Service Intelligence: App for vehicle maintenance.

Our client launched an app for vehicle maintenance to build mutual trust between all market players. Aftermarket vehicle maintenance in the USA is hard on both customers and service companies: vehicle owners tend to believe that regular service-check notifications work mainly for parts suppliers and service providers. Meanwhile, under-performed maintenance costs the US automotive industry roughly $60 billion per year, a colossal loss driven by that lack of trust.

Is there a win-win solution that revives trust? Jorge H. Antico, founder of eAutoClub, Inc., decided there was and launched Service Intelligence, a powerful data-mining tool that automatically finds any service an automobile needs. Its main feature is a smart, flexible approach to the individual needs of each vehicle owner. The key idea: the service-writing process usually lacks an analysis of previously installed parts and fluids, so it cannot determine recurring service needs objectively.

The application examines how a vehicle is actually used and determines its real service needs based on its condition, rather than relying on formal metrics or assumptions made by managers. That benefits both vehicle owners and service providers, including parts and lubricant distributors and service stations. Since the solution launched, vehicle owners have become more accepting of service or part substitutions because they receive trustworthy justifications, which in turn benefits repair shops and service providers.

To stay competitive, the application had to be thoroughly refined at its core and integrate seamlessly with the wide range of CRM and platform services that service providers use.

Parts shops and service stations commonly rely on widely recognized CRM and warehouse management systems (WMS), so Service Intelligence needed the connectors to exchange information with those platforms. The main challenge was safely connecting Service Intelligence via APIs to the databases of the clients’ platforms and ensuring correct synchronization.

When our team started, Service Intelligence was only a desktop application. Running it on remote client machines required a significant amount of support. The client wanted to migrate from desktop to a cloud web application with a “thin client” responsible for connecting to the shop’s CRM systems.

As an outcome of modernizing the system and launching the app for vehicle maintenance, stakeholders expected to deploy a condition-based platform that tracks the entire life-cycle of every part, lubricant, and fluid. Setup is quick and populates from Point-of-Sale service history. Expected capabilities included:

• Extensive reports covering what is due, not due, or never done for the vehicle.
• Condition-based marketing. Service Intelligence ages the service history based on vehicle-specific daily usage for fair, trustworthy service recommendations.
• Contact automation, including the ability to send reports on a vehicle’s state to its owner.
• The ability to send thank-yous, declined-work notices, appointment and service reminders, newsletters, review requests, and promos, with custom logo, branding, messages, and pictures.
• Promoting a service only to vehicles that need it, with the option to pick a theme and customize the message.

• Deep integration: deep integration of the Service Intelligence platform with third-party services and platforms.

• Integration layer: to ensure smooth data exchange, our engineers developed the integration layer (middleware) that bridges the application and the third-party services and platforms.

• New SaaS architecture: our team designed a completely new SaaS architecture for Service Intelligence and developed a scalable multitenant web application.

• Cloud migration: migration from a desktop to a cloud web application with a “thin client” responsible for connecting to the shop’s CRM systems.

unicrew engineers took responsibility for the deep integration of the Service Intelligence platform with third-party services and platforms. We ran a thorough needs assessment to determine the specific requirements and objectives of the integration, working out what data and functionality needed to be shared between Service Intelligence and the services and platforms our client’s customers use. Identifying the APIs or integration tools provided by those third-party platforms let us outline the right architecture, which in turn ensured the application could send and receive data in the required formats.

We developed an integration plan that outlined the data flow and the points to be synchronized, and set rules for data validation, transformation, and synchronization.

To ensure smooth data exchange, our engineers built the integration layer (middleware) that bridges the application and the third-party services and platforms. These connectors handle data transformation and communication.

Our team designed a completely new SaaS architecture for Service Intelligence and developed a scalable multitenant web application. In parallel, we built a “thin client” that enabled smooth data synchronization between the client data (located on the client’s machines) and the web application.