Notes from the picks-and-shovels side of AI search.
Technical deep dives on generative engine optimization, brand monitoring across LLMs, agentic workflows with Hermes, and the data engineering behind MentionsAPI.
The complete build of a Generative Engine Optimization tool. Four atoms, six surfaces, daily cadence, real cost numbers. One weekend of work, ~$120 a month to run.
Track which URLs AI engines cite as answers. Citation extraction, gap analysis, and an outreach feedback loop. Around 150 lines and ~$50 a month.
Build the single visibility score every CMO asks for. One number, plotted over time, sourced from real AI answers across six surfaces. ~80 lines, $30 a month.
Track brand position inside ChatGPT, Claude, Gemini, and Perplexity answers the same way you track Google rankings. ~100 lines, $15 a month.
Compare how ChatGPT, Claude, Gemini, and Perplexity treat your brand differently. Per-model gap reports drive specific optimizations the rollup score hides.
Build an AEO and GEO automation agent on Hermes in one evening. Five steps, real config, $15 to $30 per month to run.
Six concrete automations you can run on Hermes today: visibility checks, citation gap analysis, share-of-voice tracking, AI Overview alarms, prompt discovery, and content evals.
Wire MentionsAPI into Hermes Agent and watch every ChatGPT, Claude, Gemini, and Perplexity answer about your brand. Setup in 10 minutes, $0.02 per check.
A 1,000-query teardown of how badly the LLM APIs you are optimizing for diverge from what real users actually see in the UI. With numbers, sources, and the picks-and-shovels framing.
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In the meantime, the docs have a surprising amount of reading material. Head to the docs →