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Vikrama.
E-commerce

Recommendation engines, demand forecasting, and commerce platforms that convert.

We build the systems behind personalization, pricing, and operational efficiency.

E-commerce at scale is a data problem disguised as a retail problem. We build recommendation engines, demand forecasting models, dynamic pricing systems, and commerce platforms that turn browsing into buying.

Why AI in E-commerce is different.

E-commerce generates more behavioral data per user than almost any other industry. Every click, scroll, hover, and cart action is a signal. 35% of Amazon's revenue comes from its recommendation engine. Most D2C brands are still showing "customers also bought" widgets that haven't changed since 2016.

The margin pressure is real. Customer acquisition costs have increased 60% over the past five years while average order values stayed flat. The brands that win are not spending more on ads. They are extracting more value from every visit through dynamic pricing, personalized merchandising, and demand forecasting that actually reduces inventory waste.

The difference between a 2% and 4% conversion rate is not better photography. It is search relevance, real-time personalization, and a checkout flow that adapts to the customer. That is an engineering problem, and we solve it.

The Problems That Slow You Down

01

Personalization that doesn't personalize

Your "recommended for you" section shows the same products to everyone. Conversion rates plateau because every customer gets the same experience.

02

Inventory forecasting by spreadsheet

Overstock on slow SKUs, stockouts on fast ones. Demand planning runs on last year's numbers and gut feel.

03

Omnichannel is a PowerPoint slide, not a reality

Website, app, marketplace, retail: inventory doesn't sync, prices don't match, and customer history doesn't follow.

04

Conversion optimization is A/B testing button colors

Real conversion gains come from search relevance, checkout friction, and personalized merchandising, not font size experiments.

These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.

What We Build in E-commerce

Return Fraud Detection

Head of Operations
Problem

30% of returns are wardrobing/abuse. 15-20% fraud rate, $1M+/year for $50M GMV brands.

System

XGBoost on return patterns (frequency, categories, velocity) + image CNN for product condition matching.

Fraud losses cut 40-60%, return processing 50% faster.

Demand Forecasting

VP of Supply Chain
Problem

Manual SKU forecasts cause 25% stockouts/30% overstock for 5K SKUs; $2M/year excess inventory.

System

LSTM time-series on sales, seasonality, marketing spend, ERP/Shopify API integration for auto-replenishment.

Accuracy up 50%, stockouts/overstock down 20-30%.

Dynamic Pricing Engine

Director of Revenue Operations
Problem

Static pricing misses 15% revenue on flash sales/competitor drops; manual adjustments lag hours on 1M visits/day.

System

Reinforcement learning on competitor scrapes, demand elasticity, inventory levels, with real-time API to storefront.

AOV up 12-18%, revenue +15-25%.

Abandoned Cart Recovery Agent

Head of Growth Marketing
Problem

70% carts abandoned; generic email triggers lose $3M quarterly.

System

LLM agent crafts personalized nudges from cart data + browse history, multi-channel (email/SMS).

Recovery rate 20-30% higher, CAC down 25%.

Visual Search & Discovery

VP of Product
Problem

Text search finds under 30% relevant items; conversion drops 40% on mobile for 10K+ image catalogs.

System

CLIP multimodal embeddings on catalog images/uploads, vector DB integrated to search bar.

Conversion +25%, pages viewed +12%.

Customer Segmentation

Director of Customer Insights
Problem

Rule-based segments miss 40% micro-niche buyers; CLV uplift capped at 10%.

System

Graph neural nets on RFM + behavioral graphs, LLM generates bundles/emails.

CLV +25-40%, conversion 15-25% higher.

Supplier Lead Time Prediction

Supply Chain Director
Problem

Vendor delays hit 25% of orders; air freight at 5x cost for perishables ($500K/year).

System

Prophet forecasting on historical shipments, disruptions, weather, alerting the procurement dashboard.

On-time delivery +35%, freight costs down 20%.

Review Fraud Detection

Head of Marketplace Operations
Problem

15-20% fake reviews erode trust, boost returns 10%, hurt SEO.

System

BERT NLP on sentiment + network analysis of reviewer graphs, auto-flagging for moderation.

80% fake review detection accuracy, returns down 15%.

Building the system is half the job. Growing the business around it is the other half.

How We Grow E-commerce Brands

AEO for Product Recommendations

Head of Ecom Growth
Problem

D2C loses 45% of "best moisturizer for dry skin" to ChatGPT/Perplexity without links.

Build

Schema-rich PDPs with UGC/reviews, FAQ clusters, Perplexity monitoring to Shopify carts.

35% AI citation increase, 25% organic traffic growth in 4 months.

Meta Ads + HubSpot Nurture for Repeat Buyers

Director of Performance Marketing
Problem

Meta CPL $15-25 with under 8% ROAS; 70% cart abandoners ignored.

Build

Meta dynamic ads to HubSpot cart abandonment flows, RFM scoring for VIP nurture.

ROAS 2.5x to 4.2, CAC down 28% to $18.

SEO + AEO for Category Pages

SEO Manager
Problem

"sustainable activewear" ranks #5; AI summaries steal 25% traffic.

Build

Category hubs with schema/products, UGC/backlinks, AEO for Gemini.

45% organic traffic in 6 months, 18% sales lift.

TikTok/Instagram Content Factory

Social Media Director
Problem

D2C posts 3x/week; under 2% engagement, missing viral loops.

Build

AI UGC clips (try-ons, hauls), hooks/hashtags, scheduled via Later, shop links.

5x engagement to 10%, 20% revenue from social.

HubSpot Abandoned Cart + Loyalty Automation

Ecom Operations Lead
Problem

65% carts abandoned; no segmentation delays repeat purchases.

Build

HubSpot cart/email workflows, loyalty RFM scoring, SMS upsells via Klaviyo sync.

Cart recovery 25%, LTV 30% uplift.

Frequently asked questions

AI moves fast. Stay ahead.

No spam. One actionable email per week on AI systems and growth.

Your catalog has 50,000 SKUs. Each one should sell itself.

Start with a conversion and data audit. We'll identify the highest-ROI opportunities in your commerce stack.