The Nine Tools We Built to Get Brands Cited by AI

By Derick Michael, Head of GEO

We built nine tools. For a while some of them were public and anyone could run them. They are internal now, and this page is what replaced them.

The short version of why: every public run cost us real money in tokens, and the people running them were not the people we wanted to talk to. Running them ourselves, in bulk, on brands we actually want to work with, costs a fraction of that and produces something far more useful — a report a founder can act on rather than a score they screenshot and forget.

So instead of letting you run them, we will run them on your brand and show you what came back.

Here is every one of them, in the order we actually use them.

First, find out what the models think you are

LLM Perception → — We ask the engines what they know about a brand from training data alone, with no web access, then compare it against what the brand's own site says. The gap is the finding. Scored out of 100 across Recognition, Accuracy and Positioning. Nothing downstream matters if this comes back low.

AI Readiness Audit → — A structural diagnostic of whether the engines can read you at all: crawl permissions, JavaScript rendering, schema completeness, entity consistency, content structure. Runs in about a minute and decides what the first ninety days contain.

Competitor Scorecard → — Grades a brand against three competitors on AI-search presence, content depth, SEO maturity and brand clarity. We pick the three, and picking the right three is the most consequential judgement in the exercise.

Then, grade what already exists

Pillar Analyzer → — Grades the content a site already has: which subjects it genuinely owns, which it touched and abandoned, which have volume but no structure holding them together. Most brands find one accidental pillar and two abandoned ones.

AIO Article Checker → — Grades a single article for whether an engine can lift a claim from it and attribute it cleanly. Meta, structure, schema and content, weighted, out of 100. Ranking and being cited are different jobs.

Then, decide what to build

Pillar Cluster Engine → — Reads a site and proposes three to five pillar pages with six to ten clusters each, ordered by what to ship first. No search volume, no SERP data, no competitor sitemaps. Every title has to work at the category level.

Review Mining and Sentiment Gaps → — Pulls reviews from the open web, not just a connected reviews app, and looks for the distance between what a brand claims and what customers keep saying back.

Then, produce

The Reddit Citation Engine → — Finds threads the engines are already citing where the existing answers are weak, and puts the client's own voice in them. Two to four placements a month, maximum. We do not operate accounts on your behalf.

The Article Production Agent → — Every article traces back to one of four kinds of evidence: strategy, reviews, Reddit, or the pillar map. Nothing gets written from a blank page.

Some of these are finished software. Some are steps our team runs by hand with tooling that keeps changing as the models do, and we say which is which on each page rather than pretending otherwise.

Your free AI Perception Report is the first three of these, run on your brand, before we ever speak.

Book the call →

Frequently asked questions

Can I still use the free tools that were on Firon Labs?

No. Some of Firon's tools were public for a while and anyone could run them, but all nine are internal now, and this page replaced them. Every public run cost real money in tokens, and the people running them were not the people Firon wanted to talk to. Running the tools in bulk, on brands Firon actually wants to work with, costs a fraction of that and produces something more useful: a report a founder can act on rather than a score they screenshot and forget. So instead of letting you run them, Firon runs them on your brand and shows you what came back, as a free AI Perception Report.

What is included in the free AI Perception Report?

The free AI Perception Report is the first three of Firon's nine tools, run on your brand. LLM Perception asks the AI engines what they know about the brand from training data alone and scores it out of 100 on recognition, accuracy and positioning. The AI Readiness Audit checks whether the engines can read the site at all, across crawl permissions, JavaScript rendering, schema, entity consistency and content structure. The Competitor Scorecard grades the brand against three competitors on AI-search presence, content depth, SEO maturity and brand clarity. Together they show what the models think the brand is, whether they can read it, and who they recommend instead.

In what order should a brand work on AI search visibility?

In Firon's method the order is fixed, and it has four stages. First, find out what the models think the brand is, using LLM Perception, the AI Readiness Audit and the Competitor Scorecard. Second, grade what already exists: the Pillar Analyzer grades the site's current content and the AIO Article Checker grades individual articles. Third, decide what to build, with the Pillar Cluster Engine and review mining. Fourth, produce, through the Reddit Citation Engine and the Article Production Agent. Each stage depends on the one before it. There is no point optimising content for engines that do not know who you are.

Are Firon's AI visibility tools finished software?

Some are and some are not, and each tool's page says which. Some of the nine are finished software. Others are steps Firon's team runs by hand, with tooling that keeps changing as the models do. For example, the review pull is built and running, but how review gaps are categorised is still in build. The Article Production Agent describes how Firon decides what to write today, run by the team, while the fully automated version is in build. The AIO Article Checker is run on client content today and is still being built out as a proprietary internal tool.

Derick Michael · Head of GEO, Firon Marketing

12+ years engineering visibility. Pioneer of the Agentic Commerce Protocol (ACP), focused on future-proofing brands for the transition to AI-driven discovery.