Service

Product Development

End-to-end product development for founders and product teams: framing the problem, shipping a credible first version, then iterating on evidence rather than opinion.

Core stack
TypeScript · React · Astro · Node.js · PostgreSQL

Build the thing that answers the question

Most failed products are not badly engineered. They are precisely engineered answers to a question nobody asked. We spend the first part of every product engagement narrowing down what must be true for the product to work, then build the smallest system that tests it.

Phases we work in

  • Frame. Users, jobs, constraints, commercial model, competition, riskiest assumption.
  • Prove. A prototype or proof of concept aimed squarely at that assumption.
  • Ship. An MVP with real production quality — auth, payments, monitoring, backups.
  • Learn. Instrumentation, funnels, qualitative feedback, and a prioritised next increment.

What you get that a staffing vendor will not give you

Opinions. We will tell you when a feature is not worth building, when a roadmap is over-committed, and when the cheapest path forward is to change the process rather than the software.

Case studies

Where this work shows up

Problem, constraint, decision, consequence. No vanity metrics, no logos we are not allowed to name.

All case studies

Sample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.

saas2026
  • Python
  • TypeScript
  • LLM integrations

01B2B SaaS company with a large support operation (sample scenario)

AI-Assisted Operations Platform

Document intake and support triage moved to a draft-first AI workflow with human approval, measured against an evaluation set built from two years of historical cases.

  • Routine classification and extraction handled as drafts, with review rather than authoring
  • Quality tracked as a published accuracy metric per task type, not an impression
  • Python
  • TypeScript
  • LLM integrations
  • RAG
  • Vector databases
travel and hospitality2025
  • Kotlin
  • Spring Boot
  • PostgreSQL

02Multi-property hospitality group (sample scenario)

Enterprise Booking Platform

A booking platform serving multiple properties and channels, rebuilt around a single availability model and a back-office console operators actually wanted to use.

  • Overbooking incidents eliminated in the peak season following launch
  • Channel synchronisation latency reduced from minutes to seconds
  • Kotlin
  • Spring Boot
  • PostgreSQL
  • Redis
  • Kafka

FAQ

Product Development — common questions

01What is the smallest sensible engagement?

A two to four week proof of concept that resolves the single riskiest assumption — technical feasibility, integration reality or user behaviour.

Start a conversation

Need this kind of work?Start here.

Tell us what you are trying to build, fix or decide. If we are not the right team for it, we will say so and point you somewhere better.

Typical first step: a 30-minute call, then a short written assessment.