The company works with airlines such as Air France Industries-KLM and aircraft manufacturers such as Dassault Aviation and is one of the players in the field of aeronautical maintenance automation.

Aircraft manufacturers, such as Airbus, Boeing and ATR, and certification bodies, such as the Federal Aviation Administration (FAA) and the European Aviation Safety Agency (EASA), require regular visual inspections of the entire external surface of the aircraft to assess the condition of their structures.

[2] All aircraft are visually inspected prior to each flight, as part of scheduled maintenance operations and after unplanned events such as a lightning strike, hail storm or other possible external damage.

One possible solution to improve the traceability of these operations and reduce costs is the robotization of aeronautical maintenance and its visual inspections.

Carried out by the Akka Technologies group, this multi-partner project involved research laboratories and companies, including Airbus.

[5][6] In 2014, in partnership with the Bristol Robotics Laboratory, the British airline easyJet became interested in drones guided by technicians to reduce the inspection time of aircraft fuselages.

[7][1] After thirteen years in the design offices of the European aircraft manufacturer Airbus on the A400M and A350 aircraft with metal and composite materials,[9][10][11] Yann Bruner, an engineer at Mines ParisTech with a PhD in Mechanics and Materials, noted that maintenance inspection reports are often incomplete for various reasons such as missing photographs, missing information, or illegible handwriting.

[8] They founded the startup, Donecle, in September 2015 and developed an automated inspection procedure for aircraft with a swarm of UAVs.

A human operator chooses a flight plan for the required inspection and a qualified inspector then validates the reports.

[33][34] In the future, the company intends to offer other types of inspections, such as quality control of exterior paint and the evaluation of corrosion.

[9][18] The sensors used for autonomous navigation also ensure safe operation, by preventing collisions with aircraft, human personnel and equipment.

[37] The navigation plans and the number of UAVs employed in each case depend on the aircraft model to be analyzed.

A second step of classification is then carried out in order to categorize defects (lightning strike, oil leak, scratching, texture irregularity, etc.)

Databases suffer from the fact that there is only a small number of defects compared to the huge amount of normal elements present on an aircraft.