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Construction · Platform

M2 — a construction-equipment rental platform. We did design and development, with Digital Technology Group.

Together with Digital Technology Group we shipped M2. ZNN owned design and development: the site, the catalog, availability, the booking flow. DTG was the partner on the project. Not a brochure of yellow iron.

M2 2025 4 min read
M2 case study
by ZNN
Design
by ZNN
Build
DTG
With

Iron, not a landing

M2 rents construction equipment. The old habit is a PDF, a dispatcher, a WhatsApp. We designed and built a platform: the fleet is a catalog, dates are availability, a request is a record.

What we did

ZNN took design and development. Screens, IA, the public site, the booking flow, the ops view. A contractor picks a class, a period, a site. The system shows what can actually go.

  • Product design: catalog, request, confirm
  • Engineering: site, availability, a request that lands in ops
  • A public site that is the same product, not a second brochure

With Digital Technology Group

The project was joint. Digital Technology Group was the partner. Design, development and the rental platform itself sat with ZNN. Names of fleet owners and contract figures stay with M2.

Technologies

  • TypeScript
  • Astro
  • Postgres

Outcomes

  • Design and front-to-back development on ZNN
  • Catalog, availability and booking in one product
  • Delivered jointly with Digital Technology Group
All cases

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The case for boring web stacks

TypeScript, Postgres and edge deployment are not exciting — they are how you still iterate fast in year three without rewriting the platform.

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Shipping evals before prompts

Why we run regression suites on LLM outputs before every release — and how it changed our AI delivery cadence at ZNN.

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Mobile release trains that do not break

Fastlane, staged rollouts and feature flags — a practical playbook from eighteen App Store submissions without a Friday-night rollback.

Luxury · Volume eDTL

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The Brand Collector resells pre-owned luxury through a supplier network. Same class of problem as a catalogue feed — except each dump crossed 120,000 rows, and the CSV they hold sits above 169,000. Fastify stayed the orchestrator. The heavy compute moved to Go workers. About 60% faster on taxonomy, mapping and pattern-match at that volume.

iGaming · Platform

Casino operator · NDA

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Marketing · Fintech

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