
AISPECO
Manufacturing advanced geo-spatial platforms.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor investor | €0.0 | round |
investor | €0.0 | round | |
investor | €0.0 | round | |
investor | €0.0 | round | |
investor investor investor | €0.0 | round | |
investor investor investor | €0.0 | round | |
* | €2.4m | Grant | |
Total Funding | 000k |
EUR | 2020 | 2021 | 2022 | 2023 | 2024 |
---|---|---|---|---|---|
Revenues | 0000 | 0000 | 0000 | 0000 | 0000 |
% growth | - | 715 % | (10 %) | 64 % | 5 % |
EBITDA | 0000 | 0000 | 0000 | 0000 | 0000 |
% EBITDA margin | (4 %) | (11 %) | (46 %) | - | - |
Profit | 0000 | 0000 | 0000 | 0000 | 0000 |
% profit margin | (4 %) | (9 %) | (45 %) | - | - |
EV | 0000 | 0000 | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 | 0000 | 0000 |
Source: Company filings or news article
Related Content
AISPECO, established in 2013 by co-founders Patrick Rüdisser and Hartmut Runge, operates in the geospatial data collection sector. The firm specializes in manufacturing highly customizable multi-sensor payloads for deployment on airborne, mobile, and stationary platforms. These systems integrate technologies such as LiDAR for laser scanning and hyperspectral imaging, among other capabilities.
The company's core business involves providing hardware solutions to a diverse client base that includes surveying, utilities, forestry, city mapping, mining, and civil engineering firms globally. AISPECO's business model centers on the sale of these advanced sensor platforms. The revenue stream is generated directly from the hardware sales to these various industries, which utilize the technology for monitoring and inspection operations.
The product offering is designed to reduce operational overhead for its clients. By enabling users to operate more efficiently and easily modify sensor configurations, the platforms address the need for flexible and cost-effective data collection methods. This adaptability allows a single payload to be used for different tasks, such as creating detailed 3D models of infrastructure or analyzing the health of vegetation, thereby reducing the need for multiple specialized devices.
Keywords: geospatial data, multi-sensor payloads, LiDAR, hyperspectral imaging, surveying equipment, asset inspection, remote sensing, city mapping, civil engineering, forestry management