Building the intelligence layer media professionals never had — real-time competitor ad spend, cross-screen audience reach, and AI-powered campaign strategy in one place.
AdAtlas AI turns hours of manual research into a single query — giving every media professional access to the intelligence that once required expert knowledge, exact naming conventions, and a steep learning curve to unlock.
Project: AdAtlas AI
Role: Product Designer
Work: Strategy,Product Design, Prototyping, Design System.
Platforms: Web Application (SaaS)
AdAtlas AI is the AI-first evolution of Ad Intel — a market-leading competitive intelligence platform covering ad spend, creatives, and placements across 90+ markets, TV, CTV, digital, and print. Ad Intel is powerful but built for power users. AdAtlas AI is the AI version — same depth of intelligence, accessible to everyone through a single natural language query.
Ad Intel had the best competitive advertising data in the industry — exclusive ad spend, creatives, and placements across every major market. But it required expert knowledge to operate: exact brand naming, complex filter hierarchies, multi-step exports. Most users weren't power users. AdAtlas AI was built as the AI version of Ad Intel — to unlock that same intelligence for everyone, not just the experts.
CHALLENGE
Build a new AI-first competitive intelligence product — that does everything filters, dashboards, and manual workflows used to, through conversation alone.
The brief was to design a new AI product from the ground up — not to fix an existing tool, but to reimagine how competitive intelligence works entirely. No filters. No dashboards. No pivot tables. Users simply ask, and the AI handles the rest.
Research during the build — interviews, beta feedback, and usage data — surfaced 12 ranked pain points. Four were critical, each one blocking trust, adoption, or task completion before the product could succeed:
Low trust in AI accuracy — Users had to validate every AI output against the traditional tool before using results in client-facing work. The AI had potential but trust was the barrier.
Clunky, hard-to-learn UI — Customers described the existing experience as "not user-friendly," requiring prior product knowledge just to get started.
Slow, time-consuming data retrieval — Users reported slow dashboards, lengthy query runs, and tedious workflows just to pull basic competitive spend data.
Exact brand naming required — If a user didn't know the precise name and hierarchy structure in the system, queries returned nothing or misleading results.
Manual reporting with no shortcuts — Users relied on client lists, Google, and ChatGPT to find competitors, then built pivot tables manually before creating any report.
Solution
AdAtlas AI replaces the traditional tool entirely. Where users once needed expert knowledge, exact naming conventions, and hours of manual work — they now just ask. The AI handles search, discovery, analysis, and reporting conversationally. The design focused on making that shift feel natural and trustworthy from the very first query, across three phases:
Trust and usability foundation — Source-backed answers with filter transparency, precise error messaging, and natural-language workflows to replace rigid report-building.
Fuzzy search and brand discovery — The AI supports fuzzy matching, synonym handling, and "did you mean" recovery so users no longer need to know exact system nomenclature.
Reporting and analyst acceleration — Reusable report templates, scheduled competitive updates, and presentation-ready charts and exports generated automatically.
Guided competitor discovery — The AI proactively suggests relevant competitors, adjacent advertisers in-category, and alternate brand variants conversationally.
Strategic intelligence layer — Proactive alerts on competitor spend shifts, AI-generated insights, and personalised views by user type — analyst, buyer, strategist, or seller.
Design Process
An iterative, AI-assisted process — each phase informed the next, with continuous testing loops to validate decisions before moving forward.
My Role
As a UX Designer on AdAtlas AI, I was responsible for designing intuitive Al-powered experiences that help marketers uncover insights, analyze competitor activity, and make faster campaign decisions.
Target Audience
Research identified three distinct user types — each with different goals and a different definition of success. The design had to serve all three without compromising any of them.
Media Directors at agencies oversee multi-million dollar budgets across multiple client accounts. They need real-time competitor spend data by channel — without pulling it manually from four different tools every morning.
Brand Managers working in-house monitor rival campaigns daily and are under constant pressure to respond to market shifts faster than the competition. What they need most is an alert the moment a competitor moves — not a report about it two weeks later.
Strategic Consultants advise C-suite stakeholders on media investment and spend the majority of their time building competitive benchmarks and cross-screen reach reports by hand. Their need is simple — polished, data-backed executive decks in minutes, not days.
Design Strategy
Key decisions that shaped the product — each one traceable to a real user problem, research signal, or beta finding.
01
Prototype testing
Conversational entry — not a filter
Users defaulted to looking for a filter bar. Made the AI prompt the unmissable hero element.
Filter habit → AI as entry point
02
Beta sessions · query analysis
Progressive clarification — ask before answering
The AI now asks one focused follow-up question first — narrowing scope before answer, so the response is faster & more precise.
Big query → Clarify first, ans precisely
03
Manual reporting pain
Split canvas — chat + dashboard
Every workflow ended in Excel then PowerPoint. Split canvas closes the loop — answer and export in one view.
Exit → Done inside product
04
Beta feedback · error patterns
Precise error states — not "something went wrong"
Vague errors broke trust immediately. Each failure now tells the user exactly why — and what to do next.
Vague error → Reason + next steps
05
No existing patterns
AI component library — from scratch
Existing design systems had no AI-specific components. Built a dedicated library — the foundation for every future AI feature.
06
Prompt log analysis
Fuzzy search + "Did you mean?"
Users typed brand names how they remembered them. Wrong spelling returned nothing. AI now absorbs naming complexity.
Wrong name → Fuzzy + recovery always
Design System
AdAtlas AI was built on Nielsen's existing design system — most components were reused with minor tweaks to fit the AI context. For AI-specific interactions, we built from scratch: a chat interface, an AI loader/thinking state, and data chart components had no existing patterns to pull from.
Qualitative Research
Where quantitative data told us how many users had a problem, qualitative research revealed why. I observed live customer interviews and VOC sessions via video call, reviewed prototype usability recordings, and watched real users interact with the product in beta — not just reading what they reported, but seeing what they actually did. That direct observation surfaced what data alone misses.
Observed · Prototype Sessions
Users couldn't find the AI entry point
In prototype sessions with the ambassador team, users instinctively looked for a search bar or filter panel — the muscle memory from the old tool. The conversational prompt wasn't obvious. Watching this happen in real time — not reading a survey — is what led to redesigning the entry point entirely.
Observed · Beta Video Sessions
Users scanned for a source before trusting
Video session analysis showed a consistent behaviour — users read an AI answer, paused, then scrolled looking for where it came from. They weren't rejecting the answer; they needed to see it was grounded. This behavioural pattern, not any survey score, is what drove the source attribution design decision.
Observed · Prompt/Output Review
Users typed differently than expected
Reviewing real prompt inputs revealed users typed brand names the way they remembered them — not the way the system stored them. Partial names, common abbreviations, parent company names. Watching real query patterns, not assumed ones, directly shaped the fuzzy search and "did you mean?" interaction design.
What user say about Adatlas AI and its design
"I didn't have to think about which filter to use. I just described what I wanted and it found it."
Beta User
Customer interview - AI Reliability
"This is actually how I wish the tool always worked — I just asked and it gave me exactly what I needed."
Beta User
Customer interview - Brand Matching
Oh — so the chart just updates when I ask? I thought I'd have to go somewhere else to see it.
Beta User
Customer interview - Reporting Workflow
Quantitative Research
Mixed-methods approach — VOC sessions, beta feedback, usage reports, and raw prompt logs. Quantitative signals confirmed which problems were widespread patterns, not isolated complaints.
Pain Points Ranked
Explainable AI outputs
Scored and ranked by severity across all data sources. Each problem rated on impact to adoption and task completion:
Critical
4
High
5
Medium
3
Quantitative Data Sources
3
Three data streams provided the numbers behind the pain points — each measuring a different type of user signal:
Beta Feedback
Usage Reports
PromptLogs
Silent Signal
Regen Rate
Prompt logs revealed a high regeneration rate on AI responses — users weren't explicitly complaining, but they were rejecting outputs silently. A critical quantitative signal that confirmed the trust gap before users said a word.
Top Themes Confirmed by Data
AI Accuracy
Every data stream pointed here — users double-checked, regenerated, and abandoned outputs at the same step. The pattern was consistent across all beta users.
Complexity
Usage reports showed high drop-off at the query stage. Users started a search and stopped — the entry point created friction before they even got to a result.
Manual Reporting
Prompt logs showed users asking for raw data, then disappearing — exiting to build reports manually in Excel rather than using the product's output directly.
Product Status
Beta · 2025
Actively iterating on every finding
Interaction UX Process for Generative AI
Since AI replaced a workflow users were used to doing manually, I designed lightweight feedback points at each AI step to gauge trust and output quality:
Thumbs up/down + comment — quick signal on whether an output was useful, with optional context on why
Star rating (1–5) — used in testing to capture quality more precisely than a binary up/down
Accept / Edit / Regenerate tracking — showed whether users trusted outputs as-is, tweaked them, or rejected them
Regeneration rate — a high rate on any step flagged that the AI output wasn't landing, even without explicit negative feedback
AI Behaviour Framework · By User Role
Defined what the AI must do, must never do, when to flag uncertainty, and what's at risk if it gets it wrong — for each of the five user types on the platform.
Business Impact
Nielsen Ad Intel tracks competitor ad spend, creative, and placement across TV, CTV, digital, print, and OOH in 90+ markets — exclusive data no other platform offers. The problem was never the data. It was that accessing it required trained power users, lengthy onboarding, and hours of manual filtering. AdAtlas AI was built to change that.
Increased Efficiency & Productivity
Manual filters, slow queries, pivot tables — 3–4 hrs per report. Now one query.
Same competitive insight, delivered in minutes — no filter, no export, no spreadsheet.
Analyst time shifts from data retrieval to strategy — higher-value output, same team.
Cost Savings & ROI
No need for external tools — users no longer supplementing tools to find competitors, reducing tool sprawl.
Significant decrease in operational costs — no dedicated training programme needed, reducing onboarding overhead per client at scale.
User Adoption Rate
75% task completion in beta — real workflows completed on first query, no training needed.
Multiple clients trialling and purchasing the AI version — commercial traction from beta stage.
Beta users requesting next version — strong product-market fit signal.
Enhanced Data Utilization & Accuracy
80% query accuracy — source-attributed results, no manual cross-referencing needed.
Competitive spend data found 3× faster than Ad Intel's manual filter workflow.
Users now access the full depth of Ad Intel's dataset — capabilities that were previously locked.
Bottom Line
Nielsen has always had the best data. AdAtlas AI makes it accessible to everyone — removing the complexity barrier so every client user can get competitive intelligence through a single conversation.
Atlas Final Product Design
Related Works
Smarti • Web portal
Libra • SAAS product
Let's talk,
samsusmsu123@gmail.com