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xavier_aurora

Xavier Alex

@xavier_aurora

AI Automation Specialist for n8n Make com and Business Workflows

Nigeria
Engels, Frans, Spaans, Duits, Italiaans, Portugees
Sommige informatie wordt in het Engels weergegeven.
Over mij
I help businesses save time, respond to leads faster and eliminate repetitive work through reliable automation. I build and troubleshoot workflows using n8n, Make.com, APIs, webhooks, CRMs and AI tools. My services include lead capture, CRM updates, appointment booking, AI receptionists, email followup, data synchronization and custom business workflows using tools like GHL, Monday.com, Click-Up and more. Every solution is tested, documented and designed around your actual business process. Contact me with your current process, the applications you use and the result you want to achieve.... Lees meer

Skills

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xavier_aurora
Xavier Alex
offline • 
Gemiddelde reactietijd: 1 uur

Bekijk mijn diensten

API en integraties
I will builld your business workflows and connect your apps with make com
AI-video
I will create cinematic ai video ads and product commercials for social media

Portfolio

Werkervaring

ScoutDNS

Recruitment Platform and Founder-Operations Automation

ScoutDNS • Freelance

Jun 2026 - Jun 2026 • 0 mos

For this scholar project, I designed and built an end-to-end recruitment and founder-operations automation system for a fictional early-stage hiring platform. The purpose was to demonstrate that I could study an unstructured business process, identify repetitive activities, convert those activities into reliable workflow logic, and create a system that remained understandable to nontechnical founders. I began by mapping the entire candidate journey, from a new application or referral through screening, classification, follow-up, interview scheduling, status updates, and final reporting. I created separate workflow stages for candidate intake, data validation, duplicate detection, AI-assisted résumé summarization, role matching, internal review, communication, and management reporting. The main workflow received candidate information from web forms, email attachments, and spreadsheet imports. I normalized names, email addresses, telephone numbers, role preferences, experience levels, locations, and application sources before writing the information to a central candidate database. I added duplicate-detection rules so that returning candidates were updated instead of being added as new records. When a résumé was available, the workflow extracted relevant text and used an AI model to produce a structured summary containing skills, experience, education, preferred role, possible strengths, and information requiring human confirmation. The AI was not allowed to make a hiring decision. Low-confidence results and incomplete applications were routed to a manual-review queue. I also built lead- and candidate-routing logic. Applications were assigned according to job category, location, experience level, recruiter workload, and priority. Qualified candidates triggered an internal Slack alert and a personalized acknowledgement email.

ClickSystems

Agency Systems Integration with Airtable, Make, n8n, and Zapier

ClickSystems • Freelance

May 2026 - May 2026 • 0 mos

In this independent scholar project, I created a reusable operations system for a fictional automation agency serving several B2B clients. The objective was to prove that I could translate written technical briefs into organized databases, integrations, tested workflows, and clear handover material. I designed the solution as a template that could be duplicated for different clients without rebuilding every component. The system included a central Airtable base, client-specific views and interfaces, intake forms, project and task tracking, automation monitoring, approval queues, and a structured record of deliverables. I first designed the Airtable architecture. Separate linked tables handled clients, contacts, projects, requirements, tasks, approvals, credentials references, automation workflows, errors, change requests, and documentation. I used normalized relationships instead of placing all information in one oversized table. Formula and rollup fields calculated project health, outstanding approvals, delivery risk, task completion, and time since the last client update. Interfaces gave management a portfolio-level view while client-facing views exposed only the information relevant to each client. I added field validation and controlled status values to prevent inconsistent data. The automation layer connected intake forms, Airtable, Gmail, Slack, Google Drive, and calendar tools. A new project submission created the correct records, generated a standard folder structure, assigned initial tasks, notified the delivery team, and prepared an acknowledgement email. Status changes triggered the appropriate internal or client notification without sending duplicate messages. Approval requests were routed to a dedicated queue, and approved items advanced automatically to the next project stage. If a task became overdue, the system sent a reminder based on priority and ownership rather than broadcasting unnecessary alerts to the entire team.

Lurulabs

AI Automation Agency Content, Authority, and Lead-Management System

Lurulabs • Freelance

Mar 2026 - Mar 2026 • 0 mos

For this independent scholar project, I created a combined authority-building and lead-management system for a fictional AI automation agency. The scenario was based on a small agency that needed to make its expertise discoverable, maintain a consistent content pipeline, identify credible directories and publications, track outreach, capture enquiries, and move interested businesses into an organized sales process. Instead of treating marketing and operations as unrelated tasks, I designed one connected system linking content assets, target publications, outreach, mentions, website enquiries, CRM records, and follow-up activity. I built an Airtable content and outreach database containing services, industries, case-study themes, published articles, target publications, directories, contacts, outreach status, placement type, quality checks, fees, follow-up dates, live URLs, and reporting metrics. I created rules to distinguish genuine editorial opportunities from low-quality link farms, duplicate directories, parked domains, and suspicious paid placements. Each opportunity could be scored using relevance, audience fit, organic visibility, editorial quality, disclosure requirements, link type, estimated effort, and cost. Final approval remained a human decision. The automation layer collected approved targets, prepared research summaries, created outreach tasks, scheduled reminders, and recorded responses. AI helped draft first-pass outreach messages using verified information from the database, but no email was sent without review. Responses were classified into interested, needs information, paid opportunity, declined, follow up later, or invalid. Positive responses created the correct writing or submission task, while published placements were checked for the agreed company name, destination URL, disclosure, and link status. Removed or changed placements triggered a review alert. I connected website enquiry forms to a lightweight CRM process.