ECOMMERCE & RETAIL · AI & MACHINE LEARNING

AI & Machine Learning for Ecommerce & Retail

We help Ecommerce & Retail teams put predictive models into production. Predictive LTV and propensity for smarter bidding and segmentation.

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ROAS — Gold Collagen

10%

CVR — Science in Sport

8–12%

ER — Northtac

An ads agency and an email agency will never share a customer view.

(01) THE APPROACH

Why ai & machine learning for Ecommerce & Retail is a different job

Predictive LTV and propensity for smarter bidding and segmentation.

One warehouse-native architecture where the same enriched data feeds paid, lifecycle and the storefront. Server-side tracking and identity resolution produce a single customer view in BigQuery. That view feeds enriched segments back into the ad platforms, and the same data drives the email and SMS flows — so paid and lifecycle compound instead of competing.

Artificial intelligence only counts when it runs in production, changes what your team does next, and shows up in revenue you can measure against a clear baseline. Everything else is expensive theater.

(02) WHAT'S INCLUDED

The build.

Predictive Models & Custom Solutions

Scoring and recommendations, plus any model where a data problem is attached to revenue. We train models on your closed-won history: lead scoring, churn propensity, lifetime value, and demand forecasting, plus catalog-trained recommendations that raise order value and repeat purchase. If it is a data problem attached to revenue, we have built it: fraud detection, pricing optimization, anomaly detection. We backtest every use case against your own history before you pay.

Generative AI & Private LLMs

Your own language models, grounded in your data, running in your cloud. We deploy internal LLMs and retrieval pipelines over your documents, knowledge base, and warehouse, powering search, drafting, and answers your team can trust. Your data stays inside your environment, behind your access controls, and nothing is sent to public model providers, so the whole system runs in your own cloud.

Model Customization & Fine-Tuning

RAG, fine-tuning, or both, chosen on the evidence and tuned to your domain. We tune models to your data and domain: retrieval-augmented generation when your knowledge changes daily, fine-tuning when a model must internalize your tone, taxonomy, and judgment, and often both. We prove a well-grounded base model cannot already clear your accuracy bar before you pay to fine-tune, and every tuned model is versioned and scored against a held-out set, so accuracy is a number you can see.

Language, Vision & Audio AI

Sentiment, transcription, and classification across text, media, and calls. We build pipelines that classify tickets, reviews, calls, and leads, score sentiment, and route what matters to the right owner. Speech-to-text, video understanding, and image analysis turn media libraries and feedback into searchable, reportable data, so unstructured text, audio, and video become structured signal in your dashboards.

(03) PROOF

4xROAS

Gold Collagen

beauty that converts

+50% DISTRIBUTION DEALS

15xROAS

Cirque du Soleil — Dralion

a full house, every night

88,000 TICKETS SOLD · 22 EVENTS

5xROI

Northtac

an engaged audience, built

8–12% ER · 200+ AFFILIATES

TRUSTED BY 80+ COMPANIES

(04) HOW WE WORK

A plan, from day one.

Map the Money

We audit your data and rank every candidate use case by revenue at stake and by how ready the data actually is to support it. You get a scored roadmap with one clear first build. Data gaps that would sink a model get named here, with a plan to fix them, before anyone writes training code.

Fix the Foundation

Modeling on broken data produces confident, expensive nonsense, so we build the pipelines, labels, and clean training sets the first use case needs. Where tracking or identity is the real blocker, we fix that first rather than paper over it with a fragile model. This is the step most vendors skip, and the one most failed projects trace back to.

Prove It Offline

Before anything touches production, we backtest the model against your own history and set a baseline it has to beat. If it cannot win against your current approach, it does not ship. You see the honest expected lift before you commit to deployment.

Ship to Production

Models deploy inside your tech stack so scores, segments, and recommendations show up where your team already works. We monitor and review anything that touches a customer or a budget from day one.

(05) WHAT YOU GET

Deliverables that ship.

Sentiment and classification pipelines that turn tickets, reviews, and calls into structured, reportable insight

A recommendation engine serving your site, email, and lifecycle flows from your own catalog data

Model documentation, drift monitoring, and retraining pipelines your team owns outright

Production lead, churn, and LTV scores synced to your CRM, updated automatically and visible in every sales view

Propensity-built audiences pushed to social and search platforms on a continuous refresh schedule

(06) FAQ

Questions, answered.

Do you have AI & machine learning experience with Ecommerce & Retail companies?+

Yes. Our published work includes Gold Collagen at 4x ROAS. We run AI & machine learning inside a full-stack engagement built for High-Volume Ecommerce & Retail, so it is wired to the same measurement layer, CRM and first-party data the rest of your growth runs on — which is what makes the numbers defensible.

We already run Shopify plus a dozen apps.+

Is headless overkill for us?+

Who owns the models, and does our data leave our environment?+

Is our data ready for artificial intelligence?+

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