Showcase · Agentic Search

Get found by AI searching on behalf of people

AI assistants don't read banners — they read structure. This scanner measures in one run how readable your site is for AI agents, has a language model assess the content, and delivers a roadmap that provably works.

Part of our website project: if you don't yet have a (modern) site, the AI Agency builds you one — same project, different entry point.

15+
checks per scan
36 → 85+
score jump (case study)
19
AI agents served
Level 5
own reference site
From URL to roadmap

Five steps, one report

01

Enter a URL

The scanner fetches the site the way an AI assistant does — including the files agents look for that humans never see.

02

15+ checks in parallel

Technology, speed and AI readability are scanned simultaneously: llms.txt, schema.org, robots rules for AI agents, structure and content.

03

AI reads your homepage

A language model assesses the content itself: is your proposition understandable to an agent searching on behalf of a customer? Where are the gaps?

04

Score + roadmap

You get an AI-findability score with concrete priorities: what to fix first and what it delivers.

05

From report to fix

Recipes are ready for the major platforms; via edge workers a site can be made agent-ready without rebuilding the existing stack.

Technology in detail

Built by those who run it themselves

The new discoverability

More and more visits come not from humans but from AI assistants searching on their behalf. Whoever is unreadable to agents soon won’t exist — this product makes that measurable.

Measures what agents see

The scanner checks exactly what an AI agent encounters: llms.txt, schema.org data, agent-discovery records, robots rules for 19 known AI crawlers, and the machine-readability of the content itself.

Proven on a real case

A webshop scored 36. After the recommended fixes via an edge worker — without changing a single line in the CMS — the score rose to 85+. The report is the playbook.

Crash-resilient backend

The scanner runs as a native host process, deliberately outside the container stack. Heavy and async jobs (bulk scans, news crawls, reports) run via n8n workflows alongside it.

Multi-LLM with failover

The AI analysis runs across a chain of providers with automatic failover and load spreading — model-agnostic, so it can also be EU-hosted or run fully on-prem.

Own site as testing ground

The reference implementation runs on our own domains: Level 5 agent-ready, including agent discovery via DNS and a machine-readable service description. We sell what we run ourselves.

Built with Python / Flask (native)n8n orchestrationSQLiteEdge workersllms.txt · schema.orgAgent discovery (DNS)
See it for yourself

Know where you stand within two minutes

Enter your URL and see your AI-findability score, the gaps found, and the first fixes right away. The scanner runs live — no waiting list.