Selected builds / 2026

Systems built from real operating constraints.

Three examples of Benjamin's approach: identify the costly bottleneck, design the smallest reliable system, ship it into live operations, then measure what changed.

Ecommerce / Product / Shopify

Roadshow POS

Eight years of checkout friction became a working tablet product in three days.

Problem

Public-site checkout was slow and venue Wi-Fi was unreliable. A clipboard-to-cashier workaround created queues and SKU errors. A custom overlay had remained too expensive to justify.

System

Benjamin built a tablet-first overlay on the Shopify Admin API with a custom product picker, payment-link generation, draft-order creation and real-time warehouse inventory.

Result

The system entered live use at the next show, removed SKU drift and reduced roadshow closing time by more than 50%.

What it shows

Technical product ownership grounded in years of operational context, with AI reducing build cost rather than replacing business judgment.

Read the build account on LinkedIn ↗

AI operations / Data / Automation

Operator OS

A fragmented reporting routine became an automated operating briefing.

Problem

Critical numbers lived across Shopify, Klaviyo, Meta and Google. Manual weekly assembly consumed Benjamin's best thinking time and still produced a stale picture.

System

A deterministic early-morning pipeline pulls source data, checks anomalies against rolling baselines and uses an AI layer to produce a structured 400-word briefing.

Result

Two hours of assembly became five minutes of analysis. The system now runs across two ventures as part of a broader agent-supported operating stack.

What it shows

Disciplined separation between deterministic business logic and AI judgment, designed around reliability, latency and decision quality.

Applied AI / Measurement / AEO

AI Visibility Snapshot

A scoring engine that measures whether AI models actually recommend a brand.

Problem

Brands can see their Google rankings but have no idea what ChatGPT, Claude, Perplexity or Gemini say when a buyer asks for a recommendation. No baseline means no way to prove any intervention works.

System

Benjamin built a scoring pipeline that runs a brand's real buyer queries across four AI models and grades the answers on seven dimensions, producing a repeatable visibility score.

Result

The engine has scored Singapore DTC brands across four models and now powers the assessment work at Firstpress.ai.

What it shows

Building the measurement instrument before selling the intervention.

Explore Firstpress.ai ↗