Every site has little failures sitting in plain sight.
A signup page starts loading slowly after a deploy. A CTA still looks clickable after the link disappears. Visitors tap the same dead element three times, leave, and never tell you why. A referral source dries up. Mobile traffic shifts. A form gets submitted twice because the first submit did nothing.
Most teams don't see it the day it breaks. They usually notice later, when the week feels soft, paid traffic stops converting, someone complains, or the post-mortem finally forces everyone to look backward.
That gap is what Gentic Ingest is for.
The Gentic Analytics MCP server already reads PostHog and Northbeam. Gentic Ingest gives it a third source: first-party site behavior the brand owns.
A tiny script sends events into a customer-scoped data lakehouse. Gentic Computer (Mother), your analytics agents in n8n, Claude, ChatGPT, Claude Code, and anything built on the Gentic MCP surface can read that lake in natural language.
Last week I wrote about Gentic Brain. Brain captures what people wrote down: Slack threads, docs, emails, decisions, campaign history.
Ingest captures what visitors actually do.
Those are two different kinds of memory. A brand brain needs both.
Heatmaps were built for humans
Most brands already have tools for this: Hotjar, Microsoft Clarity, heatmaps, session replay, product analytics dashboards.
Those tools are useful. They were built around the previous operating model: a human logs in, watches recordings, clicks through heatmaps, compares date ranges, and decides whether something looks wrong.
That explains why problems can sit in the data for days. The data exists, but it is still functionally invisible until someone remembers to look.
Gentic Ingest changes the reader. It gives your analytics agents a clean stream of site behavior they can query on schedule: yesterday versus the day before, the last seven days versus the seven before that, page movers, Web Vitals, form behavior, rage clicks, traffic sources.
The agent suppresses tiny sample-size noise and posts the summary in Slack when something changed enough to matter.
Gentic Ingest is not one more place for your team to look. It is the site behavior layer your agents can watch.
The site becomes readable
The setup is intentionally boring.
<script async src="https://ingest.gentic.co/gentic-ingest.js" data-site-key="YOUR_WRITE_KEY"></script>The snippet is under 2KB gzipped. It loads async. It doesn't block the page.
Events flow into a customer-scoped data lakehouse: Parquet on S3 with a Postgres catalog, using DuckLake. Open formats. Org scoped. Built for agents to query, not for another UI to own.
Then the Analytics MCP server exposes the useful surfaces.
Surface | Tools |
|---|---|
Audience & sessions |
|
Acquisition |
|
Engagement |
|
Conversion & funnel |
|
UX signals |
|
Performance |
|
Site and schema |
|
Ad-hoc investigation |
|
That is the product shape. Gentic Ingest is the signal layer. The Analytics MCP server is the agent interface. Mother is the operator experience.
The agent should not care which dashboard a human would have opened. It should care which signal answers the question.
The report is a skill
The attached analytics report skill is the part that makes this feel different.
It teaches Mother how to run a daily or weekly site report without a human assembling it. The core rule: every query runs twice.
Current period. Previous period.
A report without comparison is just a snapshot. The value is movement.
A weak report says:
/signup got 300 pageviews.
A useful report says:
/signup went from 430 pageviews to 300. Form submits dropped from 38 to 30. Rage clicks on the fake button went from 2 to 23. LCP crossed from passing to 4.1s p75.
That is the difference between a metric and a reason to act.
The skill handles the analyst chores: resolve the right site, run the same tools for both windows, suppress tiny sample sizes, find page movers, flag bounce-rate movement on entry pages, surface traffic-source changes, and use SQL only when a follow-up question needs it.
The daily report stays short. The weekly report gets a little more room. Either way, Mother posts what changed, not every row she saw.
What your AI agents can notice
A few examples.
Performance regressions
/dashboard was passing Core Web Vitals last week. Yesterday it hit 4.1s LCP at p75. Mobile is the problem. Maybe a hero image regressed from WebP to PNG after a deploy. Maybe a third-party script started blocking the page. Either way, the next step is a deploy check or asset audit, not a dashboard review.
UX friction
/signup had a 3x spike in rage clicks. Same element each time. It is a <p> styled like a button with no class. Twenty-three visitors clicked it. Seven hammered it three or more times.
Engagement collapse
Visitors still land on the page, but active engagement time dropped 40%. Scroll depth fell by 18 percentage points. Something above the fold is no longer doing its job.
Traffic anomaly
A new referrer sent 217 visits yesterday. Or a source that usually sends qualified visitors went quiet. The agent can see both without anyone opening acquisition reports.
Funnel drop-off
Add-to-cart volume held steady, but checkout_started dropped. Or checkout_started held steady while purchase dropped. The agent can locate the step that lost ground.
Health baseline
After a few weeks, the agent knows normal: bounce rate, form submits, rage clicks, mobile split, LCP per top URL. The report gets more useful once the agent has a baseline to compare against.
At that point, it is no longer just answering questions. It is noticing when reality changed.
Proactive AI starts with boring data
The exciting version is easy to imagine: the agent files a ticket, pages someone, suggests a redeploy, rewrites the landing page, launches a test, or fixes the broken CTA.
Those actions only work if the agent has a signal it can trust.
A signup problem hidden in a dashboard is invisible to the agent. Web Vitals trapped in a UI are just screenshots waiting for a human. Click and scroll events need to be queryable. An anomaly needs a baseline, or the agent cannot tell the difference between a real problem and a noisy Tuesday.
Gentic Ingest is that first layer: a clean, current, AI-readable stream of what is happening on the site.
Once that exists, the behavior changes.
Old workflow | Gentic Ingest workflow |
|---|---|
Someone remembers to open Hotjar, Clarity, or analytics | Your AI agent runs checks on schedule |
Humans scan dashboards and heatmaps | Agents compare periods and find movement |
Reports show totals | Reports show what changed |
Problems surface after revenue drops | Problems surface when behavior starts drifting |
Follow-up requires another dashboard session | Follow-up happens in Slack or an MCP-compatible agent |
The bigger shift is ownership. The brand gets an agent-readable data layer that can feed Mother in Slack, n8n automations, Claude, ChatGPT, Claude Code, or a custom agent built on the Gentic MCP surface.
What this unlocks next
The first version is reporting.
Daily report. Weekly report. Current period versus previous period. Biggest movers. UX friction. Web Vitals regressions. Funnel drop-off. Traffic changes.
The next version is action.
You AI agent should be able to file a ticket with the element selector when rage clicks spike on /signup. A top page crossing the LCP threshold can trigger a deploy check. A checkout drop-off spike can reach the operator before the week is ruined. A dead referral source can turn into a campaign-link audit.
Some actions will stay human-approved. Some will become automatic. That boundary will vary by brand.
The foundation is the same either way: the agent needs clean, current, queryable data about what visitors are actually doing.
The interesting moment is when the agent learns normal. That is when a report stops being an update and starts becoming an early warning system.
Get started
Sign up for Gentic Ingest at gentic.co/ingest.
If you want the setup walkthrough, start here: How to connect Claude Code to website analytics.
DM Bora on LinkedIn with any questions.
