One team for the whole journey: understand the problem, prove the opportunity is real, and build something that lasts longer than the contract.
Legacy modernization
For enterprises carrying software older than most of the team.
The system works, nobody wants to touch it, and every change costs weeks. We take ownership of code we did not write: read it properly, document what it actually does, get tests around the risky parts, then modernize in slices while it keeps serving customers. No rewrite-and-pray.
Taking over undocumented systems
Cloud and platform migration
Untangling the database
Replacing pieces without downtime
Contract engineering teams
For teams that need senior people, not more headcount.
Engineers who join your standups, your repo and your standards, and work as part of your team rather than behind a wall. You get people who push back when something is a bad idea, and who leave good documentation behind when the contract ends.
Dedicated squads or single specialists
Your process, your tools, your timezone overlap
Monthly or per-project
Handover built in from day one
New product builds
For an idea that has to become real.
From the first conversation to something people can use: web platforms, mobile apps, internal tools, the infrastructure underneath. We start by narrowing the problem until it is small enough to build well, then build it.
Web platforms and SaaS
iOS and Android
Internal and operations tooling
APIs and infrastructure
Proofs of concept
For the assumption your plan depends on.
Before anyone commits a year, we build the smallest honest version and put it in front of real users. Sometimes it proves the idea. Sometimes it saves you the year. Both are a good outcome.
Feasibility spikes
Prototypes with real users
Technical due diligence
A clear recommendation either way
AI where it earns its place
For work that is genuinely repetitive or genuinely hard.
We use AI where it removes real friction — extracting meaning from documents, answering questions over your own data, automating the work nobody wants — and we say so plainly when a simpler system would do the job better.
Assistants over your own data
Document and workflow automation
Evaluation before deployment
An honest no when it does not fit
The process / 02
How the work actually goes
Step 1
Understand
We spend the first weeks learning your domain, your constraints and what has already been tried. Most bad software comes from skipping this.
Step 2
Prove
We build the smallest thing that tests the riskiest assumption, and we show you the result even when it is not what either of us hoped for.
Step 3
Build
Working software in front of you continuously, not a reveal at the end. You always know where it stands.
Step 4
Keep it alive
Software is not finished when it ships. We stay for as long as it is useful to have us, and we leave it maintainable when we go.
Who we build for / 03
We work across industries
We have built for regulated healthcare data and for online stores that just need to be fast on a bad connection. What carries over is the habit of learning a domain before writing code for it.