Jefferson Alves

• • Design, build, and embed agentic AI automations into hospitality and service business operations —

specialising in workflow identification, no-code implementation, and team adoption.

• • Identify operational inefficiencies through direct observation and stakeholder interviews; translate findings into scoped automation projects with measurable outcomes.

• • Build integrations across n8n, Claude API, Supabase, WhatsApp/Evolution API, and Next.js —

connecting existing operational tools rather than replacing them.

• • Document every automation with SOPs, runbooks, and training materials so non-technical team members can operate, monitor, and troubleshoot independently after handoff.

• • Monitor workflow performance post-deployment; iterate based on adoption rates, failure patterns, and evolving operational needs.

• • Built three integrated AI-powered systems for a Lisbon tour operator, solving operational failures identified from two years of frontline experience at the same company.

• • Scheduled Comms Agent: fully automated WhatsApp guest-touchpoint pipeline — booking confirmation, pre-arrival briefing, day-of logistics, and post-stay follow-up. Zero manual intervention. Guest journey owned end-to-end by the system.

• • OrkestraTour: mobile-first scheduling and operations platform — driver assignment, booking management, real-time fleet coordination. Replaced a fragmented manual workflow entirely.

• • Cockpit LX: real-time operational intelligence dashboard — city events, traffic, cruise arrivals, weather. Enabled proactive decision-making instead of reactive firefighting.

• • Drove adoption with non-technical operators: built training materials, ran walkthroughs, and designed interfaces with zero technical knowledge assumed. All systems in production 24/7.

• • Stack: n8n

◦ · Claude API

◦ · Supabase

◦ · Next.js

◦ · Evolution API / WhatsApp

◦ · REST APIs.

• • Delivered guest experiences for hundreds of visitors (80–90% North American) — full accountability for quality, logistics, and real-time problem resolution from departure to return.

• • Two years of direct operational observation: identified recurring guest communication failures, scheduling inefficiencies, and information gaps that became the automation briefs above.

• • Managed escalations in real time — booking conflicts, vehicle issues, guest health situations — without scripts or escalation paths. Judgment calls under operational pressure.