Our principles.
Six things we believe strongly enough to put in writing. Some will resonate. Some won't. That's the point: AI-native work requires explicit opinions.
01. AI-native is architecture, not adoption.
We're an AI engineering team because AI is the highest-leverage tool we have. But a company does not become AI-native by buying tools or opening more ChatGPT tabs.
The work starts when agents can understand the workflow, access the right context, operate inside clear boundaries, and hand judgment back to humans at the right time.
02. We don't reinvent what already works.
We're aggressively pragmatic about using battle-tested tools. If LangGraph, Langfuse, or Composio solves your problem, we configure them.
We don't write a worse version from scratch to inflate the invoice.
03. No production AI without observability.
Every system we ship is instrumented from day one. Tracing, evals, dashboards, audit trails, and escalation review.
We won't put an agent into production blind, and we won't let you do it either.
04. We build for portability, not lock-in.
Every system runs on open-source or industry-standard tools with portable data.
If we go away tomorrow, you hire someone else who knows the same stack. The work belongs to you.
05. Small team. Direct contact. No layers.
The people who scope the work build it, ship it and support it.
As we grow, we add more small teams, not more layers between them and you.
06. Make the implicit explicit.
Agents need to know what good looks like, where truth lives, which actions are allowed, and which decisions require human approval.
We turn policies, edge cases, tone, approvals, examples, and institutional memory into systems agents can work from.
These aren't aspirational.
They decide what we say yes and no to. We'll turn down a project when observability isn't in scope, recommend a simple database view when that's the right answer, and tell you plainly when your operating layer needs work before agents can help.