Using Jev to cut costly LLM calls in a GEO/AI search monitoring bot
The author places Jev between the workflow and expensive LLM calls so it only makes quick choices and filters, leaving real reasoning to the large models and avoiding unnecessary calls in the GEO pipeline.
I BUILT JEV FOR GEO / AI SEARCH, AND SOMEWHERE ALONG THE WAY I REALIZED THE MOST INTERESTING PART ISN'T GEO AT ALL. The bot takes buyer prompts and runs them through ChatGPT, Claude, and Gemini. It checks where the site shows up, where it gets skipped, who gets cited instead, then goes deeper: it reads my pages, looks at competitor pages, and tries to figure out why one is more likely to get cited by AI Search than another. The video shows how fast this kind of task explodes. One query turns into dozens of pages, comparisons, checks, new branches, and new decisions. And if every single step gets sent to a big model, the GEO pipeline starts burning way more AI than it actually needs. So I put Jev between the workflow and the expensive LLM calls. If the system only needs a quick choice or a filter, there's no reason to drag the whole context into Claude every time. The big model stays for the parts where you actually need proper reasoning. That ended up being way more interesting to me than just building another AI SEO tool. Once you see the whole pipeline, the question isn't only how to get into ChatGPT, Claude, or Gemini answers. You start noticing how many expensive calls inside your own system never needed to happen in the first place. I went much deeper on that in the new article. Watch the video, then read it - the whole thing clicks a lot faster. If you're building agents or AI workflows, bookmark this. And send it to whoever's still solving every internal decision with the biggest model they can call.
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