[ Technology practice ]

From bold idea to production-grade product.

From architecture to App Store, one team delivers web, native mobile, cloud, and applied AI - without the handoff gaps.

+ product engine+ 4 surfaces+ 1 intelligence layer
+ Product surfaces
4

Web, iOS, Android, and intelligent interfaces, designed as one connected experience.

+ AI core
1

A shared intelligence layer that helps every product understand, adapt, and improve.

+ Products in motion

We don't stop at ideas. We turn emerging technology into products people can actually use.

+ Build loop
24/7

Imagine, prototype, ship, learn, repeat. The product is never finished, only getting smarter.

What we make when the client is us

The practice funds its own products. Each one starts as a tool we needed and could not buy.

Dexter

Live

Chat, tasks, tickets, wiki and calendar as views over one object graph. Web, iOS and Android from a single codebase.

Meet Dexter

Data Portrait

In development

A living portrait of an organisation, drawn from the data it already produces rather than the deck it wrote about itself.

Unannounced

Slot 03

Something is being prototyped here. It gets a name when it survives contact with real usage.

[ A brief overview of Dexter ]

Your team's chat, work and time, connected in one place

Dexter connects chat, projects, tickets and calendar in one workspace. A message becomes a task, the task moves the sprint, and the deadline lands on everyone's calendar.

One data layer

Chat, tasks, files and calendar are views over the same object graph, not five products holding hands through integrations.

workspace
  object_graph
    message   -> chat
    task      -> board, sprint
    ticket    -> queue
    document  -> wiki
    event     -> calendar

AI as a material

Summaries render where the unread state lives. Commitments made in conversation become proposed tasks, with no chat window required.

It proposes, you dispose

Nothing auto-assigns silently. Every generated artifact is reviewable, and every summary links back to its source thread.

Web, iOS and Android

One shared UI and one backend across every platform your team works on, built with Kotlin Multiplatform and Compose.

Everything between the idea and the release

We are a product engineering team, not a staffing desk. The same people who draw the architecture write the code, ship the build and hold the pager.

Product engineering

Production web applications in React and TypeScript. Real-time data, role-aware interfaces, and design systems built as tokens so the look survives the port to native.

React 19
TypeScript
Vite
Tailwind
Motion

Cross-platform mobile

Native iOS and Android, plus tablet, from a single Kotlin Multiplatform codebase. One shared UI, one backend, one design system across every device.

Kotlin Multiplatform
Compose
Material 3
Ktor

Applied AI

AI treated as a material, not a chatbot in the corner. Summarisation, extraction and drafting wired into the interface itself, proposing rather than committing, always traceable to a source.

Claude
OpenAI
Gemini
Retrieval
Evals

Cloud, backend and data

Serverless backends on Firebase and Google Cloud: authentication, Firestore data modelling, Cloud Functions, storage, and migration paths for when the schema changes under live traffic.

Firebase
Firestore
Cloud Functions
GCP

Security and access engineering

Permission models resolved once at the data layer, not reconciled at four edges. Security rules, workspace isolation, server-side secret handling, and written threat models you can audit.

Security rules
RBAC
SSO
Threat models

Architecture and strategy

High and low level design, technical due diligence, and the unglamorous decisions that decide whether year two is cheap or expensive. Documented before implementation.

HLD / LLD
System design
Due diligence

The people who draw it are the people who ship it

One team from architecture through handover, which is why the data model in the diagram is the data model in production.

[ In production ]

Boring where it should be. Sharp where it counts.

Everything here runs in something we ship. We pick tools that let a small team ship like a large one, and that a client's own engineers can pick up without a translator.

Better with every iteration

Four phases, run in the open. You see the architecture before the invoice, and a working build long before the launch date.

01
Discovery
02
Define
03
Build
04
Harden
01

Understand the actual problem

Audit the current stack, the workflows, and where the hours really go. We write down what we found, including the parts that argue against building anything.

artefact findings memo

02

Architecture, on paper first

High and low level design, data model, permission model, and a scope you can price. Decisions are documented so year two is not archaeology.

artefact HLD / LLD plus priced scope

03

Production increments

Shipped in slices you can use, not milestones you can only read about. Design system, then screens, then the long tail, reviewed at every step.

artefact working builds on a schedule

04

Launch and keep it standing

Security rules, load behaviour, observability, and a handover your own team can actually own. Then we iterate on what real usage reveals.

artefact runbook plus handover session

Less switching. Less chasing. More work that ships.

Tell us what you are building, or what is currently held together with integrations and goodwill. We come back with an architecture, a scope and an honest opinion.

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