AI DevOps in the restaurants business

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HI Iberia has been a provider for some years of restaurants and catering Point of Sale systems (software controlling the sales, stocks, and other aspects of these businesses) called HIPPOS which is used in 40+ installations around the whole Spain.

The software is customised for each restaurant/restaurant chain and the process of maintaining the diverse code base and delivering updates and debugging errors, etc. is done manually by a small dedicated team at the company. They agree that they could benefit from a more streamlined process such as DevOps to organize this and use aspects of AI to automate many of the tasks and hence rely less on expensive and cumbersome alert systems.

The maintenance and updates of the system most often involves visits to the deployment sites which are usually out of Madrid, so it involves expensive and time consuming travels of the development team members. The current size of the team will grow rapidly and expenses accumulate for any enlargement of the customer base. It is expected that with the efficiency of AI enabled tools and proper AIDOaRT methodology the team will be more capable of servicing a larger installation base. The HI Iberia product team (HIPPOS) is the provider of the POS software. The restaurant chains would not be directly involved during the project but some installation sites would be chosen as testbeds for any advancements that AIDOaRT provides. HI Ibera R&D team would be developing tools based on AIDOaRT progress. The rest of the consortium would be providing tools and methodologies that the use case could profit. The overall business goal is to reduce load of maintenance, bug fixes and system updates to the development team of HIPPOS. Predictive maintenance would be good to have. Coherent unified management of the different instances of the customized platform would be welcome.

Moreover, HI Iberia identified the following tasks:

  1. Identification of current methodology and inefficiency points.
  2. Design of AI solutions to:(a) detect errors and point to possible issues, (b) automate the update and in-situ testing of customized solutions, (c) provide means for a semi-automated management of the code base for all of the customized variants, (d) any other detected action points in one)
  3. Implementation of tools and adaptation of the team methodology to AIDOaRT tools and workprocesses.
  4. Evaluation of the solutions and calculation of benefits.