u/phonethoughts

I've spent six months building Zypact, a measurement tool for brand visibility within LLMs. The category itself — GEO (Generative Engine Optimization) is still being defined as it emerges.

Three counter-intuitive lessons from this experience:

Methodology infrastructure pays before features: I built a 12-principle manifesto, a documented gold set of 200 annotated entries, and three documentary registries (data-debt, product-limits, architectural-decisions) before shipping most user-facing features. This felt slow at first. It now compounds dramatically every architectural decision references a stable framework instead of being relitigated each time.

Educating the market is 50% of the work in emerging categories: Half my time goes to explaining the problem before selling the solution. Most prospects don't yet know that LLMs cite their brand differently than competitors. Once they see comparative data, the value becomes obvious. Without that data, no pitch lands.

External critiques are project assets, not threats: Three external evaluators reviewed Zypact recently. One rejected the entire approach with sharp methodological critique. I documented all three reviews — including the rejection in a public registry. This transparency builds more trust than any positive review alone could generate.

Curious to hear from others building in emerging categories: how do you balance category creation work with revenue acquisition?

I've been running systematic comparisons of how ChatGPT, Claude, Gemini, and Perplexity respond to identical brand-related queries. The variance in source citations is more substantial than I expected.

Methodology:

- Same prompt sent to GPT-4o-mini, Claude 3 Haiku, Perplexity Sonar, and Gemini 2.5 Flash

- Captured on the same day (May 7, 2026)

- max_tokens=600, temperature=0.7

- Prompt: "What are the leading observability platforms for cloud-native applications in 2026?"

Datadog appeared in 4/4 responses but with completely different framing:

- ChatGPT: "industry standard"

- Claude: "popular but expensive"

- Perplexity: cited via TechCrunch article (Q1 2026)

- Gemini: mentioned third, after Grafana and New Relic

Total unique brands cited across the 4 engines: 14

Brands appearing in all 4 responses: 3

Brands appearing in only 1 response: 6

The 6 brands cited by only one engine each represent significant blind spots that would be invisible to anyone monitoring just one LLM. Has anyone else noticed similar patterns? What prompts have surfaced the most divergent results in your testing?

The breakdown isn't your engine (your site, your SEO).
It's your GPS.

You don't see the new traffic lights.

The new lights have 3 colors:
🟢 GREEN: AI knows you AND recommends you
🟡 ORANGE: AI knows you but won't recommend
🔴 RED: AI has no idea you exist (you're invisible)

Most B2B brands are stuck on RED. They don't know it.

I built (Zypact.com )to be the GPS for this new road network.

It tells you:
→ Where the lights are (your AI score 0-100)
→ Who's ahead of you at each intersection
→ The fastest route (sometimes a side road wins)

I have 10 feedback, I want more feedback from you collaborator thank you (zypact.com)

1/ The traffic code has changed.

For 20 years, brands raced on one highway: Google.
Today, 4 new highways exist: ChatGPT, Claude, Perplexity, Gemini.

Most drivers don't even see them.
They keep flooring it on the old road, wondering why they're being passed.