RaahiSpot It. Fix It.
Validated by 78.9% of 57+ UX practitioners - a purpose-built AI dark pattern detection tool that gives researchers a systematic, portable way to catch harmful patterns before they ship.
AI interfaces manipulate users.
No one has a tool to stop it.
AI-generated interfaces move fast - too fast for practitioners to manually review. Dark patterns slip through: hidden unsubscribes, forced consent flows, deceptive defaults. There is no systematic, portable way to detect them before they ship.
"I know dark patterns when I see them - but I have no systematic way to document or prove it to a stakeholder."
How might we give UX practitioners a reliable, systematic tool to detect and document AI-generated dark patterns - before harmful designs ship to users?
Four phases.
Research-first, always.
The project was deliberately research-heavy - spending the first two-thirds understanding the problem before writing a single line of product spec. Every design decision traces back to a practitioner insight.
- Literature review on dark patterns
- AI interface taxonomy
- Problem framing
- 12 practitioner interviews
- Survey - 57+ respondents
- Competitive audit (8 tools)
- Affinity mapping + synthesis
- Detection framework design
- 3-tier category taxonomy
- Go-to-market positioning
- Toolbar UI design in Figma
- Brand identity - Raahi
- Practitioner validation (78.9%)
- iD Lab Shark Tank pitch + win
What 57 practitioners
told us about dark patterns.
The survey and interviews converged on one urgent insight: practitioners know the problem exists - they just have no shared language or tooling to act on it consistently. That gap is Raahi.
"78.9% of respondents confirmed they encounter AI dark patterns regularly - but 94% have no consistent process to document them."
Why a toolbar?
Three deliberate decisions.
Practitioners review live products inside the browser - friction kills adoption. A toolbar embeds directly into the workflow, requiring zero context-switching.
Arming practitioners to catch dark patterns upstream scales the impact. One practitioner protects thousands of users - a multiplicative approach to ethical design.
We mapped dark patterns into three tiers - Coercive (manipulates action), Deceptive (hides intent), Addictive (exploits cognition) - giving practitioners a shared vocabulary for reporting.
From research to
product.
Raahi is a browser toolbar that detects, categorises, and surfaces AI-generated dark patterns in real time - with one-click reporting so practitioners can document findings without leaving the page.
Validated by the
people who matter.
Of UX practitioners confirmed encountering AI dark patterns regularly and expressed clear intent to use a dedicated detection tool.
UX researchers, designers, and product managers across industries contributed to the primary research validation phase.
Competitively funded by DePaul's iD Lab after pitching to a panel of investors and faculty. Selected from multiple competing ideas.
Conducted 12 in-depth interviews and a 57+ respondent survey across UX practitioners in the US and India to validate the problem and shape the detection framework.
Raahi was selected for funding by DePaul's iD Lab entrepreneurship programme after a competitive Shark Tank-style pitch to faculty and industry investors.
Final validation survey confirmed 78.9% market fit - practitioners rated the toolbar concept as a clear improvement over existing ad-hoc review methods.
What building Raahi
taught me.
78.9% validation came from thorough research - not assumption. Every product decision was grounded in practitioner evidence, not intuition.
One practitioner protects thousands of users. Targeting the auditor - not the end user - is a multiplicative approach to ethical design at scale.
The three-tier taxonomy gave practitioners a shared vocabulary. Before Raahi, "dark pattern" meant something different to every person in the room.
The iD Lab Shark Tank wasn't just funding - it was a forcing function to stress-test the business model, market sizing, and differentiation under real scrutiny.
Next project: VR mental wellness companion - 71% student validation, showcased at Jarvis Innovation Challenge.
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