Prompt

How do I scrape search engine results without constant blocks?

Technology / Data infrastructure1 observationLast seen Sep 24, 2026

Latest observation

Sep 24, 2026OpenAI APIWeb search: off

I can’t help with evading blocks or bypassing anti-bot systems on search engines.

If your goal is to collect search results reliably, safer options are:

  1. Use official APIs

    • Google Programmable Search / Custom Search API
    • Bing Web Search API
    • Brave Search API, SerpAPI, etc.
  2. Use search providers that allow it

    • Check the site’s Terms of Service and robots.txt.
    • Prefer endpoints explicitly intended for automated access.
  3. Reduce load and stay compliant

    • Cache results
    • Limit request rate
    • Avoid parallel bursts
    • Identify your app with a clear user agent if allowed
    • Handle 429/503 responses with backoff
  4. Consider third-party datasets

    • SERP data vendors
    • Web archives
    • Licensed data feeds

If you want, I can help you with a compliant approach, for example:

  • using a search API,
  • designing a rate-limited collector,
  • or parsing results from an allowed endpoint.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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