Lokrix AI documentation

Lokrix measures the calibrated probability that the major AI answer engines — ChatGPT, Google AI Overviews, AI Mode, Perplexity, Gemini, Grok and Claude — cite, link, recommend or name your brand for the questions your buyers actually ask. Every score comes with a confidence interval, a feature-level reason, and a simulated path to improve it.

How a scan works

You enter a URL. Lokrix detects the brand, generates the real prompt universe your audience uses, runs those prompts through each engine multiple times, and turns the results into a Monte-Carlo presence probability with a confidence interval — never a single-run guess.

The Lokrix AI scan pipelineA six-stage flow: a URL is analyzed to detect the brand, which generates a prompt universe; each prompt runs across seven answer engines K times; the sampled presence is aggregated into a Monte-Carlo probability with a confidence interval and rolled up into the Lokrix AI Score.URLexample.comDetect brandindustry + NERPrompt universebuyer questionsEngines × K7 enginesMonte-Carlopresence + CILokrix Score0–100K samples

Explore the docs

Pick a section to dive in, or use the sidebar to browse everything.

Getting Started

What Lokrix is, the core concepts, and a five-minute quickstart.

Properties

Track many sites at once — create, switch, and onboard each property.

Running Scans

6 pages.

Answer Engines

How each of the seven engines grounds its answers — and why it matters.

Understanding Scores

5 pages.

Optimization

Prioritised, simulated recommendations with a predicted path to #1.

Integrations

Google Search Console & Analytics, re-indexing, and data-processing (AVV/DPA).

Developers

The REST API, the MCP server, SDKs, API keys and scopes.

Billing & Credits

Plans, seats, and how usage credits meter live engine runs.

FAQ & Troubleshooting

2 pages.