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A unified intelligence layer for e-commerce and retail products. Built from the patterns we kept rebuilding across retail engagements. Plugs into your stack, ships AI features in weeks, yours to own outright.
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Lighthouse covers the AI capabilities a modern e-commerce product runs on. Each one is engineered around the way shopping data actually behaves. Catalog structure, browsing signal, checkout patterns, merchandising logic. Every model inside was built for e-commerce from the start. None were adapted to it.
Compose what your roadmap calls for. Integrate it with the stack you already run. Deploy it into your codebase, yours to own outright. What you launch this quarter sits in the same architecture as what you'll add next year. A product that competes on AI depth, not on feature checklists.
Reads every signal your platform already tracks, diagnoses what's underperforming against a benchmark, and surfaces ranked prescriptions a merchant can approve in one click.
Surfaces behavioral clusters that rule-based filters will never catch. Each segment ships with campaigns drafted against it, so marketing teams move from discovery to launch in one session.
A fine-tuned model that reads product similarity the way a merchandiser does. Coconut hair oil and coconut cooking oil belong on different shelves. Search, recommendations, and taxonomy all get sharper downstream.
Takes any anchor product, finds complementary matches against live inventory, and assembles bundles tuned for revenue. Drops into cart, checkout, or post-purchase flows and fills recommendations automatically.
A shopper uploads a photo and gets matching products back in milliseconds. Works on texture, silhouette, and context, which is how people actually shop in categories where words fall short.
Lighthouse is a living library of retail AI modules, each architected directly into your stack. New ones ship every few weeks.
Ionio's R&D ships new retail AI components continuously. The library keeps growing.
Each module architects directly into your existing codebase and infrastructure. It becomes part of your platform.
Every module ships with full source and documentation. Pull new ones in as priorities shift.
A focused engagement that ends with production-ready modules inside your codebase, maintained by your engineering team.
Every module below started as a pattern we saw repeat across live retail deployments. We hardened each one into its own component. Plug in what you need, leave what you don't.
A composable intelligence layer that reads every signal your platform already collects, diagnoses what's underperforming, benchmarks against your entire user base, and surfaces ranked prescriptions your users can approve in one click.
The gap between data and decision is where all the value lives. Prescriptive Intelligence closes it. Your platform stops being a reporting tool and starts telling users exactly what to do next.
A composable segmentation engine that sees who your customers actually are. It reads purchase behavior, browsing patterns, price sensitivity, and lifecycle stage to surface microsegments invisible to rule-based platforms, then auto-generates campaigns for each one.
Traditional segmentation sorts users into broad buckets. Microsegments asks a sharper question: who is this customer, what do they want, and when are they ready to hear from you?
A fine-tuned embedding model that reads product names the way a merchandiser would. It maps every SKU into a semantic space where similarity means actual category proximity, not keyword overlap.
Off-the-shelf models confuse "Coconut Hair Oil" with "Olive Cooking Oil" and pair "Dark Chocolate" with "Chocolate Body Lotion." Ours scores them at 0.16 and 0.11. The difference between a misclassification and a clean taxonomy is the model underneath.
A three-stage pipeline that takes any product in your catalog, generates complementary recommendations through an LLM, matches them against real inventory using embedding similarity, and assembles revenue-optimized bundles your team can ship instantly.
Static "frequently bought together" lists decay fast. Smart Bundling rebuilds itself from live catalog data and purchase signals, so every recommendation maps to a real SKU and every bundle stays relevant.
An image-first discovery engine that turns any photo into a product search query. It extracts deep visual features using SigLIP embeddings, maps them into a shared semantic space, and retrieves the closest matches across your entire catalog in milliseconds.
Text search breaks when users can't describe what they want. Visual search closes the gap. Your users snap a photo of a dress they saw on Instagram, upload it, and find exactly what they're looking for.
Quick concept breakdowns on some of the core Lighthouse components. What they do, why they exist, and how we built them.
Why off-the-shelf models misclassify retail products, and how we fine-tuned one that actually understands category proximity.
Letting customers find products by uploading a photo instead of typing keywords. How it works under the hood.
An AI bundle creator for Shopify that pairs products using purchase graph data, the same way Amazon does it.
Lighthouse is a library, not a platform. Each component was forged in live retail deployments and hardened into standalone, composable modules. You pick what you need. We build it into your stack. The code, the models, the IP transfers to you permanently.
Submit your platform URL. We will assess which Lighthouse components would create the most value and share a detailed breakdown.