Make the consequential choices explicit.

The tools changed. A few experienced people can now build software that once required a much larger team. We organize around that opportunity: fewer handoffs, focused products, and decisions made close to the work.

AI assists the build. People decide what should exist, make the tradeoffs, and remain responsible for the result. Our standard is software that holds up in use. That means thinking about clarity, maintenance, and operating costs from the beginning.

Different work needs different capability. A narrow extraction task and an open-ended reasoning task may need different models. Some tasks need no model at all.

Our principle is to use the least expensive model that meets the quality requirement. Local and hosted systems offer different tradeoffs in capability, response time, cost, and data handling. More capability should earn its additional cost.

Those choices should remain open to revision. As models improve, the architecture should allow a product to improve with them. A provider choice made for the first release should not become a permanent constraint.

A model choice is also a data decision. Before information leaves a system, the questions should be explicit: which service will receive it, what does the task require, and could the same result be achieved with less information?

Our design principle is to keep sensitive material within a controlled boundary where the task permits, and limit external processing to the context needed for the work. These decisions belong in the architecture, before a request is sent.

Each product needs its own accurate explanation of those boundaries. A company-wide principle is useful only when the product’s actual behavior supports it.

Language models are good at sounding finished. A plausible answer can still miss the point, invent a detail, or fail on an ordinary input.

Our standard is to evaluate important behavior against fixed examples and explicit acceptance criteria drawn from the product’s actual work. Repeatable checks make regressions visible and help show where a cheaper model is sufficient.

Human judgment remains part of that process. The checks should reflect the consequences of an error, including the cases where the right response is to ask, abstain, or hand the decision to a person.

We believe useful products begin with close knowledge of the work. Living with a workflow exposes friction and weak assumptions that a market description can miss.

That proximity also imposes discipline. Some work should be automated. Some should be accelerated. Some needs a person to decide.

A product earns its place by helping someone do real work better. We build with that standard and a long-term view.