Industry

E-Commerce

Commerce experiences that convert

I help e-commerce businesses build fast, reliable, and conversion-optimized shopping experiences. From headless commerce architectures to AI-powered personalization, I solve the technical challenges of selling online.

At a glance
Regulations
6 frameworks
KPIs tracked
6 core metrics
Reference stacks
5 patterns
Services
4 engagements
Case studies
1 published
Perspective

How I think about e-commerce

The architecture, the trade-offs, and where I push back on conventional wisdom.

E-commerce is one of the few corners of the web where engineering quality has a direct, measurable line to revenue. A 100ms improvement in LCP shows up in conversion. A flaky checkout loses you the whole basket. A miscounted inventory unit becomes a refund, a chargeback, and a 1-star review. I work with brands and platforms where the engineering bar is set by the P&L, not by an internal style guide.

Headless commerce became the default for a reason: the monolithic platforms are great for catalog management and operations, terrible for the kind of front-end iteration modern brands need. I build storefronts on Next.js backed by Shopify, BigCommerce, commercetools, or Saleor, with edge rendering on Vercel for global performance. The wins are real - sub-second LCP across geographies, double-digit conversion lifts on category pages, and a frontend team that can ship without the platform team's permission.

Peak traffic is where commerce architecture earns its paycheck. Black Friday, a celebrity drop, a Reddit hug of death - your steady-state traffic is irrelevant if the spike kills you. I design systems with explicit capacity plans, queue-backed checkout, Redis-backed inventory locks, and circuit breakers around every third-party. The goal is graceful degradation, not heroic recovery: if Stripe is degraded, you queue and retry; if search is down, you fall back to category browse.

Personalization is where AI is genuinely changing commerce, and where most teams are still doing it wrong. Static "customers also bought" lists were a 2010 solution. The current frontier is vector-based product retrieval tied to session intent, generative on-site search, and post-purchase email content tailored at the individual level. I build personalization stacks that respect user signals without burning your margins on compute - embedding-cache hierarchies, candidate generation in Postgres, reranking with smaller models.

Operations is the silent half of commerce. OMS, WMS, ERP integrations, returns, fraud screening, tax (Avalara, TaxJar), and chargebacks (Signifyd, Riskified). I help teams architect the back-of-house so the storefront feels magical and the warehouse stays sane. Idempotent webhooks, exactly-once order ingestion, and reconciliation jobs that catch the edge cases before finance does. See a speed-optimization case study or start a project.

Challenges

What teams struggle with

The recurring problems I see on e-commerce engagements.

  • 1Page speed and Core Web Vitals
  • 2Inventory and order management at scale
  • 3Peak traffic handling (Black Friday, flash sales)
  • 4Cart abandonment and conversion optimization
  • 5Multi-channel selling and integration
How I help

Capabilities I bring

Concrete engineering work that resolves the challenges on the left.

  • Headless commerce architecture
  • Performance optimization for conversion
  • AI-powered personalization and recommendations
  • Inventory and order management systems
  • Payment processing integration
Metrics

What teams measure

The KPIs leadership obsesses over in this sector. Most tie back to performance and architecture decisions made years before the dashboard was built.

01

Conversion rate

Sessions to orders - the master metric every storefront optimizes against.

02

AOV (Average Order Value)

Revenue per order; raised through bundling, cross-sell, and free-ship thresholds.

03

Core Web Vitals (LCP/INP/CLS)

Google's UX trio; LCP under 2.5s on mobile is a meaningful conversion threshold.

04

Cart abandonment rate

Industry baseline ~70%; a 1-point improvement is a major UX project.

05

Repeat purchase rate

Drives LTV and is the cleanest signal that the post-purchase experience is working.

06

Return / refund rate

Operational health metric, often gated by sizing tools and accurate PDP content.

Reference stacks

Stacks I see most often

Patterns I reach for first when scoping a e-commerceengagement. I don't pick technologies for novelty - read more about how I choose.

1

Next.js storefront on Vercel + Shopify Hydrogen or Storefront API

2

commercetools or Saleor headless backend, Algolia or Typesense for search

3

BigCommerce + custom React frontend for B2B / complex catalogs

4

Stripe + Adyen for global payments, Klarna/Afterpay BNPL, Signifyd for fraud

5

Klaviyo for lifecycle email, Segment + Snowflake + Hightouch for reverse-ETL

Technologies

Tools of the trade

The platforms and frameworks I lean on for e-commerce work.

Building for E-Commerce?

Let's discuss your specific challenges and how technology can help you ship safely, sleep well, and keep regulators happy.

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