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.
Most enterprise AI products bind you to a vendor for life. Lighthouse is engineered the other direction.
You pay once. The license, source, and IP transfer to your team. Cost is bounded by the engagement, not by how long you keep using it.
The code runs entirely inside your own infrastructure. There's no Ionio service standing between you and your users, and nothing to depend on after handover.
Every module ships with complete source and documentation. Your engineers can read it, modify it, and extend it on their own timelines.
Each module installs directly into your codebase, runs on your infrastructure, and is maintained by your engineers after handover. Watch a deployment unfold.
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?
Off-the-shelf embedding models score "coconut hair oil" and "olive cooking oil" at 0.85. They both contain "oil," so the math agrees. Your shoppers don't.
We fine-tuned an embedding model on retail catalogs until it learned the difference. Same pair: 0.16. "Dark chocolate" and "chocolate body lotion": 0.11. Suddenly your search, recommendations, and category trees stop confusing kitchens with bathrooms.
"Frequently bought together" lists are usually static, slightly stale, and built off whatever popped up in last quarter's purchase data. They look intelligent. They aren't.
Smart Bundling pairs an LLM with your live catalog. The model proposes complements for any anchor product. Embeddings then check each suggestion against what you actually have in stock. What ships is a bundle that maps to real SKUs, not a placeholder waiting for a backorder email.
A shopper sees a dress on Instagram and wants it. Typing "floral mid-length wrap dress with three-quarter sleeves" into your search bar isn't going to happen. They'll bounce.
Visual Search lets them upload the photo instead. SigLIP embeddings turn the image into a vector, the vector hits your catalog index, and the top matches come back in milliseconds. The shopper finds what they came for, in the categories where words give up.
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.
Drop your platform URL. We'll study what you've built and come back within 48 hours with a tailored read on which modules create the most value, how they'd architect into your stack, and what shipping them looks like in practice.