AI That Drives Conversion
and Retention
We build AI systems for online retailers and marketplaces — personalization engines, customer service automation, and demand intelligence that turn behavioral data into measurable revenue impact.
Why AI Has the Highest ROI Density in Ecommerce
Ecommerce generates more behavioral data per transaction than almost any other industry — clicks, searches, cart additions, abandonment patterns, return reasons, support interactions. Most of this data is collected but not acted on in any meaningful way. AI closes that gap between data collection and data-driven decision making.
Personalization is table stakes, but most implementations are shallow. "Customers who bought X also bought Y" is 15-year-old logic. Real personalization means understanding intent — why someone is browsing, what problem they are trying to solve, where they are in the buying journey — and adapting the entire experience accordingly. LLMs make this kind of deep personalization technically feasible for the first time.
Customer service is the largest hidden cost in ecommerce. Order status inquiries, return requests, product questions, delivery issues — the same questions come in thousands of times a day. AI agents can handle the majority of these interactions end-to-end, with seamless escalation to human agents for complex cases. The impact on cost-to-serve is immediate and significant.
Demand forecasting directly impacts margin. Overstocking ties up capital and creates markdown pressure. Understocking loses sales and damages customer trust. AI-driven demand forecasting that accounts for seasonality, promotions, competitor activity, and external factors can meaningfully improve inventory efficiency — which flows straight to the bottom line.
Speed is revenue. In ecommerce, the difference between a 200ms search response and a 2-second one is measurable in conversion rate. AI systems need to be fast, not just smart. This is an engineering challenge as much as a modeling challenge, and it requires teams that understand both sides.
Outcome Metrics We Look For
What We Build
Personalization Engines
Intent-driven product recommendations, search personalization, and dynamic content that adapts to each customer's behavior and context. Built on real behavioral data, evaluated against real conversion metrics.
Customer Service AI
Intelligent agents that handle order inquiries, returns, and common issues automatically across all channels. Built with seamless handoff to human agents for complex cases, available around the clock.
Demand Forecasting
AI-powered inventory optimization that predicts demand patterns by accounting for seasonality, promotions, competitor activity, and external signals. Integrated with supply chain systems for end-to-end intelligence.
Semantic Search & Discovery
Search systems that understand natural language queries and buyer intent, not just keyword matching. Faster, more relevant results that directly impact conversion rates and average order values.
How We Work With Ecommerce Companies
The engagement starts with data — understanding what behavioral signals exist, how they are structured, and where the gaps are between data collection and data-driven action. In ecommerce, the data is typically abundant but underutilized. The first step is identifying which data actually predicts the outcomes that matter: conversion, retention, and lifetime value.
Implementation is built around A/B testing from the start. Every AI feature is deployed alongside a control group, and impact is measured in revenue terms, not just model accuracy terms. The question is never "does the model work" but rather "does the model make money." This commercial focus shapes every architecture and prioritization decision.
Performance is measured continuously against the metrics that were agreed on at the start — conversion rate lift, cost-per-serve reduction, inventory efficiency improvement, and customer satisfaction scores. The engagement evolves based on what the data shows is driving value, with the flexibility to reprioritize as results come in.