Cloud & DevOps
Infrastructure defined in code, automated deployments, monitoring and alerting, backup and recovery you have actually tested, and a hard look at what you are spending each month.
Services
Everything from the idea to the
thing running in production.
We take on whole problems rather than isolated tickets. Below is what that covers in practice — most engagements combine two or three of these.
01 — Mobile
Mobile applications
Apps people keep on their home screen. We handle the full lifecycle: store submission, release management, crash monitoring and the awkward parts like offline sync, background jobs and push notifications.
- Cross-platform or native. Flutter and React Native when one codebase makes sense; Swift and Kotlin when it does not.
- App Store and Play submission. Signing, review responses, staged rollouts and privacy declarations handled for you.
- Built for real devices. Tested on physical hardware, not just simulators, across the range your users actually own.
Typical deliverables
PrototypeClickable, testable
iOS + Android buildSingle codebase
Backend & APIIncluded where needed
Store listingAssets & submission
Post-launchMonitoring & updates
Typical deliverables
Design systemReusable components
Front endResponsive & accessible
APIs & databaseDocumented
Admin toolingFor your team
AnalyticsWired from day one
02 — Web
Web platforms
From a marketing site that loads instantly to a multi-tenant SaaS product with roles, billing and audit trails. Built on mainstream, well-supported technology so you are never dependent on one person's private stack.
- Performance as a requirement. Core Web Vitals treated as an acceptance criterion, not an afterthought.
- Accessible by default. Keyboard navigation, contrast and semantics checked against WCAG 2.2 AA.
- Integrations. Payments, CRM, ERP and whatever else already runs your business.
03 — AI
AI & machine learning
AI is only worth doing where it removes real cost or real delay. We start from the workflow you want to improve, prove it on your own data, and only then build it properly.
- Assistants over your own content. Retrieval-augmented search across documents, tickets and knowledge bases.
- Document and image processing. Extraction, classification and routing of the paperwork that currently eats hours.
- Data handling made explicit. Where your data goes, what is retained and what stays on your infrastructure — agreed in writing before we build.
How an AI engagement starts
Step 1Pick one workflow
Step 2Test on your real data
Step 3Measure against a baseline
Step 4Build it into the product
If step 3 does not beat the baseline, we tell you and stop there.
04, 05, 06
The foundations underneath.
Applications are only as good as what runs and feeds them.
Data engineering
Pipelines that pull your systems into one place, a warehouse that can be queried without fear, and reporting your team trusts enough to make decisions from.
Product & UX design
User research, information architecture, wireframes and a clickable prototype — plus a design system so the tenth screen still looks like the first.
Not sure which of these you need?
That is a normal place to start. Describe the problem and we will tell you what it would actually take — including when the answer is "less than you think".