How we work
Most automation projects fail in the boring parts: unclear scope, flaky integrations, nobody watching after launch. Our process is built around avoiding exactly those.
Timings are typical ranges. Your proposal gives specific dates for each milestone.
We map how the work happens today: who touches it, which tools hold the data, where it breaks. You get a short written plan with the automations worth building first and the ones that aren't worth it.
We build the riskiest part first against your real data, often the AI step or a tricky integration, so you can judge quality before committing to the full build.
Weekly demos, a shared board and a staging environment. We add logging, retries, human review steps and alerts, because an automation nobody can see fail is a liability.
We roll out in stages, train your team and hand over documentation. After launch we monitor, tune prompts, and keep integrations working when your vendors change their APIs.
Pick the one that matches where you are. You can switch between them as things evolve.
You know the problem and want it solved.
You know something is slow but not what to automate first.
You want a team that keeps improving things.
Data sync, retries, permissions and logs aren't glamorous, but they decide whether an automation survives its first month. We spend real time on them.
Where an AI step can be wrong in a way that costs money or trust, it drafts and a person approves. We remove the review step only when the data says it's safe.
Code in your repository, workflows in your accounts, API keys in your vault. If we part ways, nothing stops working.
Every system ships with a short runbook: what it does, how to tell it's healthy, and what to do when it isn't.
Want to see how this plays out? Read how we built our own products and client systems, or browse the services this process applies to.
A single focused automation is often live in two to four weeks. AI agents and custom apps with several integrations usually take six to twelve weeks. The proposal gives you dates per milestone, and you see working software from the prototype stage.
A main contact who knows the process, about an hour a week for demos and questions, and access to the tools involved (usually a sandbox or a limited-permission account). For AI work, examples of real inputs, such as emails, tickets or documents, make a huge difference to quality.
We use least-privilege access, keep credentials in your accounts or a secrets manager, and avoid copying production data to our machines. For AI features we choose providers and settings that don't train on your data, and we can keep everything inside your own cloud if required. Ask for our security notes during discovery.
Yes, and we recommend it. One workflow that clearly saves time builds more trust inside your team than a big programme. Many clients start with a discovery sprint or a single automation and grow from there.
Often. We can build alongside your team, review their work, or hand a system over to them with documentation and a walkthrough. We'll match your conventions for code, branching and deployment.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.