An AI pilot project works best when it starts from a single, well-defined process, not the whole operation at once. In Jestor, the starting point is usually an adjusted starting template, with an AI Agent configured through Agent Builder for that specific process. Why a narrow scope makes the
Today, AI agents for financial operations can already read documents, fill in records, and classify requests — but they don't decide approvals on their own. In Jestor, AI Agents work inside Gestão Financeira alongside Edição de Registros por IA, always with a human decision on the approval itself. How
To identify bottlenecks with real-time data in an approval workflow, the way to go is checking the process dashboard and directly asking about the observed pattern. In Jestor, Chat with Data queries Approvals indicators in natural language, with no need to wait for a ready-made report. Why approval
AI applied to HR helps mainly in two areas: screening candidates and filling in data during onboarding. In Jestor, the RH e Recrutamento module organizes job openings and applications on a Kanban board, and AI Agents support prioritization, with Edição de Registros por IA filling in fields automatically. Why manual
Connecting AI agents to internal systems depends on two things: having data already centralized, and an open way to exchange information between systems. In Jestor, Agent Builder uses the company's own Tables as its base, and the open API integrates with internal systems the company already uses. Why
Before adopting AI in process automation, the starting point is mapping the current process and checking whether it's repetitive enough to justify an agent. In Jestor, that evaluation happens before configuring AI Agents or using Agent Builder, usually supported by BPMS to design the flow first. Why evaluate
Building operational reports with AI means asking about the data in natural language, instead of manually building a spreadsheet. In Jestor, Chat with Data queries each process's dashboard indicators and answers based on generative AI, with no technical knowledge required. Why does building an operational report manually take
To implement AI in operations management, the most effective path starts with one specific process, not the whole company at once. In Jestor, that means configuring a first AI Agent through Agent Builder, measuring the result, and only then expanding with App Builder com IA to other processes. Why starting
AI to identify bottlenecks works by cross-referencing the data a process already generates — time per stage, volume by status — and answering questions about it in natural language. In Jestor, this happens through Chat with Data, based on SLA indicators and each process's dashboard. Why operational bottlenecks tend
Efficient support with AI agents on WhatsApp depends on three things: a channel connected via the WhatsApp Business API (Meta), an agent configured for the right process, and visibility through a support dashboard. In Jestor, those three elements already come natively integrated. What makes WhatsApp support efficient? Efficiency here isn&
Using AI for request triage means letting the system classify and prioritize every ticket as soon as it comes in, before anyone even looks at it. In Jestor, AI Agents handle that triage inside the Service Desk, with Edição de Registros por IA filling in the classification fields. Why manual
AI in approval decisions supports the approver by organizing and classifying the request before review, without making the decision in their place. In Jestor, Edição de Registros por IA prepares the record inside the Approvals flow, and Chat with Data helps query the history of past decisions. Where does AI