First time entrepreneurs saw an opportunity in the dry cleaning on-demand pick up/delivery market and needed the technical expertise to create a mobile app that provides a premium service.
ShareActor’s Resource Platform provided the on-demand business model infrastructure needed to implement the sophisticated resource allocation algorithms required for the application. Our development team layered the brand designs provided by Lavando onto our skeleton app and built the in-app user flow needed to perform the service. This included integration with a delivery provider,quiqup, that connects Lavando’s clients with their laundry processing hubs. As their business grows, since their infrastructure is built with our Platforms, they will be able to leverage the machine-learning driven optimisation tools that are baked into our software if they so choose in the future. It will simply be a matter of switching on the feature once they reach a critical mass of data, sending the insights directly to their management dashboard.
Lavando has a fully built, tested app that is the preferred laundry service of the world’s largest boutique concierge service,Quintessentially. Their experiencing fast growth, are already planning on adding features to the app and looking at strategies for their next round of funding.
WeClean’s founders saw an opportunity to create an on-demand cleaning service that wasn’t present in their marketplace and they needed the digital infrastructure and technical expertise to build it. They had a system that sent emails to the founder and he organized all the cleaners by calling them and sending them out on jobs. As they grew they lost business and customers started to complain.
ShareActor built the infrastructure and the mobile app that weClean used to win the Angel Challenge pitch contest in 2016. Our User Platform and Resource Platform provided them with the user management and resource allocation tools they needed to connect their cleaning partners to their end users. They used the management dashboard to track appointments, ensure they had coverage when it was needed and send automated prompts and messages to both cleaners and customers.
The founders were able to focus on building their business, training their cleaning partners and executing their launch plans while ShareActor provided the technical expertise and on-demand infrastructure to make it possible. They saw steady organic growth until they launched a marketing campaign, resulting in a 15x growth in revenue in their sixth month of operation.
An experienced entrepreneur wanted to build a third party banking app that would provide an invoice payment system in the FinTech market, leveraging new banking regulations in the E.U., and they wanted it ready to launch in time to be early to market.
ShareActor’s User Platform provided the infrastructure that allowed users to sign into the app, quickly create a profile and jump into using the technology in under two minutes. When users take a picture of an invoice, the data is read by our algorithms built on top of Google’s Optical Character Recognition system, and all bank account and invoice identification information is automatically entered into the relevant fields, allowing the user to tap ‘pay now’ or choose a later date upon which to send the invoice payment. Point. Shoot. Pay.
ShareActor built custom features for Payr, creating a payment system that meets all anti-money laundering, data security and other banking regulations, and qualified Payr, as a third party banking provider, to be granted an operating license from the Norwegian Banking Authority.
Payr, free to focus on creating value for users and executing its marketing plan, had a successful launch and saw a quick spike in adoption rates, reaching thousands of users in a few weeks and transferring hundreds of thousands of NOK daily. The success of their launch made it easy to raise another round of funding and put them in a great place to build their next version, offering users better alternatives to the services and products they purchase through machine-learning driven search and recommendation.