PILOTS AND DEPLOYMENT

SKINBOT pilots and deployment scenarios

SKINBOT is designed as a lightweight AI layer over an existing catalog. The pilot is not there to present a pretty interface, but to quickly test impact on choice, conversion, and experience quality in the beauty category.

Launch usually starts with a limited assortment and one entry scenario so that the team can see real effect quickly and decide on scaling.

Week 1: connection

Catalog integration, operating mode setup, and logic calibration to the assortment.

Weeks 2-3: pilot

A limited category, first sessions, and engagement and choice-quality metrics.

Week 4: decision

A report on the scenario, growth points, and scaling format for new storefronts and categories.

WHERE IT WORKS

Typical launch scenarios

On a marketplace, SKINBOT helps narrow an overly broad catalog and reduce wrong choices in skincare. On the brand side, it turns a lineup into a personal dialogue rather than just a banner with a promise.

In retail, the product works as an AI consultant for online and offline touchpoints: product card, landing page, consultant tablet, QR in the trading floor. In duty-free, it is especially useful where multilingual support, speed, and clear explanation matter.

Marketplaces

Neutral selection from the full platform catalog without a conflict of interest between brands.

Retail

A unified AI scenario for the online store, sales floor, shelf, and consultant.

Brands

Brand-scoped mode for new collections, seasonal campaigns, and personal discovery of the lineup.

METRICS

What to measure in a pilot

A SKINBOT pilot should not be evaluated by interface views, but by how user behavior changes inside the category journey. In beauty retail this matters especially because a wrong choice destroys trust faster than in an ordinary commodity category.

  • Share of users who completed the selection flow and reached recommendations.
  • Conversion from the selection session to product-card view or move to cart.
  • Average number of products in the personalized routine compared with the regular entry point.
  • Qualitative feedback: whether the choice is understandable and whether the user trusts the recommendation.
INTEGRATION

Plug in within 1–2 weeks. No infrastructure rewrite.

API architecture. No system rebuild. A lightweight layer over the catalog with no heavy dependencies or lock-in.

01Brief and catalog audit1–2 days
02SKU tagging and language layer2–3 days
03API / QR / iframe integration2–3 days
04QA and compliance check1–2 days
05Pilot launch at one locationLIVE
// Minimal integration via iframe
<iframe
  src="https://skinbot.ru/embed"
  data-partner="your-brand-id"
  data-catalog="v3"
  width="100%" height="640"
></iframe>
// API integration for deep customization
POST /api/v1/recommend
{
  "answers": [...],
  "catalog_id": "your-brand-id"
}
RELATED PAGES

Where to go next

For marketplaces
Neutral selection from the full platform catalog.
For retail
Scenarios for beauty chains, pharmacies, and department stores.
Articles and explainers
SKINBOT materials on skincare choice, AI, and the privacy-first approach.

Launch pilot in a month.
Pay for results.

A one-month pilot with fixed metrics. Integration via API, QR or iframe. Compliance-ready architecture. Support team at every step.

Reply within 24 hours · NDA on request