Start with the problem.
AI changes quickly.
The underlying operational problems usually don’t.
We start by understanding the work: the people, systems, information, decisions, constraints and exceptions that make up the actual operation.
Then we determine where AI is useful.
Work close to the operation.
The people building the system work closely with the people who understand the problem.
That shortens the distance between what happens in the real world and what gets engineered.
Get something real working.
We prefer working systems over long AI roadmaps.
When appropriate, we start with a focused problem, build something usable, put it in front of the people doing the work, and learn from what happens.
Then we build outward.
Fit into the real environment.
Useful AI rarely lives by itself.
It may need to work with existing databases, applications, documents, APIs, permissions and workflows.
We design around that reality.
Build for production.
A prototype can show that something is possible.
Production requires more.
Security. Privacy. Permissions. Reliability. Evaluation. Auditability. Human oversight.
We consider these part of the system—not things to add after the AI works.
Use the right technology.
We’re not tied to a single model or AI vendor.
We choose technologies based on the problem, the environment and the constraints.