Product engineering for teams that ship on a deadline

Web and mobile applications, applied AI, data platforms, and the cloud they run on. Designed, built and operated by one senior team.

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Two-week fixed-scope discovery. Working software in your hands from the first sprint. Code, infrastructure and documentation handed over as we go. No lock-in.

Senior engineers only You own the repo Fixed scope, fixed price

The stack we build and operate with

See how we use it →

React

Next.js

TypeScript

Node.js

Python

Flutter

Swift

Kotlin

PostgreSQL

Kubernetes

Terraform

AWS

PyTorch

dbt

01 React / Next.js / React Native / Flutter

Product engineering. Web and mobile applications built to survive real users, real load, and the roadmap you haven't written yet.

Scope an application →

Typed end to end, tested in CI, deployed on every merge. We build the product and the delivery pipeline that keeps it moving after launch, then hand both to your team.

app/api/checkout/route.ts200 · 84ms

import { z } from 'zod'; import { db, stripe, logger } from '@/lib'; // validated at the edge, typed all the way down const Body = z.object({ cartId: z.string().uuid(), coupon: z.string().optional() }); export async function POST(req: Request) { const { cartId, coupon } = Body.parse(await req.json()); const cart = await db.cart.findOrThrow(cartId); const intent = await stripe.paymentIntents.create({ amount: total(cart, coupon), currency: 'usd', metadata: { cartId, tenant: cart.tenantId }, }); logger.info('checkout.intent.created', { cartId, ms: 84 }); return Response.json({ clientSecret: intent.client_secret }); }

→ Typed contracts between every layer

→ Automated tests on the paths that earn money

→ A preview deployment for every pull request

→ Handover docs written as the code is written

02 LLM applications / RAG / Agents / MLOps

Applied AI. Models wired into the workflow that actually makes you money, not a demo that impresses in a meeting.

Discuss an AI build →

Retrieval pipelines, evaluation harnesses, and guardrails. We measure answer quality before anything ships, and keep measuring it in production against a fixed test set.

eval/retrieval_quality.pyPASS · 412 cases

$ longan eval run --suite support-copilot --baseline v3.1 suite support-copilot cases 412 baseline v3.1 ───────────────────────────────────────────── groundedness 0.96 ▲ +0.04 citation@1 0.91 ▲ +0.07 refusal 0.02 ▼ −0.01 p95 latency 740ms ▼ −310ms cost / 1k $0.42 ▼ −$0.19 ───────────────────────────────────────────── PASS: 412/412 above threshold. promoting v3.2 → staging

→ Evaluated against a fixed suite before release

→ Grounded retrieval with citations users can check

→ Explicit cost and latency budgets per feature

→ Human review loops where the stakes justify them

03 Pipelines / Warehouse / Streaming / Analytics

Data platforms. One source of truth your team stops arguing about, wired to the dashboards people actually open.

Map your data stack →

Ingestion, modelling and orchestration versioned and tested like application code, with lineage you can trace from a number on a dashboard back to the row that produced it.

Ingest

Every source, one contract

Product events, app databases, payment and ad platforms, all landed on a schedule you can rely on.

Model

Logic in version control

Business definitions written once as tested dbt models, reviewed like any other pull request.

Test

Freshness and quality gates

Null, uniqueness, range and freshness checks fail the run before a wrong number reaches a meeting.

Serve

Answers, not exports

Governed metrics feeding dashboards, product features and the models that depend on them.

→ Transformations under version control

→ Freshness and quality tests on every run

→ Column-level lineage end to end

→ Warehouse spend reviewed every quarter

04 Kubernetes / Terraform / CI-CD / Observability

Cloud and platform. Infrastructure defined in code, deployed on merge, and observable at three in the morning.

Review your infrastructure →

We set up the unglamorous parts properly: environments, secrets, rollbacks, alerting and budgets. Releasing then stops being an event and becomes something that happens quietly, several times a day.

ServiceDeploys / wkp95Availability

api-gateway3484 ms99.99%

checkout-service21112 ms99.98%

events-ingest1239 ms99.99%

ml-inference18740 ms99.95%

→ Every environment reproducible from code

→ Zero-downtime rollouts with instant rollback

→ Alerts tied to user-facing objectives

→ Cloud spend reviewed against real usage

Six disciplines.
One team.

Design, engineering, AI, data, platform and quality sit in the same team, so the thing that gets designed is the thing that gets built, and nothing is lost in a handoff between three vendors.

Product design. Research, flows and interface design that make a complicated product feel obvious, delivered as a system your engineers can build from, not a picture.

Web development. Applications and platforms that stay fast as they grow, server-rendered where it matters, measured against real devices and real networks.

Mobile development. Native and cross-platform apps taken from prototype through store review to the release cadence that follows launch.

AI and machine learning. Copilots, agents, ranking and forecasting. Each scoped against a metric, evaluated before launch, and monitored once real users arrive.

Data engineering. Pipelines, warehouses and metric layers that turn scattered systems into numbers your whole company agrees on.

Cloud and DevOps. Infrastructure as code, continuous delivery, observability and cost control. The foundation everything else is deployed onto.

How an engagement runs

Four stages, and something working in your hands from the first one.

No six-week discovery phase that produces a slide deck. We agree what success means, then start building against it, with a demo you can click at the end of every sprint.

01 / WEEK 1–2

Discover

We pressure-test the idea, map users, constraints and integrations, and write down what success means in numbers. You get a fixed scope and a fixed price.

02 / WEEK 2–4

Design

Flows and interfaces you can click through, validated with real users before production code is written, plus the architecture decisions that follow from them.

03 / ONGOING

Build

Senior engineers ship in two-week cycles with automated testing and continuous delivery. A working demo every sprint, and the repository is yours from commit one.

04 / AFTER LAUNCH

Operate

We launch, watch the numbers, harden what breaks and keep improving performance and cost, or hand the whole thing to your team with the documentation to run it.

What clients typically see

2 wk

From kickoff to working software in your hands

3–5×

Faster to first release than a traditional build

30%

Average reduction in cloud and delivery cost

100%

Code and infrastructure ownership, from day one

Tell us what you're building.

Send us the problem, not a specification. We'll come back with scope, a timeline and the team to deliver it, usually within two working days.

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