AI agents that act inside real processes, wired into your environment and running on your business rules.
Request a demo →Animal protein industry
60%
lower cost to serve
Chemical industry
Zero → App
from concept to production
Grupo Orangefox
17
years applying AI inside real operations
SOFIA™
24×7
agent running inside real clients
There is a basic difference between systems that answer and systems that act.
An agentic system does not wait for a question. It watches, processes, decides and executes, inside the limits you set.
Grupo Orangefox designs, trains and runs those agents. On your data and your rules.
Agentic AI
is not a chatbot. It is autonomous execution inside your operation.
Not to the chat. Not to the help desk. To the processes that today depend on people repeating the same work every day.
SOFIA™ is Grupo Orangefox agentic AI. Built in house, it already runs inside real client environments, putting any company data within reach of whoever decides, at the moment they need it.
It is not off the shelf. It is configured for each operation: your data sources, your business logic, your technology environment.
See SOFIA™ at work →What the C-level asks. What SOFIA™ answers.
The CEO asks anything and gets the answer with the company real data, as if an analyst were available around the clock.
The agent watches patterns and flags deviations before they become a problem: fraud, waste, performance drops.
Workflows that depend on manual steps start running on their own, with traceability and the business rules applied.
Reports produced, formatted and delivered automatically to whoever needs them, with the data team out of the operational loop.
Chemical industry / Loyalty
Chemical industry
A loyalty app with built-in CRM, gamification and cashback, conceived, built and launched with intelligence applied by Grupo Orangefox. From zero to production, with an agentic architecture from the start.
Delivery
Full app
in production
Most AI projects that fail do not fail on the model, they fail on the foundation. AI applied to bad data does not fix the data, it just gets things wrong faster and with more confidence. So when the foundation is not standing, the first conversation is about data, not about agents.
With the base in order, the question changes: which repetitive work eats the team time without deciding anything? Support answering the same question a hundred times a day, a report someone builds every Monday, a manual reconciliation between two systems, an order typed by hand out of an email. That is where automation pays, and quickly.
What we do not recommend: dropping in a generic agent to solve everything. The more specific the agent, the better it answers and the less it costs. One for sales, one for expenses, one for support, each with its own scope and its own source.
Every agent that goes into operation has three things defined from the start: what it may decide on its own, what has to go through a person e what it never does. In customer service, for example, price and contract usually stay out of scope and the conversation is handed to someone on the team.
That design is what separates automation the team adopts from automation the team works around. An agent without clear limits creates rework and loses the trust of whoever depends on it.
Customer service, data questions in plain language and task execution, connected to the client environment, with scope, limits and escalation to people.
Flows in n8n that connect systems which do not talk to each other: ERP, CRM, spreadsheets, email and WhatsApp, with an alert when something fails.
When a system has no API, automation goes in wherever it can. The route is not elegant, but it removes the typing.
Apps and portals for the operation and for end customers, from concept to launch, like the loyalty app that went from zero to production in two months.
It does not have to be. The standard design keeps sensitive data in the client infrastructure, and only what the answer requires goes to the model. When the case calls for it, we run the model inside the client environment.
It depends on volume and on the model chosen, which is exactly why a specific agent costs less than a generic one. We measure cost per conversation from the pilot onwards, so the bill is visible before scaling.
That is why the limits are set in the design. Anything uncertain escalates to a person, and every conversation is recorded for review.
For agents that answer with operational numbers, yes. For process automation, not always: many flows are worth doing even before the data foundation is ready.
In 30 minutes we show how it works in a real client environment.