See how we help enterprises turn AI into measurable business results: from recommendation engines to data infrastructure

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Recommendation engine for a major grocery retailer


Personalized product recommendations across website and mobile app, powered by ML models trained on customer behavior

Results:

+25% conversion

4× CTR

+18% avg order

Industry: Retail/E-commerce

AI-powered first-line support automation

AI copilot that classifies tickets, searches the knowledge base, and drafts responses for operators

Results:

45% auto-resolved

3× faster response

24/7 coverage

Industry: Cross-industry

Omnichannel AI chat operator


AI chat platform unifying Telegram, Instagram, WhatsApp, and web chat with 24/7 automated responses

Results:

-60% operator load

≤15× response

35% leads

Industry: SaaS/Cross-industry

AIDoc - corporate intelligence over documents

Conversational access to corporate knowledge base with source-verified answers and on-premise deployment

Results:

Faster knowledge access

Lower search costs

On-premise ready

Industry: LLM, AI Agents, Smart AI Search

Dynamic pricing engine
for self-storage


Revenue management tool replacing manual pricing with data-driven rate optimization across 85+ US facilities

Results:

ML-driven pricing

Competitor monitoring

Demand forecasting

Industry: LLM, AI Agents, Smart AI Search

AI Agent integration for ETL monitoring & incident response

Automated diagnostics for data pipeline failures — root cause analysis, knowledge search, and ticket enrichment

Results:

85% auto-processed

3× faster retrieval

-60% engineer load

Industry: Enterprise IT/Data Operations

Industry: Retail/E-commerce

1 500 stores

The retailer faced a classic e-commerce scaling problem: as the product catalog grew, customers increasingly struggled to navigate the assortment. Conversion rates remained flat despite growing traffic, and average order values plateaued. The existing product suggestions were rule-based and generic, meaning the same recommendations for everyone, regardless of purchase history or preferences.

Client:

One of the largest supermarket chains

Solutions used:

Recommender systems, Data Science & ML

Tell us about your data and goals — we’ll propose architecture, timeline and delivery model

Email: info@zentavor.ai