Cenred | AI Automation Specialist

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// ABOUT CENRED

Building the future
with AI Automation

I'm Cenred — an AI Automation specialist based in Davao, Philippines. I design, build, and deploy intelligent automation systems that turn repetitive workflows into fully autonomous pipelines. From n8n to LLMs, I bridge the gap between business needs and cutting-edge AI.

n8nLLM IntegrationWorkflow AutomationAI AgentsAPI ArchitecturePrompt Engineering

// CORE FOCUS

⚡

AUTOMATION

End-to-end workflow automation using n8n, Zapier, Make, and custom agents.

🤖

AI AGENTS

Building LLM-powered agents that reason, decide, and act autonomously.

🔗

INTEGRATION

Connecting APIs, platforms, and data sources into unified intelligent systems.

// WORK EXPERIENCE

2023 — PRESENT

AI Automation Engineer

Freelance / Independent

Design and deploy end-to-end AI automation pipelines for clients globally. Specialize in n8n workflows, LLM agent integration, and business process automation. Delivered 20+ automation systems across industries.

2022 — 2023

Workflow Automation Developer

Tech Startup — Davao, PH

Built internal automation tools reducing manual workload by 70%. Integrated third-party APIs with CRMs, Google Workspace, and communication platforms. Led AI adoption strategy for the company.

2021 — 2022

Backend Developer & API Integrator

Digital Agency

Developed RESTful APIs and webhook integrations. Built automated reporting dashboards and notification pipelines for e-commerce clients.

// FEATURED PROJECTS

01 / AI AGENT

AutoLeads AI

Fully autonomous lead generation agent. Scrapes, qualifies, and emails prospects — zero human input required.

n8nOpenAIGmail APIAirtable

02 / CHATBOT

DocuBot

AI assistant trained on internal company docs. Answers employee questions 24/7, reducing support tickets by 60%.

Claude APIRAGPineconeSlack

03 / WORKFLOW

SalesFlow 360

End-to-end CRM automation: deal tracking, follow-up scheduling, and AI-generated sales summaries delivered daily.

HubSpotn8nGPT-4Notion

04 / INTEGRATION

DataSync Engine

Real-time multi-platform data synchronization across Shopify, Google Sheets, and warehouse management systems.

Shopify APIWebhooksMakeBigQuery

// TECHNICAL SKILLS

// AUTOMATION PLATFORMS

n8n95%

Make (Integromat)88%

Zapier85%

// AI & LLM

Prompt Engineering92%

OpenAI / Claude API90%

RAG Systems80%

// DEVELOPMENT

JavaScript / Node.js85%

Python78%

REST API Design88%

// DATA & TOOLS

Airtable / Notion90%

Google Workspace APIs85%

Webhook Architecture92%

// LIVE SKILL DEMOS

Sample: n8n-style automation logic — Lead qualification pipeline

// Lead qualification automation
const qualifyLead = async (lead) => {
  let score = 0;
  if (lead.employees > 50) score += 30;
  if (lead.budget > 10000) score += 40;
  const intent = await analyzeIntent(lead.message);
  if (intent === 'high') score += 30;
  if (score >= 70) {
    await createDeal(lead, 'HOT');
    await notifySlack(`🔥 Hot lead: ${lead.name}`);
  }
  return { score, status: score >= 70 ? 'HOT' : 'WARM' };
};

Sample: Structured prompt engineering for AI agents

// System prompt architecture
const systemPrompt = `
You are a lead qualification agent.
RULES:
1. Extract: name, company, budget, timeline
2. Score intent: low | medium | high
3. Output ONLY valid JSON

OUTPUT FORMAT:
{
  "name": string,
  "intent": "low|medium|high",
  "score": 0-100,
  "action": "nurture|demo|close"
}
`;

Sample: Webhook handler + API integration pattern

// Webhook-to-action pipeline
app.post('/webhook/new-lead', async (req, res) => {
  const { name, email, message } = req.body;
  const [enriched, score] = await Promise.all([
    enrichContact(email),
    scoreWithAI(message)
  ]);
  await Promise.all([
    addToHubSpot({ ...enriched, score }),
    notifySlack(name, score),
    scheduleFollowUp(email, score)
  ]);
  res.json({ status: 'ok', score });
});

Sample: Autonomous AI agent loop with tools

// Autonomous agent with tool use
const runAgent = async (task) => {
  let context = { task, steps: [], done: false };
  while (!context.done) {
    const decision = await llmDecide(context);
    switch (decision.action) {
      case 'search': context.data = await webSearch(decision.query); break;
      case 'write': context.output = await generateContent(context.data); break;
      case 'send': await sendEmail(context.output); context.done = true; break;
    }
    context.steps.push(decision.action);
  }
  return context;
};