Zentavor AI Search is built to reduce “zero results”, increase relevance, and shorten time-to-product (or time-to-answer)

Hybrid keyword + semantic

Replace “keyword-only” search with a hybrid semantic engine that finds the right items even when users misspell, use synonyms, or describe needs in natural language. Improve discovery across catalog, content, and internal knowledge — with enterprise-grade analytics and experimentation

Reranking & relevance

Odatdagi natijalar

You get a search layer you can measure, tune, and scale — not a black box

Enterprise-ready deployment

conversion uplift

5–20%

fewer “no results”

20–60%

higher revenue per search

10–30%

higher search-to-click

10–35%

xpected impact depends on domain and baseline search quality:

We validate impact via A/B tests and segment-level analytics (new vs returning users, categories, locales)

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Smart AI Search

Gibrid
retrieval:

keyword + vector search working together

Reranking:

LTR / neural rerankers for precision at the top

Query understanding

spelling correction, synonyms, entities, intent

Merchandising controls:

boosts, rules, availability, margins

Analytics & A/B tests:

relevance you can measure and optimize

Smart AI Search is a modern retrieval system that combines multiple signals — lexical matching, semantic similarity, behavioral signals, and business rules — to rank results the way users expect

Keyword search breaks when queries are vague (“good laptop for design”), contain typos, or don’t match catalog wording. Semantic retrieval fixes that by understanding meaning. However, semantic-only search can miss exact constraints (“iPhone 15 256GB”). That’s why production search should be hybrid

Zentavor implements smart search as a measurable capability: with analytics, tuning workflows, monitoring, and experimentation to continuously improve relevance and business outcomes

Core components

Users don’t type exact catalog names — they search in their own words. If search doesn’t work, they leave or switch to a competitor. Intelligent search reduces failures through semantic matching, synonyms, and tolerant parsing

“No results” and dead ends

Keyword scoring often overvalues exact terms and ignores intent. Reranking improves top results, where the business impact is highest

Relevant items are buried

Real users write “waterproof backpack for hiking 30l” or “invoice export doesn’t work”. Semantic retrieval and entity extraction handle long-tail language without manual rules for every case

Long-tail queries don’t work

If you can’t see why results are ranked, you can’t tune them. Zentavor adds analytics: query clusters, zero-result reasons, click feedback loops, and experiment dashboards

No visibility, no improvement

Start with the essentials (hybrid search + analytics), then add advanced modules such as personalization, multilingual search, and cross-domain retrieval as you scale

Hybrid retrieval (keyword + vector)

Combines exact matching and semantic similarity

Neural reranking & relevance tuning

Improves the first page where most users decide. Supports LTR, neural rerankers, and business-aware ranking

Query
understanding

Spell correction, synonyms, entities, intent, category prediction, and structured filters extraction

Personalization (optional)

Adapts ranking by user behavior, segments, and context — while keeping controls for merchandising and safety

Multilingual
search

Strong performance for both precise and descriptive queries

Analytics & experimentation

Track search-to-click, zero-results, revenue-per-search, CTR@k, and run A/B tests to prove impact

The same architecture works for eCommerce catalogs, marketplaces, media/content libraries, and internal enterprise portals. We tailor query understanding and ranking signals to your domain

Support
& self-service

Reduce ticket volume and improve CX:

RAG-ready retrieval for AI assistants

Deflection analytics: what users searched before creating a ticket

Continuous improvement loops from unresolved queries

Find the right documents fast:

Semantic search over PDFs, articles, policies, wikis

RAG-ready retrieval for AI assistants

Access control-aware search (RBAC/ABAC)

Content & knowledge search

eCommerce & marketplaces

Improve product discovery and revenue:

Better relevance for long-tail and natural language queries

Smarter facets: auto-detected filters and attributes

Controlled boosts (availability, margin, campaigns)


Zentavor AI Search is delivered as a modular stack. You can deploy it in cloud, on‑prem, or hybrid. We integrate with existing search engines (or replace them), and provide the relevance layer to keep improving

We build a robust pipeline so search remains accurate as your data change

Index strategy: lexical index + vector index + metadata store

Normalization: attributes, units, locales, synonyms

Connectors for catalogs, CMS, PIM, ERP/CRM, file stores

reshness: incremental updates, near-real-time for critical fields

Data & indexing

The system supports explainability at the level needed for tuning and stakeholder trust

Business rules: boosts, demotions, availability, policy constraints

Reranking for top results with neural models or LTR

Hybrid retrieval to cover both exact and semantic needs

Personalization (optional): context and behavior signals

Retrieval & ranking

Synonyms and domain dictionary management

Spell correction and typo-tolerant search

Autocomplete: popular queries by entities and categories

Facets and filters, including automatic constraint detection

Did-you-mean suggestions and query rewriting

UX features users expect

Biznes KPI: konversiya, revenue‑per‑search, AOV

Relevance KPI: CTR@k, NDCG, time‑to‑click

Query analytics: clusters, zero-results, abandonment

Experiments: A/B testing, holdout groups, and segment analysis

Monitoring: latency, drift, data freshness, error rate

Analytics & optimization loop

Discovery
& baseline

MVP
сin 3–6 weeks

Rollout
& scaling

Continuous optimization

We deliver smart search as a production capability: baseline, MVP, rollout, and continuous improvement. The goal is to prove impact early and scale without rewriting

We balance semantics and strict constraints using result re-ranking and business controls for predictable outcomes

Hybrid search done right

Analytics and experimentation are built in by default. We prove impact, analyze segments, and establish a repeatable optimization loop

Built for measurable impact

Deployment in cloud environments, on local infrastructure, or in a hybrid model.
Data management, access control, auditability, and reliability for production-scale workloads

Enterprise-grade deployment and security

End-to-end solution delivery: data ingestion, indexing, models, APIs, admin panels, and MLOps monitoring — aligned with your technology stack

Strong expertise in AI and data engineering

Share your current search stack and KPIs. We’ll propose an architecture, an MVP scope, and a measurement plan to prove uplift fast

Email: info@zentavor.ai

If you already have search logs, we can quickly establish a baseline and identify the query clusters with the highest potential impact

What to send to start

Catalog/content size and languages

Current search engine (or platform) and constraints

Key KPIs (conversion, CTR@k, search-driven revenue, zero-result queries)

Top query clusters and pain points (if any)

Deployment requirements (cloud / on-prem infrastructure, latency, compliance)