Use cases

How to use Aito in your projects.

Benefit from using Aito

Aito is for those who need fast-to-deploy non-narrow machine learning. For developers that care about fast time to market and have no access to data science resources. Or simply are after best bang for their buck and want to maximize their AI ROI.

Aito is faster to use than TensorFlow, more intelligent than Elasticsearch, yet easy and familiar like SQL.

IKEA demand simulator prototype in 5 weeks

As part of the IKEA Open Innovation program 2019, a demand simulator prototype was created for IKEA.

Demand simulator prototype allows IKEA’s demand planners to quickly test different hypotheses on how product features (materials, colours etc) will influence the demand of unlaunched articles and why. Using Aito, IKEA had feedback from real users to support their further investment decision.

Aito’s predictive database powered the prototype through SQL-like queries. A fully functional solution was built in only 5 weeks.

Data sources

Product data

Sales data

Ikea demand simulator

Churn prediction prototype for Freska

Aito's predictive database can be used to predict churn from time series and, apart from the predictive part, will also return explainable results.

Freska, a modern home cleaning company based in Finland, Sweden and Norway, have been using an RFM (recency, frequency & monetary) model to predict its customer churn so far. They started a POC (proof of concept) to experiment if a better churn accuracy would be reached with Aito’s predictive database. These explainable results will help to anticipate and to decrease the churn rate.

Aito’s predictive database, except for doing the predictive part, will also return explainable results - as in, giving you the reason why a certain customer might be churning. Read more about it on our blog post: Using time series forecasting for predicting Freska's customer churn.

Using Aito resulted in a +10% gain in churn prediction accuracy. The explainability helped to provide insights on why a customer might be churned and direction on how to continuously improve the model.

Data sources

Satisfaction metrics

Marketing data

Booking details

freska result

Empowering customer shopping experience

A.S.Helsingö, a Finnish design brand, offers high-quality kitchens, wardrobes, and sideboards built on IKEA cabinet frames. They believe that everyone deserves the joy of a beautiful, personal kitchen and home – for an affordable price tag.

As a digital-first brand, that interacts with customers primarily online, A.S.Helsingö has an extreme focus on continuously improving the shopping experience. They selected Aito, the predictive database, to do the statistical heavy lifting needed to improve the experience related to price quotations requests.

Using Aito to automate the process of price quotations online resulted in faster response times and improved customer service. The service tool also saves time and effort for A.S.Helsingö’s employees.

Data sources

Customer data

Product prices

helsingo result

Combining dynamic datasources to generate smart search prototype

For a fast-growing global consultancy company like Futurice, with more than 500 people across 7 offices, knowledge sharing is both crucial and challenging. It is not evident to get easy access to heterogeneous and scattered documentation throughout your organisation. Think of internal chat channels, online tutorials, customer support issues, project management information.

Futurice set up an experiment to see if AI could be used easily and quickly to bring transparency to these numerous dynamic data sources and as such, de-mystify the problem of who knows what/who has already done what within a big company.

Read more about it on our blog post: How agile AI experiments can help to identify who knows what within big organisations.

Aito's predictive database enabled quick testing of whether data could provide insightful results to help resolve the issue of knowledge management.

Data sources

Trello

Slack

Github

smart search UI

Invoice automation prototype in 2 days

Aito's predictive database can be used to deploy Robotic Process Automation (RPA), automating repetitive tasks such as invoice handling. And consequently reducing the chance of mistakes and improving process efficiency. Together with Sisua Digital, we set up an invoice automation prototype in only 2 days.

For the prototype, we had 250 training examples, resulting in an 77% accuracy rate in invoice categorization. More details on the blog post: Using a predictive database for AI-enabled RPA.

Free employees from error-prone chores and focus on the core tasks within your organisation.

Data sources

Invoices

Korkia UI

High-speed lead qualification using AI

Intwixt helps businesses become faster, more efficient and transparent through intelligent process automation. Intwixt CAP (Conversational Automation Platform) turns team communication apps (Slack, etc.) into business process engines. Based on research from HBR and MIT, Intwixt DealFlow empowers companies to do real-time lead qualification and assignment while not leaving their favorite communication environment.

Aito facilitates the recommendation engine to match a sales rep to an incoming lead. The engine assists the sales manager to make faster decisions and shortens the time to contact a lead, which is critical for sales. The ML model used in DealFlow is customizable, so it can be extended with customer’s own proprietary information when requested.

Read more on how Aito can be used in lead assigment on our blog post.

Intwixt DealFlow with Aito significantly shortens the time for lead qualification.

Data sources

Hubspot

Intercom

Slack

Intwixt DealFlow UI

More details

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