{"id":3774,"date":"2026-11-04T21:30:18","date_gmt":"2026-11-05T03:30:18","guid":{"rendered":"https:\/\/exyge.com\/solutions\/oficina-de-ia\/"},"modified":"2026-11-08T08:31:47","modified_gmt":"2026-11-08T14:31:47","slug":"ai-office","status":"publish","type":"page","link":"https:\/\/exyge.com\/en\/solutions\/ai-office\/","title":{"rendered":"AI office"},"content":{"rendered":"\r\n
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Home \u203a Solutions \u203a AI office<\/p>\n<\/div><\/div>\r\n

Solutions \u00b7 AI Office<\/span><\/p>\n<\/div><\/div>\r\n

Your AI office, with people at the center<\/h1><\/div><\/div>\r\n<\/div>\r\n
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AI staff who work with your people, not in their place: agents with roles, memory and a schedule, the right model for each task and always a person who decides. Your staff, \u201caugmented\u201d by AI.<\/p>\n<\/div><\/div>\r\n

Schedule a conversation<\/span><\/a><\/div>\r\n<\/div>\r\n<\/div>\r\n
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\"Professionals<\/span><\/div>\r\n
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Sound familiar?<\/span><\/p>\n<\/div><\/div>\r\n

When AI is an individual habit, not a way of working<\/h2><\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n
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Everyone uses AI on their own, in a different chat, with no shared method, memory or rules.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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Agentic AI looks like something for programmers, and business teams are left out.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n

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We don't know which model to trust or where our data goes.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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Nobody is clear on who approves what the AI does.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n

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How does it work?<\/span><\/p>\n<\/div><\/div>\r\n

One interface, many models, people in the loop<\/h2><\/div><\/div>\r\n

The work and the people first; the model after.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n

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\"Diagram<\/span><\/div>\r\n<\/div>\r\n<\/div>\r\n
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Start with the work<\/h3><\/div><\/div>\r\n

We map the decisions, processes and routines where AI adds value. Each agent gets a role, a scope and a person it reports to, like any new hire.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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One interface, many models<\/h3><\/div><\/div>\r\n

Clairvoyance is the human interface. Behind it, the right model for each task: frontier AI, multi-model cloud, local models when the work is private, and your business data.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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A person decides<\/h3><\/div><\/div>\r\n

Agents prepare, review and alert. When something needs approval, they notify the person in their own task app, even when they are out of the office.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n

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How do we roll it out?<\/span><\/p>\n<\/div><\/div>\r\n

From the first agent to a capability that stays in your company<\/h2><\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n
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01<\/h4><\/div><\/div>\r\n

Diagnosis<\/h3><\/div><\/div>\r\n

In 2 to 3 weeks: the routines and decisions where AI helps, how sensitive the data is for each task, the first roles and the approval rules.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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02<\/h4><\/div><\/div>\r\n

Pilot<\/h3><\/div><\/div>\r\n

In 6 to 8 weeks, 2 to 4 agents in one team, with duties, a schedule, a model for each task and a person in the loop. We measure time saved and quality.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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03<\/h4><\/div><\/div>\r\n

Scale and transfer<\/h3><\/div><\/div>\r\n

More teams and roles, internal champions trained to run and expand the office, and the rules on data, models and approvals written into your governance.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n

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04<\/h4><\/div><\/div>\r\n

Custom models<\/h3><\/div><\/div>\r\n

Where the data calls for it, for recurring decisions such as demand, credit or deviations: models trained on your ERP data.<\/p>\n<\/div><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n

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Where does it fit?<\/span><\/p>\n<\/div><\/div>\r\n

AI applied to what we already do with you<\/h2><\/div><\/div>\r\n<\/div>\r\n
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