by AISee | 2025-11-27 21:29:16+00:00
Hi HN,

I’ve been experimenting with how large language models surface products, companies, and information inside generated answers — and built a tool called AISee to measure this more systematically. I wanted to share it here in case others are exploring similar questions.

Problem

As AI assistants (ChatGPT, Claude, Perplexity, Gemini) become discovery channels, many teams started noticing something odd:

Their website ranks fine on Google

But AI systems don’t mention them

Or worse, describe them inaccurately

Or mention competitors instead

This isn’t visible in traditional SEO analytics, and manually prompting each AI engine doesn’t scale or stay consistent.

The question that drove this project was:

“How exactly does each AI system represent a given brand or product across different queries, and how does that change over time?”

What AISee Does

AISee runs structured queries across multiple AI engines and captures:

whether a brand appears

how often it appears

in what context

which competitors appear instead

how consistent the descriptions are

whether key product attributes are missing or wrong

It then computes an “AI presence” score and highlights inconsistent or outdated information.

No marketing layer, no growth hacks — just visibility and representation data.

Why This Matters

LLMs compress what used to be an entire SERP into a single aggregated answer. That means:

fewer brands get surfaced

category winners are amplified

outdated model knowledge lingers

and LLM “ranking behavior” varies significantly by platform

For anyone working on search, ranking, NLP, entity integrity, or recommendation systems, this offers an interesting new space to study.

Implementation Details

A few notes about how it works under the hood:

Query sets are generated using a structured taxonomy (category → intent → modifier)

Responses are parsed through a semantic extraction layer (entity detection, attribute matching, competitor identification)

We track deltas over time to detect model drift or knowledge shifts

No scraping of proprietary content — only model outputs

The system is event-driven, and each AI engine is queried via its public API or UI automation depending on platform availability

I’m especially interested in improving the consistency detection and competitor graph.

What I’m Looking for

I’m hoping to hear from:

People working on AI search / LLM ranking transparency

Anyone studying how LLMs internalize and surface entities

Teams who have seen strange inconsistencies between AI engines

Feedback on the methodology or architecture

Thoughts on how to make this more technically useful

Link

You can try it here: /https://aisee.live/

(Early version — mostly visibility analysis and pattern detection.)

Closing

This is an early-stage project. I’m not sure where it goes yet — whether it becomes a useful monitoring tool, a research dataset, or something closer to a “visibility debugger” for LLMs.

But the underlying question feels important as AI-generated answers start replacing traditional search interfaces.

Happy to answer technical questions about the implementation, the modeling approach, or observations from early data.

Thanks for reading.