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Digital Commerce

Sell more. Stock smarter. Turn browsers into repeat buyers.

We build product discovery engines, inventory optimization systems, loyalty platforms, and conversational shopping assistants for retailers and D2C brands. The software that increases AOV, reduces stockouts, and turns one-time buyers into repeat customers.

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85%

Traffic growth

28%

Conversion lift

Overview

Broken discovery costs you sales every hour

E-commerce software development at 1Raft focuses on the three areas that most directly impact revenue: product discovery, pricing, and inventory. We bring patterns from 100+ products across adjacent industries to build recommendation engines, search optimization, demand forecasting, and shopping assistants - each engineered to drive measurable revenue lift.

Most commerce platforms treat search as keyword matching and recommendations as 'customers also bought.' The result: 70% of product catalogs get zero visibility, customers can't find what they want, and conversion rates plateau at 2-3%.

Our search systems understand natural language intent ('summer dress for a beach wedding'), recommendation engines increase AOV by 18-25%, and inventory systems predict demand 6 weeks ahead with 92% accuracy.

Every product we build connects to existing commerce platforms - Shopify, BigCommerce, Magento, custom headless stacks. We work with your catalog, your data, and your customer base. No rip-and-replace required.

Experience Signal

1Raft builds product discovery engines, inventory systems, loyalty platforms, and conversational shopping assistants for D2C brands, marketplaces, and omnichannel retailers. Our engineering draws on patterns validated across 100+ products in adjacent industries.

85%

Traffic growth

28%

Conversion lift

Industry Pain Points

What's broken in digital commerce

01

Site search returns irrelevant results for 40% of queries because it matches keywords, not intent

02

Product recommendations show 'more of the same' instead of cross-sell and upsell opportunities that increase basket size

03

Inventory planning uses last year's sales data and gut feel, leading to $200K+ in annual deadstock and frequent stockouts on popular items

04

Loyalty programs have 15-20% active rates because points systems don't create emotional connection or drive behavior change

05

Customer service handles the same 30 questions about sizing, shipping, and returns - pulling agents away from revenue-generating conversations

06

Cart abandonment rates sit at 65-75% with no intelligent recovery beyond a generic discount email

Solutions

Problems we solve in digital commerce

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Product Discovery and Search

Natural language search that understands intent, not just keywords. 'Waterproof hiking boots for wide feet under $150' returns relevant results. Visual search, filters, and facets adapt to each query.

02

Personalized Recommendations Engine

Goes beyond 'customers also bought' with contextual recommendations based on browse history, purchase patterns, seasonal trends, and real-time cart contents. Increases AOV through genuine cross-sell and upsell.

03

Demand Forecasting and Inventory Optimization

Predicts demand at the SKU level using sales velocity, marketing calendar, weather, trends, and competitor signals. Automates reorder points and quantities. Reduces deadstock and stockouts simultaneously.

04

Conversational Commerce Assistant

AI shopping assistant on web, WhatsApp, and SMS that helps customers find products, answers sizing and shipping questions, processes returns, and recommends alternatives. Handles 70%+ of pre-purchase inquiries.

05

Intelligent Loyalty and Retention Engine

Moves beyond points and tiers. Tracks behavior, predicts churn risk, and triggers personalized win-back campaigns. Rewards actions that drive lifetime value - referrals, reviews, repeat purchases - not just transactions.

06

Cart Recovery and Conversion Optimization

AI-driven abandoned cart recovery with personalized incentives based on cart contents, customer value, and price sensitivity. Multi-channel follow-up - email, SMS, push - timed for maximum recovery.

Use Cases

Real-world use cases

Product Discovery for a Fashion Marketplace

Problem

A fashion marketplace with 180K SKUs had a site search exit rate of 34%. Customers typed queries like 'cocktail dress for fall wedding' and got keyword-matched results that missed intent.

What we built

We built a semantic search engine that understands natural language product queries, style intent, and occasion context. Added visual similarity search and dynamic filtering based on query type.

Result

Search exit rate dropped to 12%. Search-to-purchase conversion increased 42%. Average session depth grew 2.3x for search users. Product catalog coverage improved from 31% to 78% of SKUs receiving traffic.

Demand Forecasting for a Consumer Goods Brand

Problem

A D2C brand with 400 SKUs across 3 warehouses held $1.2M in deadstock while losing $680K annually from stockouts on their top 50 products.

What we built

We built a demand forecasting system using sales velocity, marketing spend data, seasonality, and external signals. Automated reorder recommendations with safety stock optimization per warehouse.

Result

Deadstock reduced 44% in 6 months. Stockout incidents on top products dropped 71%. Inventory carrying costs decreased $340K annually. Warehouse utilization improved 22%.

Conversational Commerce for a Beauty Brand

Problem

A beauty brand received 3,200+ customer inquiries per week. 68% were pre-purchase questions about ingredients, shade matching, and product compatibility. Customer service team of 8 was overwhelmed.

What we built

We built an AI shopping assistant trained on the full product catalog, ingredient database, and shade matching logic. Available on web chat and WhatsApp with order tracking and returns handling.

Result

AI handled 74% of inquiries without human handoff. Customer service team refocused on VIP customers and complex issues. Conversion rate from chat interactions hit 18% - 3x the site average.

Our Approach

How we approach digital commerce projects

1
Phase 1· Weeks 1-2

Commerce Audit and Conversion Analysis

We analyze your catalog, customer behavior, conversion funnels, and inventory performance. We identify the highest-ROI opportunities - where you're losing sales, margin, or customers.

Deliverables

  • Conversion funnel analysis with drop-off quantification
  • Catalog performance audit - visibility, search coverage, and recommendation effectiveness
  • Revenue opportunity map ranked by estimated impact and implementation speed
2
Phase 2· Weeks 3-4

Product Design and Platform Integration Planning

We design the product with your commerce, merchandising, and operations teams. Every integration point - commerce platform, ERP, warehouse, payment - is mapped before build.

Deliverables

  • Product design validated by merchandising and ops leads
  • Integration specifications for commerce platform and data sources
  • Data architecture for catalog, customer, and inventory signals
3
Phase 3· Weeks 5-10

Build, Integrate, and A/B Test

We build in sprints with continuous integration to your live store. New features launch behind A/B tests so every change proves its impact before full rollout.

Deliverables

  • Working product integrated with live commerce platform
  • A/B test results validating impact on target metrics
  • Performance benchmarks for search, recommendations, and conversion
4
Phase 4· Weeks 11-12

Full Rollout and Revenue Optimization

We roll out fully with merchandising team trained on configuration and optimization. AI models improve continuously from customer behavior data.

Deliverables

  • Full rollout with merchandising team self-sufficient
  • Revenue impact dashboard tracking AOV, conversion, and LTV
  • Continuous optimization plan tied to quarterly revenue targets

Outcomes

Measurable outcomes

18-30% increase in average order value through AI-powered recommendations and upsell
25-45% improvement in search-to-purchase conversion through intent-based product discovery
35-50% reduction in deadstock through demand forecasting and automated reorder optimization
60-75% of pre-purchase inquiries handled by AI shopping assistant without human intervention
15-25% improvement in customer retention through behavior-driven loyalty and win-back campaigns
20-35% increase in cart recovery rate through personalized, multi-channel follow-up

Pattern Transfer

1Raft first built recommendation algorithms for a media streaming client optimizing content discovery. That same collaborative filtering approach - user behavior patterns predicting next best action - powers our product recommendation engines for commerce clients.

Services

Services for digital commerce

Frequently asked questions

Projects range from $40K-$180K. A product search and recommendation engine starts around $40K. A full commerce platform with inventory forecasting, conversational commerce, and loyalty runs $100K-$180K. We provide a fixed estimate after a strategy session.

Next Step

Every percentage point of conversion rate is worth millions in annual revenue.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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View commerce case studies