boost.ai
![boost.ai Logo boost.ai Logo](/files/vendor/boost.ai6672dbde320dc_1.jpg)
Overview
HQ Location
Norway
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Year Founded
2016
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Company Type
Private
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Revenue
$10-100m
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Employees
51 - 200
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Website
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Company Description
Boost.ai stands at the forefront of enterprise-grade conversational AI. Driven to enable unparalleled interactions between people and organizations, Boost.ai harnesses cutting-edge technology to push the boundaries of AI responsibly. Its proprietary self-learning AI platform empowers businesses to automate interactions at scale, enhancing efficiency and driving positive outcomes.
IoT Snapshot
boost.ai is a provider of Industrial IoT infrastructure as a service (iaas), wearables, and platform as a service (paas) technologies, and also active in the aerospace, automotive, buildings, cement, cities and municipalities, e-commerce, education, finance and insurance, retail, and telecommunications industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
boost.ai’s Technology Stack maps boost.ai’s participation in the infrastructure as a service (iaas), wearables, and platform as a service (paas) IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
Conversational AI Enhances Internal Support and Efficiency at Aker BP
Aker BP, one of Norway’s largest oil exploration and development companies, faced a significant challenge in providing efficient internal support to its approximately 2,000 employees across five offices and various offshore sites. Initially, the company relied on administrative assistants to handle queries related to IT, HR, training, supply chain management, and more. However, as the company grew, this approach became less efficient, with support staff becoming overwhelmed with repetitive tasks and queries. These tasks, while important, consumed significant amounts of time, diverting them from key tasks such as recruitment, training, and organizational development. Aker BP sought to move away from a reliance on human support and aimed to achieve administrative self-service by increasing employee efficiency while maintaining the high level of service they were accustomed to. The company aimed to create a central hub of knowledge that employees could easily access for answers, while still having support staff available for more complex requests.
Case Study
Enhancing Customer Experience in Insurance with Conversational AI
Aspire General Insurance Services, a California-based private passenger auto liability and physical damage carrier, was facing challenges in managing customer service efficiently. The company, which handles all aspects of the insurance process, was relying heavily on human agents for customer interaction and professional conversations across the insurance cycle. This reliance was making optimal customer service cumbersome and time-consuming. The customer service team, including chat services, was supported exclusively by human agents, which limited the resolution time for customer chats and led to elevated wait times for simple customer inquiries. Depending on various factors like staff turnover and external pressures, customers sometimes had to wait for as long as half an hour to be served.
Case Study
Íslandsbanki's AI-Powered Virtual Agent Automates 50% of Chat Traffic in Six Months
Íslandsbanki, one of the three major banks in Iceland, was facing a challenge in managing its customer service. The bank was trying to make the banking experience more digital and less branch-heavy, while simultaneously improving its infrastructure. They identified a growing desire amongst their customer base to interact via online channels. However, during incidents where the bank’s app or website login experienced downtime, calls to the contact center would skyrocket, potentially leaving customers hanging after phone lines closed for the day. The bank did not have a 24/7 call service, and they needed to control what was happening in the call center. They realized that a chatbot could be a viable alternative channel to manage the increasing demand for online chat as opposed to phone calls.
Case Study
Mekonomen's AI-Powered Virtual Agent Revolutionizes Customer Service
Mekonomen, a leading automotive spare parts retailer in Northern Europe, was facing a significant challenge in managing customer inquiries. The company's Swedish operation had recently relaunched its website with a new engine and webshop, aiming to shift 95% of customer contact from phone to online channels. However, the live chat service they had implemented was overwhelmed by the volume of customer inquiries, leading to inefficiencies and long waiting times. The company recognized the need for a solution that could scale their response to the high volume of customer service traffic, while still providing a great customer experience.
Case Study
Transforming Customer Service in Air Travel: A Case Study on PLAY Airlines
The air travel industry has become increasingly complex, with customers demanding instant, personalized, and round-the-clock service. PLAY Airlines, a startup airline based in Iceland, recognized the need for a superior customer service solution that could handle a high volume of inquiries efficiently, provide 24/7 support, and scale up as necessary. The traditional phone-based customer service model was proving inadequate, leading to long wait times and customer dissatisfaction. The challenge was to replace this outdated model with a solution that could offer a frictionless experience, even during peak times or during disruptions like the Covid-19 pandemic.
Case Study
Roskilde Kommune Enhances Citizen Services with Conversational AI
Roskilde Kommune, a municipality in Denmark, was faced with the challenge of efficiently addressing the queries of its over 80,000 inhabitants. The municipality needed a solution that could provide consistent, quality responses to citizen inquiries, while also reducing the need for extensive employee training. The solution also needed to be easily updated and maintained by the municipality's employees, without requiring technical training. Furthermore, the municipality sought to improve the end-user experience through more interactive and readily available customer service.
Case Study
Sector Alarm's Successful Transition to Conversational AI Across Multiple Markets
Sector Alarm, a leading European security company, was facing challenges with its customer service strategy. Despite having a robust omnichannel approach, the company identified inefficiencies in its live chat sessions, particularly in larger markets. Multiple agents were handling each chat session, often dealing with repetitive inquiries such as password resets or product information requests. The company estimated that agents were spending an average of two and a half minutes per chat searching for customers and updating the CRM. Sector Alarm had previously implemented a chatbot solution in 2018, but it was 'tech-heavy' and lacked flexibility. The company found it difficult to make necessary changes and updates without the frequent involvement of the IT department. The initial chatbot solution was not scalable and was unable to meet the growing demands of the company's customer service needs.
Case Study
Advania's Successful Implementation of Conversational AI in Iceland
Advania, a leading Nordic IT services firm, identified a unique opportunity in Iceland's high customer service expectations due to the country's high internet usage. With 98% of Icelandic households being online, the demand for efficient online interaction with companies and government institutions was high. However, the high salary costs in Iceland posed a challenge for companies to quickly scale up and down their customer service as needed. Furthermore, despite the country's inclination towards online communication, chatbots and conversational AI were relatively unheard of due to skepticism about AI's ability to handle the complex Icelandic language. This necessitated Advania to partner with a conversational AI vendor whose Natural Language Processing (NLP) took a language-agnostic approach.
Case Study
Asker kommune Enhances Employee Support with Conversational AI
Asker kommune, a Norwegian municipality with over 6,500 public sector employees, was in search of a digital solution to facilitate the transition after merging with the Hurum and Røyken districts in 2020. The municipality needed a central repository of information that was both easy to access and use for its employees. The challenge was to provide a platform that could answer a wide range of questions related to the merger and other work-related topics, and to do so in a manner that was clear, consistent, and easily understandable by the employees. The solution also needed to be able to make better use of existing information sources within the municipality and provide a common framework of answers to be shared between municipalities.
Case Study
Nordic Bank Nordea Employs Conversational AI to Scale Customer Service Across Four Markets
Nordea, a leading Nordic bank, was facing a significant challenge in managing the growing volume of customer service requests across its operations in Sweden, Denmark, Norway, and Finland. The bank, which serves over 9 million private customers and more than 500,000 active corporate customers, was receiving thousands of customer service requests daily through various channels including in-person, phone, email, and both live and automated chat. In 2017, Nordea’s Norwegian operation was receiving upwards of 2 million contacts per year across three contact center locations staffed by approximately 150 FTEs. The bank needed a scalable solution to manage this volume while providing consistent and on-brand experiences. They also identified a preference among their customer base to interact via chat, which had the advantage of handling multiple inquiries simultaneously.