How to Feed Real-Time Web Content into Your GPT Pipeline

You are building a pipeline that uses GPT to summarize industry news, monitor competitors, or generate reports. The model is capable, but it has no access to current information. You need live web content, and building a crawler is a project in itself -- managing request queues, handling rate limits, parsing HTML, dealing with JavaScript rendering, and maintaining it all when sites change their markup.

A search API eliminates the crawling layer entirely. You search for what you need, get structured results back, and feed them directly into your GPT pipeline. This post walks through the architecture.

The Problem With Crawlers

Building a web crawler for an AI pipeline introduces problems that have nothing to do with your actual product:

• You need to discover URLs before you can fetch them
• Many sites block automated requests or require JavaScript rendering
• HTML parsing is brittle and site-specific
• You have to handle rate limiting, retries, and error recovery
• Content extraction (removing nav, ads, footers) is its own challenge

A search API solves the discovery problem (it finds the URLs) and returns structured data (titles, snippets, metadata) that you can feed directly into a language model without parsing HTML.

Architecture: Search Then Summarize

The simplest pipeline has two stages: search for relevant content, then pass the results to GPT for processing.

async function searchAndSummarize(topic: string) {
  // Stage 1: Get fresh content
  const searchRes = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-key": process.env.SCAVIO_API_KEY
    },
    body: JSON.stringify({
      platform: "google",
      query: topic,
      mode: "full"
    })
  });
  const searchData = await searchRes.json();

  // Stage 2: Summarize with GPT
  const context = searchData.organic?.slice(0, 8).map((r: any) => (
    `Title: ${r.title}\nSource: ${r.link}\nSnippet: ${r.snippet}`
  )).join("\n\n");

  const gptRes = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [
      { role: "system", content: "Summarize search results into a brief." },
      { role: "user", content: `Topic: ${topic}\n\n${context}` }
    ]
  });
  return gptRes.choices[0].message.content;
}

Multi-Platform Pipelines

Real-world pipelines often need data from multiple platforms. A product research pipeline might combine Google results with Amazon product data and YouTube reviews:

async function productResearch(product: string) {
  const [google, amazon, youtube] = await Promise.all([
    search("google", `${product} review 2026`),
    search("amazon", product),
    search("youtube", `${product} review`)
  ]);

  return openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{
      role: "user",
      content: `Analyze this product based on the following data:
        Web reviews: ${JSON.stringify(google.organic?.slice(0, 5))}
        Amazon listings: ${JSON.stringify(amazon.results?.slice(0, 5))}
        YouTube reviews: ${JSON.stringify(youtube.results?.slice(0, 5))}

        Provide: sentiment summary, price range, key pros/cons.`
    }]
  });
}

async function search(platform: string, query: string) {
  const res = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-key": process.env.SCAVIO_API_KEY
    },
    body: JSON.stringify({ platform, query })
  });
  return res.json();
}

Handling Freshness and Deduplication

When running pipelines on a schedule, you want to avoid processing the same content twice. Track URLs you have already processed and skip duplicates:

async function getNewContent(topic: string, seenUrls: Set<string>) {
  const data = await search("google", topic);
  const newResults = (data.organic ?? []).filter(
    (r: any) => !seenUrls.has(r.link)
  );
  for (const r of newResults) {
    seenUrls.add(r.link);
  }
  return newResults;
}

Cost and When to Use This

A pipeline that runs 10 searches and one GPT-4o call costs under $0.10 per execution. The search API completes in 1-3 seconds with structured data -- no parsing step needed. For high-volume work, batch searches in parallel.

This pattern works best for daily content summaries, competitive intelligence, product research across marketplaces, news monitoring, and lead enrichment. For most AI pipeline use cases, structured search data -- titles, snippets, metadata -- is enough for the model to produce useful output.