Runflow and ChatGPT Enterprise: talking about the system, or executing inside it.
An enterprise assistant billed per seat, with an admin console. Where it is enough and where the process needs to execute.
In one paragraph.
ChatGPT Enterprise does a good job of the problem it solves: giving AI to the whole company with admin controls. It is a conversation tool: the person asks, it answers. An operations agent does the opposite of what an assistant does: instead of waiting for the question, it receives the trigger, queries your systems, executes and returns the result, with a trail of what it decided and why.
ChatGPT Enterprise and Runflow.
The comparison is about the delivery model, not about a feature list. Features change month to month; who builds it, who sustains it and how it is billed is what decides the project.
| ChatGPT Enterprise | Runflow | |
|---|---|---|
| What it is | OpenAI's enterprise assistant, with an admin console, security controls, connectors and custom GPTs. | An AI agent orchestration platform, plus the Forward Deployed engineers who deploy it with your team. |
| How the work starts | A person opens the conversation and asks. | A system trigger or a customer message fires the agent, with nobody having to start it. |
| Where it operates | In the conversation interface, with the connectors that have been configured. | Inside the operation: ERP, CRM, the service channel and the customer's official WhatsApp. |
| Choice of model | OpenAI models. | Claude, GPT, Gemini, Llama, Mistral or your own model, with automatic fallback when a provider goes down. |
| Cost control | A per-seat license. | LLM Hub: consumption and cost by agent, by team and by execution, with a spending cap before it becomes a problem. |
| How it is billed | Per seat, with the price negotiated by OpenAI's sales team. | By scope, defined in the free assessment. No hidden setup fee. |
| Where the data runs | OpenAI's cloud, which states that it does not train models on company data by default. | Your cloud, with your own keys, or operated by Runflow. Per-client isolation and compliance with the LGPD, Brazil's data protection law. We do not train models on your data either. |
We are not always the answer.
When ChatGPT Enterprise is the right choice
- The goal is to give an AI tool to everyone, with admin controls.
- The use is conversation: researching, drafting, analyzing, summarizing.
- A single model vendor is enough, and standardization is worth more than choice.
- There is no end-to-end process waiting to be executed.
When Runflow is the right choice
- The agent has to execute inside your systems, not talk about them.
- The work starts on its own, on a system trigger or a customer message, without anyone opening a conversation.
- Each team needs a spending cap, and you need to know who spent what.
- A regulated sector, with a per-decision trail and human approval at the risk points.
The two coexist. The assistant serves the person who asks. The agent serves the process that has to happen, with or without somebody watching.
What changed in a real operation.
Osten Group
+30%
in contact-to-visit conversion, with the first-contact team going from 7 people to 2.
PX Ativos Judiciais
4% to 10%
conversion on first contact, roughly R$ 1.5 million converted over the year without growing the team.
Vero
R$ 2 million
saved on overtime.
You are evaluating more than one.
Information about ChatGPT Enterprise according to OpenAI's public material, accessed on August 24, 2026. No price is quoted because OpenAI does not publish a seat price.
Bring a real process and see what changes.
In a no-cost conversation with an expert, we look at the process you have today and tell you plainly whether the case calls for an agent or whether the tool you already use solves it.