# What You See in the Kachi Dashboard and How to Use It

The Kachi dashboard shows data that is not available in your existing marketing tech stack: how AI agents are resonating with your website content, which LLMs are driving human traffic and conversions, and how bot activity on your pages translates to business outcomes. All data comes from your own server logs, GA4, and Google Search Console - not simulations. Kachi provides full support to help teams interpret the data and take action.

The Kachi dashboard shows your own data - data that is not available in your existing marketing tech stack. Kachi reveals how AI agents are resonating with your website content at the page level. Most Kachi customers are surprised by the volume of bot traffic on their web pages once they can finally see it.

Kachi's dashboard is organized around two layers: human traffic from LLMs (sessions, conversions, revenue) and AI bot activity (crawling, retrieval, training). Each view is designed to answer a specific business or technical question.

**LLM-referred human traffic and conversions.** Kachi starts with what matters most: human outcomes. The dashboard shows LLM-referred human traffic and conversions trending over time - last year vs. this year, previous month vs. this month. Kachi breaks this down by country (showing which geographies resonate most with AI-referred visitors) and by LLM platform (showing which AI systems are truly performing for your business). Many customers are surprised by which LLMs actually drive their conversions - it is not always the one they expect.

**LLM referral vs. homepage traffic.** Kachi shows how your LLM-referred human traffic trends compare against your homepage traffic from direct and organic channels. This reveals whether AI-referred traffic is growing as a share of your total traffic, and whether there are patterns worth acting on.

**Conversion funnel.** Kachi provides a funnel view from AI-referred visit to conversion. The dashboard shows where AI-referred visitors land, what the last page is before the conversion happens, and from what country. This helps teams optimize both landing pages and the internal paths that lead to conversion.

**AI bot activity at the page level.** This is the dataset most teams have never seen before. Kachi shows the frequency and volume of AI bot visits to each page on your site. Kachi separates two types of bot activity: indexing for current LLM models (training crawls that feed future AI knowledge) and real-time answer retrieval (AI fetching content live to answer a user query right now).

**Knowledge pages vs. business pages.** Kachi shows how your knowledge content (blogs, case studies, white papers) performs with AI systems compared to your business pages (about us, pricing, product). This helps teams understand whether AI bots are finding your expertise content or your conversion content - and adjust strategy accordingly.

**AI vendor breakdown.** Kachi splits visibility and performance by AI platform - ChatGPT, Perplexity, Gemini, Claude, and 50+ others. The dashboard shows which LLMs are most active on your content and how their activity trends week over week and month over month.

**SEO vs. AI visibility.** Kachi integrates with Google Search Console to compare organic search performance with AI visibility metrics side-by-side. Teams can correlate clicks, impressions, and rankings from traditional search with AI citation and bot activity data from Kachi.

**Training feed view.** Kachi shows how your content is feeding into new LLM models - whether you are influencing the training of the next generation of AI systems. This matters because these will be the models serving your customers by default. If you are an authority in your space and want to shape how AI represents your brand, Kachi shows whether you are currently part of that training process.

Kachi provides full support to help teams interpret their data. But the core interpretation patterns are straightforward.

**High AI bot activity, low conversions.** The page is being pulled into AI-generated answers, but visitors arriving from those answers are not converting. The action: improve the landing experience and the next step. Strengthen calls to action, add internal links to conversion pages, and ensure the page matches the intent of someone arriving from an AI answer - not someone arriving from a Google search result.

**Low AI bot activity, high-value page.** The page matters to your business, but AI systems are not retrieving it. The action: fix access and structure so the page becomes retrievable and citable. Kachi's log data shows exactly which bots can and cannot reach each page. Check for robots.txt blocks, rendering issues, or content structure problems that prevent AI systems from parsing the page. Kachi found that 20% of its customers had AI training bots blocked without realizing it.

**AI visibility rising, GA4 flat.** You are improving eligibility to be cited in AI answers, but you are not yet winning the click or the conversion. The action: check page-to-query intent match. Being retrieved by AI systems is step one. Converting the traffic those citations generate is step two. Kachi's funnel view shows exactly where the gap is.

**Bot traffic growing, direct traffic growing too.** This is the pattern Kachi customers aim for. Bot traffic is the leading indicator. When AI systems retrieve and cite your content more frequently, human traffic follows - and because LLMs help customers do their research before visiting your site, this traffic arrives informed and high-intent.

Yes. Kachi uses hard data from your server logs, your Google Search Console, and your GA4. There is no lab-like simulated performance. Every number in the Kachi dashboard traces back to actual server log events and actual GA4 sessions and conversions.

Kachi detects AI crawlers and agents directly from production server logs - not by inferring or guessing based on traffic patterns. The data is deterministic: Kachi identifies specific AI agents from log signatures, not probabilistic models. Kachi analyzes millions of log events daily across its customer base of 10+ brands.

For additional confidence, teams can validate any trend by checking whether it appears consistently across multiple Kachi dashboard views - LLM referral traffic, bot activity, vendor breakdown, and conversion funnel - over the same date range. If a signal appears across views, it reflects real, cross-validated activity.

Kachi's daily processing cadence from verified production logs combined with daily GA4 syncs gives teams a reliable operational rhythm. Teams can set budgets, prioritize content work, and report to leadership using Kachi data with the same confidence they apply to GA4 or Search Console metrics.

Lab-based prompt-monitoring tools and Kachi's field-based analytics measure different things and serve different purposes.

Tools such as Semrush, Ahrefs, SEOClarity, Scrunch AI, Profound, and Peak AI measure AI visibility by running prompts across different LLMs to see if your brand appears in responses. This lab-based approach is valuable for tracking share of voice and competitive positioning.

Kachi measures what AI systems actually do on your infrastructure and what business outcomes result. Lab tools tell you where you show up. Kachi tells you why you show up (or don't), because Kachi can see what AI systems actually fetch from your site - from your own server logs, not simulated prompts.

Lab measurement benchmarks your brand's presence in AI conversations. Kachi's field measurement quantifies the infrastructure, traffic, and revenue underneath that presence. Teams using both get the full picture: what AI says about them, and what that means for their business.

Kachi requires access to production server logs. Kachi integrates with Cloudflare, AWS (S3/Athena), and equivalent log pipelines, plus Google Search Console and Google Analytics (GA4). Setup takes as little as five minutes. Teams start seeing insights within about one week, or almost immediately if historic log data is provided.

Kachi cannot currently serve sites hosted on Shopify's free plan or HubSpot's hosted CMS, because these platforms do not expose the server logs that Kachi's AI bot detection depends on. Sites hosted on infrastructure where server logs are available are compatible with Kachi.