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A collection of e-commerce specific, composable AI components engineered to ship intelligent retail software at velocity. Pre-built, production-tested AI modules that each solve a distinct retail problem inside your existing product, so your engineering team ships in weeks instead of quarters.
Book a CallThe default way to ship enterprise AI is one large, bespoke build. It takes months, it solves one problem, and when the next use case shows up, everything starts from scratch.
Lighthouse flips that. Each component is an independent AI module purpose-built for e-commerce. They plug into your existing stack on their own. Stack them, swap them, deploy them separately. The architecture stays flexible because nothing was fused together to begin with.
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.