About Louhe

Louhe provides an AI‑driven platform that continuously monitors access‑control data to learn normal patterns and automatically detect anomalous behavior. Its Detection & Response module delivers real‑time alerts with actionable recommendations, while the Insights dashboard visualizes historical movement trends and facility usage, extending existing security hardware without additional sensors.

<problem>Physical security teams often rely on manual review of access control logs and static alarm systems, which can miss subtle anomalies and require significant staff time to monitor and investigate. This leads to delayed responses to security threats and limited visibility into overall security posture.</problem> <solution>Louhe offers an AI-driven platform that continuously analyzes real-time access control events to learn normal patterns and automatically detect anomalous behavior. Its Detection & Response module classifies security incidents and provides actionable alerts for rapid response, while the Insights component aggregates historical data to reveal movement trends, facility usage, and emerging threats. By turning existing security infrastructure into a data-driven system, Louhe enables 24/7 monitoring, reduces reliance on manual oversight, and supports more efficient security operations.</solution> <features> - Real-time machine‑learning analysis of access control events to identify and classify anomalies - Automated alerting with context‑rich recommendations for appropriate response actions - Historical analytics dashboard that visualizes user behavior, perimeter usage, and trend patterns - Integration with existing physical security hardware, extending its capabilities without additional sensors - Continuous learning engine that adapts to evolving security environments and reduces false positives </features> <target_audience>Primary customers are security operations teams and facility managers in enterprises, campuses, and critical infrastructure sites that operate access control systems and need automated threat detection.</target_audience>

What does Louhe do?

Where is Louhe located?

Louhe is based in Oulu, Finland.

When was Louhe founded?

Louhe was founded in 2020.

How much funding has Louhe raised?

Louhe has raised $3.3M.

Who founded Louhe?

Louhe was founded by Janne Pirttilahti.

Janne Pirttilahti - CEO

Location

Oulu, Finland

Founded

2020

Funding

$3.3M

Employees

16 employees

Investors

Lifeline Ventures

Louhe

Oulu, FinlandFounded 202016700+ followers8/10 TractionRelative Traction Score based on online presence metrics compared to companies in the same age group.

Updated 2 months ago

Funding

$3.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Product

Problem

Physical security teams often rely on manual review of access control logs and static alarm systems, which can miss subtle anomalies and require significant staff time to monitor and investigate. This leads to delayed responses to security threats and limited visibility into overall security posture.

Solution

Louhe offers an AI-driven platform that continuously analyzes real-time access control events to learn normal patterns and automatically detect anomalous behavior. Its Detection & Response module classifies security incidents and provides actionable alerts for rapid response, while the Insights component aggregates historical data to reveal movement trends, facility usage, and emerging threats. By turning existing security infrastructure into a data-driven system, Louhe enables 24/7 monitoring, reduces reliance on manual oversight, and supports more efficient security operations.

Target Audience

Primary customers are security operations teams and facility managers in enterprises, campuses, and critical infrastructure sites that operate access control systems and need automated threat detection.

Features

Real-time machine‑learning analysis of access control events to identify and classify anomalies
Automated alerting with context‑rich recommendations for appropriate response actions
Historical analytics dashboard that visualizes user behavior, perimeter usage, and trend patterns
Integration with existing physical security hardware, extending its capabilities without additional sensors
Continuous learning engine that adapts to evolving security environments and reduces false positives