How Deconstruct brought its science-first approach to skin diagnosis with Fynd s AI Skin Analysis

Deconstruct is one of India's most trusted science-backed skincare brands, known for formulating gentle, highly effective products for beginners and skincare enthusiasts alike. The brand's entire identity rests on transparency - every serum and sunscreen lists exact ingredient concentrations, backed by clinical data instead of marketing claims.

When Deconstruct decided to bring this same philosophy to skin diagnosis, they wanted a tool that did not just detect skin concerns but also explained the why behind them, the same way their product labels do.

The challenge: Turning ingredient literacy into skin clarity

Deconstruct's audience is the informed skincare buyer, people who already read ingredient lists and percentages. Even they ran into three recurring gaps:

Self-diagnosis isn't reliable

Most people misread their own skin type and concerns, so even the most ingredient-literate buyers still ended up picking the wrong actives.

Education alone doesn't convert

Content that explains ingredients builds trust, but it does not tell an individual user which product is right for their skin.

Hard to stand out

Every skincare brand now publishes ingredient-led content. Deconstruct needed an experience competitors could not copy with a Canva post.

What the brand needed was a bridge from education to personal relevance: show each user their actual skin data, then map it to the right actives.

The solution: Decoding skin the same way Deconstruct decodes ingredients

Deconstruct partnered with Fynd AI Skin Analysis - an AI-powered facial scan that reads a user's skin condition and connects it directly to Deconstruct's ingredient-first product philosophy.

Fynd's GlamAR integrated advanced facial mapping and multi-concern detection directly into Deconstruct's website, keeping the experience true to the brand's clinical with no nonsensical tone.

The journey: from scan to shelf

Step 1: Instant scan 

A selfie produces a full skin report in seconds with no signup friction.

Step 2: Multi-concern detection 

Post-acne scars, dark circles, whiteheads, hydration, pigmentation, wrinkles, acne, pores and more, each scored individually.

Step 3: Personalised recommendations

The report maps detected concerns directly to Deconstruct SKUs, the right acid and the right percentage with add-to-cart built into the journey.

Step 4: Accuracy on different skin tones

Detection is tuned across the medium, light, olive and tan spectrum that mirrors Deconstruct's actual customer base.

The impact: From scan to cart, without a human in between

Roughly 1 in every 46 people who scanned their face went on to convert to a recommended product. The scan itself did the selling.

• ~24,800 skin analysis scans completed on the Deconstruct site

• 2.2% conversion, directly from the skin analysis recommendation journey, spread across 25 unique SKUs

• Recommendations converted across the catalog, not just one hero product, toners, serums, sunscreens, moisturisers and lip care all saw add-to-cart activity

Why it worked

Deconstruct's audience already trusted the brand's science. The skin analysis gave that trust a personal dimension, not "niacinamide reduces pigmentation" as a general claim, but "your scan shows pigmentation, and this is your product."

Education became personalisation and personalisation became conversion.

The campaign layer is what makes this different from a typical skin-analysis integration: the tool generated the content, the content drove the scans and the scans drove carts, a closed loop between social and site that most D2C brands never manage to build.

If you are a skincare brand looking to turn scientific credibility into a personalised, AI-led shopping experience, Fynd can help you get there.

Book a demo today