Founder / CEO
IPercept
IPercept provides an AI-powered predictive maintenance platform that monitors CNC machine health using motion pattern recognition and behavioral analysis. The solution deploys as a software update on existing hardware without controller integration, detecting component-level degradation in bearings, ballscrews, guides, and spindles. It delivers actionable insights within days across 50+ machine brands, with documented savings from €10,000 to €180,000 per incident.
- Manufacturing / Industry 4.0
- Artificial Intelligence
- Internet of Things
- Data & Analytics
- Software Only
- Industrial Automation
Updated 1 month ago
Funding
Raised to date
$23.6MRaised 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.
across 3 rounds
BI+2$16.5M · Series A
Announced Sep 2026 · from article date
Disclosed cumulative: $23.6M
3/3
RoundDateAmountValuationInvestors
Founders
Product
Problem
Manufacturers often face unexpected downtime and high maintenance costs due to the inability to accurately predict machine failures. Existing monitoring solutions lack the granularity needed to detect subtle signs of degradation, leading to reactive maintenance and inefficient resource allocation. This results in reduced productivity, increased expenses, and a shortened lifespan for critical machine components.
Solution
IPercept offers a predictive maintenance solution that uses AI-powered analytics and IoT sensors to monitor the health and performance of industrial machines, specifically CNC machines. By analyzing motion patterns and operational behavior, IPercept identifies early signs of wear, misalignment, and other degradation trends. The system works without controller integration or IT projects, deploying as a software update on an already-installed device, and delivers actionable insights within days. Component-level intelligence covers bearings, ballscrews, guides, and spindles—each with its own trend and recommended action—enabling manufacturers to proactively address potential issues before they escalate into costly breakdowns. This empowers maintenance teams to optimize resource allocation, reduce downtime, and extend the lifespan of critical machine parts.
Target Audience
The primary target audience includes maintenance and production teams in manufacturing facilities, particularly those utilizing CNC machines across aerospace, automotive, and equipment manufacturing industries, who seek to improve machine reliability, optimize maintenance schedules, and reduce unplanned downtime.
Features
- Condition monitoring based on integrated motion pattern recognition for spindles, rotary axes, and linear axes
- Operation monitoring based on integrated behavioral profile analysis for utilization, load intensity, collision, and process monitoring
- AI-driven insights into wear, misalignment, and degradation trends, providing 1,000 times more accurate data than existing solutions
- Automated identification of collisions with classification to severity levels
- Process monitoring for automated identification of abnormalities and corresponding root causes
- Integration independent of machine brand, controller type, or IT infrastructure, compatible with 50+ machine brands
- Predictive maintenance capabilities to map gradual and sudden machine degradation without requiring historical data
- Web interface providing clear, ready-to-use insights with component-specific trend lines and recommended actions