AI-powered personalization

AI-powered personalization is an implementation project (via an ML personalization/recommendation engine or your infrastructure): we connect data, set up algorithmic delivery of content/recommendations/offers for the user, defaults, metrics and quality control. Honestly about the prerequisites, this is key: ML works only with a SUFFICIENT volume of quality data and traffic — on small data it is no better than simple rules (and often worse and unpredictable); 'AI' does not work a miracle from poor data (garbage in, garbage out). Honestly about cold start: for new users and products there is no data, so rules/popular work first, not a personal model. Honestly about explainability and control: ML decisions are not always transparent ('why it showed this') and can give strange/irrelevant selections — quality control and limiters are needed so the algorithm does not show something inappropriate; we build this in. Honestly about the result: AI personalization CAN raise relevance and metrics, but we do NOT guarantee growth and do not promise '+X%' — it should be compared with simple rules and validated with A/B (sometimes rules give almost the same more cheaply). Honestly about data/privacy: behavior personalization is user data; consent/a policy (lawyer). Honestly about the essence and sobriety: this is ML personalization, NOT an all-powerful 'AI' and NOT a guarantee; often rules/basic recommendations (644/658) are enough for a business — we honestly say if AI is overkill. An important boundary: this is AI personalization, not basic personalization (644), not rule-based recommendation widgets (658) and not federated learning (660). If data/traffic is low, AI is premature. Picture this: instead of manual rules for every segment the algorithm itself selects the relevant — if there is enough data and it is justified against simple rules. The base price starts from 30,000 ₽ per project; it depends on data, the engine and integration (the platform — separate).

• Too many manual rules for each segment, it does not scale.
• You want relevance 'for everyone' but without endless rules.
• You are not sure there is enough data/traffic for ML.
• You fear 'AI for AI's sake' without real benefit — justified.

• Connecting data and an ML personalization/recommendation engine
• Algorithmic delivery of content/recommendations/offers
• Cold-start handling (rules/popular for new ones)
• Quality control and limiters (not to show something inappropriate)
• Comparison with simple rules (is AI needed at all)
• Accounting for privacy/consent (assessment — by a lawyer)
• A recommendation and help with A/B (AI vs rules)
• Reviewing with you

• Relevant delivery without endless manual rules
• Cold start handled for new users/products
• Quality control against strange selections
• A hypothesis to validate against rules (growth — not guaranteed)

• We assess data/traffic sufficiency; is AI needed or are rules enough
• We connect the engine, data, delivery, limiters and defaults
• We compare with rules via a test, review with you

• Does AI personalization guarantee a conversion increase?

  No. ML can raise relevance and metrics, but growth depends on data, traffic and the catalog. It should be compared with simple rules and validated with A/B — sometimes rules give almost the same more cheaply. 'AI' is not magic; we do not promise '+X%'.

• I have little data — will it work?

  Probably not. ML requires a sufficient volume of quality data and traffic; on small data it is no better than rules, and often worse and unpredictable (garbage in, garbage out). If data is low, we honestly say AI is premature and suggest rules.

• How is it different from regular personalization and recommendations?

  Basic personalization (644) and recommendations (658) are by rules/links; AI personalization is an algorithm learning from data. AI is justified with large data and scale; for many tasks rules are enough — we honestly advise.