Fixed where it can be, flexible where it must be.
Both established approaches have a cost, and the cost falls on the client either way. We use each where it is strongest.
Waterfall
Predictable budgets and timelines with expectations set at the start. It cannot absorb a change in requirements, and a project that meets a rigid plan can still miss what the client needed.
Agile
Requirements iterate in short cycles and the result is usually better. The price is that the price is unknown, which is why large software companies plan against a burn rate.
Our work
Fixed scope, timeline and budget across the areas that support it, set from hundreds of prior projects, with short agile cycles inside an agreed budget on the areas that do not.
A release should be completely uneventful.
The people managing your project work here.
Engineers and project managers on our own staff, using our own tooling. No part of the delivery depends on a third party we cannot reach, and the person answering on a Tuesday is the person who was there in the planning.
Everything between done and running.
The gap between a finished feature and a working production system is where most projects lose their time, and almost all of it is manual work repeated.
Source control is the spine: a branch per change, review before merge, and a permissions hierarchy deciding who can send what where. Nothing reaches production that has not been through it, including the urgent thing at five on a Friday, which is the one that most often breaks something.
Deployment runs automatically from a merged branch to AWS. The same steps run every time in the same order, so a release is not an event and nobody schedules an evening of downtime around one. Anything that changes stored data goes out ahead of the code that needs it, which is what keeps a rollback available.
Automated tests drive a real browser through the application: clicking, filling in forms, moving between pages, in the conditions a person would. That catches the regression a unit test cannot see, which is the kind that reaches a client. Monitoring then watches the running system and alerts on what a user would notice rather than on what a server reports.
The result is felt as reliability rather than as speed. Problems are found and corrected quickly, the risk that a release takes the system down falls to something an organization can live with, and the engineering team spends its time on the product instead of on the process around it.
