Mark Jherico
Magno

A multi-platform conversation aggregator that pulls inbound messages from four different services into a single dashboard.

The client was managing brand conversations across multiple messaging platforms with different APIs, auth flows, and message schemas. Each platform had its own quirks — one used cookie-based auth with a short-lived token, another required webhook ingestion, a third had a polling-only interface.

• Built normalization layer to unify message schemas across 4 platforms
• Implemented auto token refresh on a 50-minute interval to avoid auth drops
• Per-platform error handling and retry logic for failed message fetches
• Single Express server with platform-specific service modules

An auto-reply bot for a third-party messaging platform that classifies inbound brand messages and responds with smart, context-aware replies.

The client was getting 30+ inbound brand inquiries per day and replying manually, often with delays. I reverse-engineered the platform's internal GraphQL API (cookie auth, undocumented token refresh flow), built a Node.js service that polls for new messages, classifies them by intent, and sends a templated reply customized to the inquiry type.

• Handles 7 distinct inquiry categories (rates, portfolio, booking, retainers, etc)
• Templated responses with LLM-assisted personalization to avoid robotic feel
• Runs 24/7 under PM2 with Slack alerts on errors or auth expiry
• Reduced response time from hours to seconds

// classify inbound message → route to template
async function classifyAndReply(message) {
  const intent = await classifyIntent(message.body);

  if (!TEMPLATES[intent]) {
    await slackAlert('unknown intent: ' + intent);
    return;
  }

  const reply = await personalize(TEMPLATES[intent], message);
  await sendReply(message.threadId, reply);

  log({ intent, threadId: message.threadId, at: Date.now() });
}

A rule-based agent that filters inbound campaigns from a creator marketplace and auto-applies to qualified ones, then pushes summaries to Slack.

The marketplace had hundreds of new campaigns daily, most of which didn't match the client's criteria (payout floor, job type, certificate status). Reviewing manually was eating into actual creative work time. I built a worker that pulls new campaigns on a schedule, runs them through a strict rule filter, applies to the qualified ones via the platform's internal API, and posts structured summaries to specific Slack channels for the team to review.

• Strict rule-based filtering — no AI hallucination risk on apply decisions
• Structured Slack summaries grouped by campaign type
• Audit log of every application sent, for client review
• Automated 200+ daily evaluations down to ~10 actually-relevant applications