekly 1:1s, and the gradual handoff of visual work so I could focus on systems, AI-feature design, and strategy.
The thing no one tells you about being the only designer at a fast-moving AI startup: your biggest force multiplier isn't a better tool. It's a second designer who thinks like you. Teaching Shreya the why behind each design choice, not just the what, meant the product kept its coherence even when I wasn't the one drawing it.
Industry advisor sessions for Shreya, paired mentorship + external practitioner reviews built into her growth planTeam calls, design reviews and weekly 1:1s where Shreya and I worked through trade-offs togetherTeam calls, cross-functional reviews with engineering and the founders, where AI-trust trade-offs got arguedTeam group photo, the people behind the platform, in the office where most of this work happened
9. Rebranding as it scaled: atomic design for a product that wouldn't sit still
By mid-2024 Strique had outgrown its original identity. New clients, a new pricing tier, more AI features in flight. The visual system wasn't keeping up.
The cleanest-looking path would have been a stop-the-world redesign: two sprints, one new version, ship it. I rejected that approach. Shipping chaos compounds in a fast-moving SaaS, a two-week blackout is six months of lost roadmap momentum, especially when AI features are still being shipped weekly. So I led the rebrand in parallel with ongoing feature work, atomic pieces first: tokens, then components, then templates, rolled out incrementally behind the scenes. The cost was six weeks of subtle inconsistency in the UI as pieces migrated. The benefit was zero disruption to the roadmap. From the outside, Strique just kept looking more and more like itself.
The outcome
The product shipped, scaled, and got used.
3,200+
Businesses growing with Strique
$4M+
Worth results generated for businesses
1.5x
Better ROAS than doing it yourself
0%
See sales growth in their first month
The most convincing validation didn't come from our team. It came from Lilly & Sid, a UK retailer 11 months into using the product:
"I didn't believe the hype at first as a seasoned eCommerce owner for more than 20 years. After installing it on the free trial, the results have been amazing. Revenue increased by 33% and the reporting has saved the team so much time. I love the AI aspect and I haven't even started using all the features yet."
Lilly & Sid, Founder & CEO, UK
That quote is still pinned to strique.io today. I check.
User testimonial, Lilly & Sid, the customer quote that became the most reliable validation
Evolution
Strique kept evolving after I left, and the trajectory the team was already discussing in 2023 has played out almost exactly as we hoped.
By 2026 the product has matured into an Autonomous Intelligence Platform Driving Growth, "one dashboard, one AI assistant, zero guesswork." It now runs paid media, SEO, websites, social, content, PR, and influencer marketing together, 24/7, with a chat interface at chat.strique.io and a category positioning the team calls vibe marketing. The industry has a new label for what Strique does now: Virtual CMO.
What I shipped in 2023–2024 is the foundation the new product runs on:
The information architecture I designed still anchors the AI-driven site
The "Smart Insights" module, the early AI-summary feature I designed, was the UX precedent for what the AI now does autonomously
The landing-page positioning of "AI insights from marketing data," shipped two years before the autonomous platform launched, set the brand framing the product still uses
The atomic design system I built kept Strique shipping new surfaces (including all the AI ones) without redesigning from scratch
It's been satisfying to watch a product I poured into grow up the way it has, into something more autonomous than any of us could have built in 2023.
Reflection
Three things I'd tell my 2023 self.
1. The system matters more than the screens. My best work at Strique wasn't the pixels I pushed alone. It was the logic map, the design system, and the trade-off conversations that made every subsequent decision cheaper. A month more on polished mockups, a week less on architecture, and the product doesn't last.
2. AI features work when they defer to the workflow. The moments we over-indexed on "AI will do it for you" and under-indexed on "what does the marketer actually do at 9am on Monday" were the moments we had to redesign. The best AI I shipped at Strique was the kind you barely noticed, it summarized, it recommended, it got out of the way. Designing for AI is, mostly, designing for the human still in the loop.
3. Mentoring is designing. Teaching Shreya how to think about a problem was the highest-leverage thing I did in my last quarter at Strique. The product still has her fingerprints on it.
Strique wasn't just a product I shipped. It was a system I helped shape, and one that was strong enough to grow into something bigger than any of us imagined in 2023.
✶ Thanks for reading
That’s the case study, front to back.
If you want to dig into anything I skimmed over, process, edge cases, the trade-offs that didn’t fit on the page, reply by email or send this to a teammate.
