REZINX
Transparent AI Solutions
Personalization & Search AI
We build high-converting search and recommendation layers for e-commerce platforms, from semantic product search and catalog tagging to personal shopping agents and automated review analytics. Built with LangChain and Qdrant.
Capabilities
Every system is designed to improve conversion rates and streamline catalog administration.
Upgrading keyword matchers to understand user synonyms, complex descriptors, and color contexts. Keeps search results highly relevant to purchase intent.
LangGraph sales agents that answer customer questions, advise sizing, find products in real-time inventory databases, and add them to carts.
Extracting colors, materials, patterns, and categories from images and descriptions automatically. Reduces manual catalog entry efforts by 90%.
Ingesting reviews to detect material issues, sizing complaints, or design problems. Synthesizes qualitative reviews into structured insights for product design teams.
Matching dynamic styles based on vector comparisons of past orders, user preferences, and real-time navigation paths.
24/7 support assistants resolving refund status queries, shipment tracking, and return labels using integrated store logic.
Conversion Growth
We connect semantic search vectors directly into active product databases, resolving intent gaps to improve sales and basket values.
Our e-commerce AI stack relies on LangChain pipelines for catalog structuring, Qdrant on AWS for fast search indexing, and secure webhook syncs to connect store APIs.
Vector queries return search results in under 50ms, maintaining page speed.
Built-in connectors for Shopify, WooCommerce, Magento, or custom store databases.
Auto-generate clean product tag metadata and alt-text tags for SEO ranking.
Payment tokens and customer profiles are isolated, complying with PCI-DSS guidelines.
Traditional search matching breaks down when users type typos, synonyms, or descriptive sentences (like "something for a summer wedding"). Semantic search converts both catalog items and user queries into spatial coordinates, returning correct matches based on conceptual intent rather than literal text matches.
Yes. The LangGraph agent hooks into your store checkout API, allowing it to check discount code validity, calculate final shipping fees, and present updated cart values directly to the shopper.
Yes. The catalog engine uses high-throughput batching pipelines that scan thousands of items, generate descriptions, categorize features, and output tags directly to your CMS within hours.
We deploy standardized API middleware that syncs via secure webhooks. Every item addition or price change triggers a sync update to our vector indexes automatically.
Most e-commerce deployments see search exit rates drop by 20–30% and overall conversions increase by 10–15% within the first month of activation due to more relevant results.
Ready to deploy semantic search, automated tagging, and high-converting sales agents? Book a free consultation with our e-commerce AI architects.
Book a Free E-Commerce Consultation