Why I Built an AI Visibility Tool When Semrush Already Had One

Semrush shipped their GEO tool first. So did Otterly. So did half a dozen enterprise SEO suites. So when people ask why I built SignalixIQ, I get it. The market looks crowded on the surface.

Here's the honest answer: the existing tools are built for enterprise SEO agencies, not for individual merchants. I tried to use them for my own stores and they were the wrong shape for the actual job.

What the Existing Tools Do Wrong for Merchants

Semrush GEO tool: $449/mo entry point, focused on keyword rank tracking in LLM results. It tells you "ChatGPT mentioned X keyword" but doesn't tell you what to fix on your store. The output is a dashboard for an agency that bills clients hourly, not a to-do list for a solo operator.

Otterly: similar pricing, similar output. Great for consultants doing retainer work with brands that have a content team. Bad for a DTC founder who wants to fix their schema and move on.

DataFeedWatch: feed optimization tool, but built for google shopping ads feeds specifically. Doesn't address AI agent visibility at all. Good at what it does, just a different problem.

The free AI visibility checkers floating around: they run one ChatGPT query and tell you "you showed up" or "you didn't". Useless. You need to know WHY and WHAT TO FIX, which means looking at your schema, your feed, your content.

The Merchant's Actual Job

When I talk to Shopify store owners, their actual job looks like this:

1. "Am I showing up in ChatGPT shopping answers or not?"
2. "If not, what specifically is broken on my store?"
3. "How do I fix it without hiring a dev?"
4. "After I fix it, how do I know the fix worked?"

That's four questions. The existing tools answer question 1. SignalixIQ is built specifically to answer all four.

What I Actually Built

The free tier runs against any store URL (no signup) and returns a GEO score from 0-100 with severity-ranked issues. Each issue has a plain-English fix and a "how to fix this on Shopify" link. That's question 1 and 2, solved.

The Starter tier ($49/mo) adds Feed Optimizer, which auto-enriches your product data using AI. Fills in missing GTINs from product descriptions using pattern matching, generates missing brand names from product titles, writes agent-readable product descriptions. That's question 3, mostly.

The Growth tier ($149/mo) adds the MCP Server Generator and Agent Analytics. You get a hosted MCP endpoint that agents can query directly, plus a dashboard showing which agents hit your catalog, what they queried, and whether they converted. That's the full loop including question 4.

Scale tier is $349 for unlimited SKUs and API access. Agency tier is $499 for white-label dashboards if you're a consultant servicing multiple merchants.

The Pricing Philosophy

I priced it deliberately low for two reasons. First, the alternative for merchants is $449+/mo for Semrush and I want to undercut that hard. Second, AI visibility is a time-sensitive problem. The longer merchants wait, the more revenue they lose to competitors who moved early. I'd rather have 10,000 merchants at $49 than 500 at $450.

The unit economics work because most of my costs are per-scan (OpenAI calls for enrichment) not per-seat. A $49 merchant with 500 SKUs costs me about $3/mo in inference. Margins are fine.

What Took the Longest

The MCP server generator took the longest. Most of an MCP implementation is straightforward, just a TypeScript SDK wiring up tools to HTTP endpoints. The hard part was making it work across different store platforms without losing fidelity.

Shopify has a consistent Admin API. WooCommerce has a REST API but it's inconsistent across versions. BigCommerce has a good API but different product attribute models. Magento is its own special kind of pain. I ended up building a normalized product model internally and writing adapters per platform. Took about 3 weeks of dedicated work.

The other time sink was the AI visibility probe. I have to actually run queries against ChatGPT, Claude, and Perplexity and check if the store shows up in the answers. Each platform has different rate limits and response formats. I use the OpenAI API for ChatGPT, Anthropic for Claude, and a scraped web interface for Perplexity. It's fragile but it works.

What I'd Do Differently

Two things, in hindsight.

First, I'd have shipped the free scanner before building the paid tiers. I spent too long on the Feed Optimizer before I had any users. Should have gotten the free tool in front of people, measured what they actually cared about, then built the paid features around that.

Second, I'd have built the MCP server earlier. It's the single most differentiated piece of the product and the one that's hardest for competitors to replicate. I kept deprioritizing it because it felt "advanced" but it's actually the core value prop.

Where I'm Going

The next 3 months are pretty focused: (1) grow the free-tier scanner user base aggressively, (2) get the first 100 paid customers onto Starter or Growth, (3) ship the B2B mode for wholesale distributors.

B2B is the most underserved piece. Everyone's optimizing for DTC because that's where the early AI shopping traffic is. But B2B procurement via AI agents is going to be huge in 2026-2027, and nobody has the tooling for ETIM codes, UNSPSC, ERP connectors, etc. That's my Q3 push.

If you run any ecommerce store, especially Shopify, go run a free scan at https://signalixiq.com/ right now. Takes 2 minutes. You'll learn something. Whether you pay me or not after that is up to you, but the scan alone is worth doing.

Building in public, will share numbers as they grow. Thanks for reading.