Prices move, access changes and models behave differently from one release to the next. If all of your AI lives inside one vendor's product, each of those is your problem on their schedule.
The useful question is not which model is best this month. It is what you would have to rebuild if you changed. The less of your work that lives inside the model vendor's product, the smaller that answer is.
The model does the reasoning. Everything that makes it your work can sit outside it.
Your live systems stay the system of record. Agents work on a copy.
Roles for people and agents decide what is allowed, what needs approval and what is denied.
A person decides what goes back to a live system, and when.
Who did it, which agent, which tools, which model and what changed.
On PRESHos the model is chosen per agent, from Anthropic, OpenAI, Google and xAI, through one gateway. The permissions, the approvals and the record stay the same whichever model does the work.
We have written about exit plans, fallbacks and model routing in more depth.
We build AI into the systems you already run, give agents a platform to work on, and keep it all running and improving.
We build AI into the systems you already run.
We give agents a place to work, under your rules.
We keep your AI running, measured and improving.
AI & Automation
Agents can do real work on a working copy of your data while a person approves what goes back. Here is how a digital twin and an approval gate work together.
AI Engineering
Different agent jobs suit different AI models. Here is how to choose a model per agent, compare options fairly and keep your controls the same throughout.
AI Engineering
Field-level permissions decide which parts of a record an agent can read or change. Here is how roles, scopes and fields combine, with a worked example.
Tell us which AI tools your team uses and what depends on them. We will talk through what to keep in your own hands.