Amiral Technologies
Provides predictive maintenance solutions.
| Date | Investors | Amount | Round |
|---|---|---|---|
| investor | €0.0 | round | |
€2.8m | Early VC | ||
| Total Funding | 000k | ||
| EUR | 2021 | 2022 |
|---|---|---|
| Revenues | 0000 | 0000 |
| % growth | - | 91 % |
| EBITDA | 0000 | 0000 |
| % EBITDA margin | 10 % | - |
| Profit | 0000 | 0000 |
| % profit margin | (43 %) | 28 % |
| EV | 0000 | 0000 |
| EV / revenue | 00.0x | 00.0x |
| EV / EBITDA | 00.0x | 00.0x |
| R&D budget | 0000 | 0000 |
Source: Company filings or news article
Related Content
Founded in 2018, Amiral Technologies emerged from over two decades of academic research conducted at the GIPSA-lab (CNRS) in Grenoble, a prominent French research institution. The company was co-founded by a team with deep expertise in signal processing and artificial intelligence, including CEO Katia Hilal, who holds a PhD in automatic control and signal processing and has a background in business development, and CTO Jean-Philippe Lauffenburger, also a PhD with extensive research experience in the field. This foundation in advanced research underpins the company's core technology.
Amiral Technologies operates in the predictive maintenance market, providing software solutions for industrial equipment manufacturers and operators in sectors such as energy and transportation. The company's business model is centered on licensing its software, which helps clients anticipate and manage equipment failures. By integrating Amiral's solutions, these industrial players can shift from costly corrective or systematic maintenance schedules to a more efficient, condition-based approach, ultimately reducing operational downtime and maintenance expenses.
The company's core offering is a suite of algorithms and software tools designed to analyze data from industrial sensors. This technology automatically extracts relevant health indicators from raw data to build robust predictive models. A key feature of their solution is its ability to function effectively even with limited historical failure data, a common challenge in industrial settings. This allows for the early detection of anomalies and the prediction of the remaining useful life (RUL) of critical components, enabling proactive maintenance interventions. The software is designed to be deployed both in the cloud and on-premise, offering flexibility to its industrial clientele.
Keywords: predictive maintenance, industrial analytics, condition monitoring, machine learning, asset performance management, industrial AI, fault detection, remaining useful life, signal processing, cleantech