Boundaries over bolt-ons
Control layers — model, tool, policy — are more durable than clever prompts.
● About
Engineering partners for production AI.
Simor helps teams design and harden production AI systems for real users. We work on agents, MCP integrations, model gateways, guardrails, observability, and evals.
§01
What we believe.
Most AI failures in production are not about the model alone. They come from weak controls around the model. Treat prompts as versioned assets, route traffic through one model layer, put every tool behind a policy, and measure quality before and after release — and most of the fragility disappears.
§02
How we engage.
Our engagements are short, focused, and engineering-led. We review architecture, fix what is breaking, and leave behind an operating model your team can keep using. We do not do open-ended strategy or build demos that will not survive contact with real users.
§03
Principles.
Evals before opinions
Quality decisions should be measured, not debated in a review meeting.
Traces or it did not happen
Every request should be reconstructable end to end, by default.
Budgets are a feature
Spend limits are part of the system, not a billing afterthought.
Least privilege for tools
MCPs and APIs get scoped permissions and tests, like any other integration.
Ship the runbook
We hand over operating notes your team can use on day one.