Agency Field Notes: I Built This Tool to Monitor AI Platform Performance

Let me paint the picture of our daily workflow before building this tool:

The Manual Process (Per Client, Per Week):

• •Open 3-5 different AI platforms (ChatGPT, Claude, Perplexity, SearchGPT)

• •Manually type 20-30 test queries relevant to client's industry

• •Read through AI responses line by line looking for brand mentions

• •Classify each mention: Is it in the main answer or just a citation? Positive, neutral, or negative tone?

• •Screenshot everything for proof

• •Copy-paste data into Excel spreadsheets

• •Compile into a client-facing report

Time Cost: 4-6 hours per client per week

Scalability: Absolute nightmare beyond 5 clients

The worst part? We were supposed to be AI marketing experts, yet we were stuck in the most manual, repetitive work imaginable. It felt like we were fighting World War II battles in the era of nuclear weapons.

When our 8th client signed on, I knew we had two choices: hire more junior staff to keep doing this manually (throwing bodies at the problem), or finally solve this properly with automation.

I chose automation.

Building this tool wasn't about simply calling APIs. The hard part was teaching code to 'read' results like an experienced marketer would.

After months of manual work, I realized we were really only looking at three things when analyzing AI responses. So I encoded these three patterns into llmoai.net's logic:

1. Keyword Hit & Position (Visibility Logic)

When doing manual checks, we'd obsess over whether the client was mentioned in the first paragraph or buried at the end of a list.

Now the logic works like this:

• •Program auto-scans generated responses, searching for client URL or brand keywords

• •If appearing in 'Top Recommendations' section: weight doubled

• •If only appearing as a 'Reference' link: weight reduced

• •Position scoring: Top 3 mentions = premium visibility score

Example: If ChatGPT recommends 'For CRM tools, Salesforce and HubSpot are popular, but [YourBrand] offers better pricing,' your brand gets flagged as 'Alternative Recommendation' with moderate-high visibility.

2. Automated Sentiment Analysis

This was the most labor-intensive part. LLMs sometimes speak diplomatically, making tone hard to judge.

I integrated an NLP analysis layer specifically to 'read the room.' No more guessing—the program analyzes adjective polarity.

Case Examples:

• •'Although pricey, feature-complete' → Identified as Neutral-Positive

• •'Users report privacy concerns' → Identified as Negative (high-alert flag)

• •'Industry-leading performance' → Identified as Strong Positive

• •'Limited customer support' → Identified as Negative (specific weakness flag)

The system doesn't just count mentions—it understands context.

3. Citation Extraction & Source Analysis

Perplexity and SearchGPT's core value lies in citations. We used to click each footnote marker individually to check sources.

Now the tool automatically:

• •Extracts all Citations

• •Deduplicates and categorizes them

• •Shows you exactly which articles competitors are getting cited from

This single feature has been a goldmine for competitive intelligence. We can now see: 'Competitor X gets cited because of these 3 blog posts'—then we know exactly what content gaps to fill.

Originally, this was just a terminal-based Python script I ran for my own weekly reports.

But I noticed everyone in my SEO and marketing circles had this same pain point. So I spent some time wrapping it in a frontend interface and deployed it online.

The New Workflow (Simplified):

1. Input your URL

2. Backend automatically queries across mainstream AI models

3. Outputs comprehensive analysis results

Time Cost: 2 minutes (down from 4-6 hours)

Key Features:

• •Multi-Platform Coverage: ChatGPT, Claude, Perplexity, Google AI Overviews

• •Automated Sentiment Scoring: Positive/Neutral/Negative classification with confidence scores

• •Citation Source Tracking: See exactly which URLs AI models are pulling from

• •Competitive Benchmarking: Compare your brand's AI visibility vs competitors

• •Historical Tracking: Monitor changes in AI mentions over time

• •Alert System: Get notified when negative mentions appear

This is how llmoai.net came to be.

This article isn't a sales pitch (basic features are currently free anyway). I'm publishing this because I believe too many people in the AI marketing space are still working with primitive methods.

If you're running an agency, or you're an indie developer or site owner struggling with 'I have no idea how AI talks about my website,' you should try this tool.

It's not perfect. The interface is simple. But at minimum, it rescued me from Excel hell.

Who Should Use This:

• •Agency Teams: Stop burning junior staff hours on manual monitoring

• •Brand Managers: Know exactly how AI represents your brand 24/7

• •SEO Professionals: Understand which content drives AI citations

• •Product Teams: Track competitor positioning in AI responses

• •Content Creators: Validate whether your content makes it into AI training

Real Agency Use Case:

We now run llmoai.net scans for all clients every Monday morning. By Tuesday, we have actionable insights:

• •Client A: Negative sentiment spike due to a critical Reddit thread being cited—we addressed it immediately

• •Client B: Dropped from Top 3 to 'also mentioned' in category queries—triggered content refresh strategy

• •Client C: Competitor overtook us in citation count—we identified their 3 key articles and created better alternatives

This kind of speed and clarity was impossible with manual monitoring.

If you encounter bugs during use, or have suggestions for additional analysis dimensions, please let me know in the comments or on the website.

Let's make AI marketing less painful—together.