Key Takeaways:
- Parsnipp’s new GEO platform aims to help marketers understand AI visibility by simulating full buyer research journeys, not just testing isolated prompts across LLMs.
- Andrew Higgins argues that accurate GEO strategy depends on better data, stronger modeling of real user behavior, and a broader view of the digital signals that shape AI recommendations.
Parsnipp has launched a new artificial intelligence (AI) Search and GEO platform designed to help marketers understand how their brands appear inside generative AI systems by modeling real buyer interactions instead of relying on isolated prompts.
The launch comes as tools like ChatGPT, Gemini, Claude, Grok, and Perplexity play a bigger role in how people research products, compare options, and make buying decisions. That shift is changing the path between brands and customers as AI acts as an intermediary in discovery, evaluation, and, increasingly, commerce.
According to company founder Andrew Higgins, Parsnipp is entering that market with a clear argument: marketers need better visibility into how AI systems interpret brands today, and they need practical guidance to improve that visibility before AI-driven discovery becomes standard.
Parsnipp Is Betting GEO Is More Than SEO With New Labels
The Seattle-based company platform takes a different approach from many early GEO tools. Rather than measuring answers to single prompts at scale, Parsnipp simulates fuller research journeys using persona-based agents and multi-turn interactions.
Higgins stated many tools today focus on prompt testing, keyword-like tracking, and broad output analysis. Parsnipp is trying to frame the problem differently.
“I think of this way more as a full channel that I do an individual touch point to get visibility,” said Higgins. “It’s not SEO, to me. This is more akin to the rise of social media marketing or mobile because of the number of different points along a customer journey that will need to be optimized for. It’s going to be a full channel that you need to manage.”
Why Parsnipp Focuses On Simulated Research Journeys
Currently, AI visibility tools mirror early SEO logic. They test large numbers of prompts, review model outputs, and report on brand mentions. This does not fully capture how real people interact with AI systems over time.
Parsnipp’s answer is to model user behavior more closely, said Higgins. Its platform lets marketers work with search personas, simulate research paths, and study how AI responds across an extended decision process. That matters because large language models (LLMs) are contextual: what they say can change depending on the user, the follow-up question, the framing of the request, and the signals available across the web.
The platform includes brand analytics across major LLMs, competitor tracking, prompt and citation analysis, GEO content optimization, search personas, and AI readiness recommendations. These features show not just whether a brand appears, but why it appears, where competitors may be stronger, and what marketers should do next, said Higgins.
Data Quality Is A Core Part Of The Pitch
A major part of Higgins’ argument is that the GEO category has a data problem as much as a tactics problem. If marketers are trying to understand how AI interprets a brand, they need a credible model of how real people use AI in the first place.
“It’s very clear to me, the things that are hard to do today are not the things that are going to be hard to do tomorrow,” he said. “There are a lot of folks operating on a lot of brands, operating a lot of incorrect or incomplete assumptions around what how AI is interpreting their brand”
That concern feeds directly into Parsnipp’s product design. Weak prompt testing can create the appearance of insight while missing the complexity of actual buyer behavior. Higgins made that point plainly: “If you model real user interactions poorly, it’s just bad data.”
Parsnipp emphasizes recommendation quality as much as raw measurement. The company scans a brand’s broader digital footprint, including website structure, social media, reviews, ratings, product feeds, and earned media, to identify the signals that may shape how LLMs cite, describe, or recommend a brand.
Preparing Brands For The Future Of AI Marketing
Parsnipp’s longer-term strategy goes beyond helping marketers measure visibility. Higgins sees the company as preparing brands for a larger structural shift in digital marketing, one in which AI sits between consumers and the businesses trying to reach them.
“There’s an interesting inflection point today—we’re selling a solution to a problem everybody just realized exists, which is the classic playbook,” he said. “Overnight consumer behavior turned upside down— instead of interacting with one of my retail partners, with my brand, directly with a competitor, there’s an AI sitting in between that shopper and my brand. So if I market and sell anything online, the playbook, the tooling, all needs to evolve, because I have to meet my customer in a whole new way.”
Higgins stressed the need for marketers to build expertise now, before those shifts become harder to manage.
“I think about it as a new marketing channel,” he said. “We need to learn. We need to build internal expertise, understanding and start putting a strategy in place.”
