Search & commerce
infrastructure you control
We help retailers with large, technical catalogs improve product discovery, migrate platforms, and connect PIM, ERP, search, and storefront systems — without sacrificing speed or control.
Complex catalogs. Measurable results.
Own the discovery layer — not another plugin stack.
Bring catalog data, fitment, content, behavioral signals, and merchandising rules into one model serving search, category pages, product pages, and AI experiences.
Answers grounded in current catalog facts, compatibility data, availability, and merchandising rules — not a disconnected AI widget.
One catalog update. Two customer answers.
Bolt a search plugin onto Shopify — Algolia, Searchspring, Klevu, Searchanise, Boost — and you haven't added one integration; you've added a sync chain. The plugin's index is a copy, not the record, so data has to hop across sync boundaries to reach it: into Shopify, shipped out to the indexer, augmented by a side process for the data the platform can't hold natively, and finally into the plugin's index. Every hop is a place to lag or break.
The alternative is an owned discovery model built directly from the systems that are authoritative for product content, price, inventory, fitment, and behavior. Search, browse, product pages, merchandising, and AI experiences use that same serving model. The transactional systems remain authoritative; the fragmented plugin copies disappear. That is an architectural change, not a tuning win.
A product's price drops from $10.99 to $9.99 and it sells out — one change to the record. The product page reads the platform, so it shows $9.99 · out of stock right away. Search, autocomplete, and the category pages read the index, which hasn't caught up — so they still show $10.99 · in stock. Same product, same second, two answers. That gap isn't a bug to be fixed; it's the width of the sync window, and the window is always open.
The shopper filters to an in-stock item at one price and lands on a page telling them it's gone at another. Search and category render as client-side JavaScript injected into the theme; the product page never moved. The split shows up in configuration too — on the leading integration, search-page facets and category-page facets live in separate configs that don't sync — and some updates never arrive at all, since a variant-level metafield change doesn't fire the product-update webhook the plugin relies on. None of this is misconfiguration; it's the architecture working as designed.
Architecture confirmed against Algolia's published Shopify documentation — the reference implementation for the category. Searchspring, Klevu, Searchanise and Boost follow the same shape. All five run on Shopify; Boost is Shopify-only.
One authoritative update → every discovery surface shows $9.99 · out of stock, near-instantly.
Every plugin in the category — Algolia, Searchspring, Klevu, Searchanise, Boost — asks the merchant to operate a second discovery model inside someone else's product and keep it synchronized forever. The owned-model approach creates one governed ingest and one serving layer for every discovery surface, while ERP, PIM, commerce, and operational systems remain authoritative for the facts they own.
Stop tuning the copies. Own the model that powers discovery.
A modern, platform-less foundation for agentic commerce•
SaaS commerce platforms are proven and convenient. But as brands grow, catalog complexity increases, and competition shifts toward the new AI era, many teams get handcuffed by their platform:
Built on 20 Years of Enterprise Experience
For the past two decades, we’ve delivered large-scale e-commerce and enterprise CMS systems — long before the term “headless commerce” existed. 2 truths and a lie! Truth: You need a modern web stack to compete. We advocate for Next.js. Truth: You need a tremendously flexible, scalable, and performant backend based on systems capable of semantic and vector searching and RAG, to process all available AI signals. Lie: The leading e-commerce platforms can provide this. Search engines drive these features, so we build on top of that. Keeping it simple and eliminating many API layers.
The AI Discovery Stack for Modern Commerce•
We build the retrieval and storefront foundation brands need to compete on search-led conversion and AI-native shopping journeys.
Next.js Performance Storefront Layer
High-performance, AEO-first storefront architecture built for scale — with full flexibility beyond themes and app constraints.
- Sub-second storefront performance
- Composable UX journeys
- Modern frontend longevity
- AI Guided adaptations
ElasticSearch Discovery & Merchandising Engine
Enterprise-grade retrieval, ranking control, and high-SKU filtering. Search becomes a conversion system — not a utility box.
- Hybrid keyword + semantic search
- Advanced facets + filtering at scale
- Merchandising-driven relevance tuning
- Automagic Variants
Agent-Ready RAG Foundations
Vector + metadata retrieval pipelines that enable conversational discovery, guided selling workflows, and future agentic commerce automation.
- RAG-ready product knowledge retrieval
- Conversational shopping experiences
- Autonomous merchandising foundations
- Extndable Signal Sources (sales, customer service, web analytics ...)
Platform Extensions vs Discovery Ownership•
Most commerce agencies optimize within platform boundaries. We build beyond them — so you own discovery, retrieval, and AI readiness.
Start with a Search & Discovery Audit•
A two-week technical assessment that quantifies discovery upside and delivers a prioritized roadmap for Elastic/OpenSearch-powered retrieval, Next.js performance, and agent-ready foundations.
Build the AI Discovery Infrastructure SaaS Platforms Can't Provide Natively
The future of ecommerce will be search-led, AI-driven, and agent-powered. Let’s architect the retrieval foundation that enables it.