01 · How we evaluate
The Four C's of production AI.
Every engagement evaluates the system against four dimensions. Each one is a data question first, an AI question second. Most evaluations get one or two right. The work is getting all four to hold at once.
Context
Does the AI have the right data, in the right shape, at the right time?
Retrieval, freshness, and the boring plumbing that decides whether the model is reasoning over reality or over a stale snapshot of it.
Control
Is the data governed so AI decisions can be audited, limited, and explained?
Lineage, access boundaries, and the audit trail a regulator will eventually ask for — designed in from the start, not bolted on after an incident.
Cost
Does the data architecture scale economically as the workload grows?
The difference between a system that gets cheaper per unit at scale and one that quietly erodes its own business case as usage climbs.
Choice
Can the infrastructure flex across models, vendors, and deployment options?
Avoiding the lock-in that turns next year's better, cheaper model into a six-month migration nobody budgeted for.
02 · How we work
Four steps. In order. Every time.
Start with the data
Before touching a model, we trace where the data comes from, what it actually contains, and where it quietly breaks. This is where most of the eventual production failures are already sitting, waiting.
Build for production
Code is written against real load, real latency budgets, and real failure modes from the first commit — not retrofitted for them after a demo earns a green light.
Make it observable
A system you cannot see is a system you cannot operate. Monitoring, evaluation, and alerting are part of the build, not a follow-on project that never gets funded.
Hand it back governed
The goal is a system your team can run without me — documentation, runbooks, and governance written for the people who inherit it, and a clean exit rather than a dependency.
03 · Principles
What we believe, stated plainly.
Specific over abstract
Concrete examples beat principles. A real number beats a confident estimate.
One throat to choke
The person who scopes the work is the person who writes it and stands behind it. No handoffs.
Boring on purpose
Production engineering is not glamorous. The systems that last are the ones that chose dull and reliable over clever.
Data first, always
Your AI is only as good as your data. Fixing the model rarely fixes a data problem.