Roadmap to AI

TAILORED AI SOLUTIONS

Customizable AI solutions and infrastructure with support and maintenance that you can count on.

CASE STUDIES

WORLD-CLASS AI EXPERTISE

Build AI solutions with top-notch AI experts or complete teams for product development or R&D.

expertise

COMPUTER
VISION

Object recognition | Image classification | Image segmentation | Image & object annotation

Get insights from automated image analysis

Build autonomous vehicles and robot guidance with machine vision

Augment human intelligence with video-based AI solutions

NATURAL
LANGUAGE
PROCESSING

Word & document classification | Text tagging & parsing | Machine translation | Speech recognition

Extract information from unstructured data

Augment RPA applications with learning language processing

Remove clerical bottlenecks, augment and empower experts

MACHINE
LEARNING

Supervised learning | Unsupervised learning | Reinforcement learning | Transfer & active learning

Understand, decide and predict in highly complex systems

Provide human experts with machine-driven support

Extract value out of vast, complex combinations of data

SOLUTION DESIGN & DEVELOPMENT

Rapid prototyping | Data engineering & architecture | Integration planning & implementation | Product & system maintenance

Deliver turnkey solutions for AI-augmented business

Human-machine collaboration for efficient knowledge work

Integrate AI seamlessly into  current ways of working

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VISION AI

Framework for machine learning and computer vision covering sensor fusion, object and image classification and segmentation, and environment modeling. 

DOCUMENT AI

Framework for machine learning and language processing covering word and document classification, text tagging and parsing, OCR and speech recognition.

INFRASTRUCTURE

Rapid validation and prototyping without major upfront investments.

RAPID POC

Get started with a focus on machine learning models and business logic.

INFRASTRUCTURE TO SPEED UP AI DEVELOPMENT

QUICK START

Rapid validation and prototyping without major upfront investments.

REDUCED COST

Get started with a focus on machine learning models and business logic.

IT ECOSYSTEM INTEGRATION

End-to-end solutions to integrate into existing business processes and IT ecosystem.

MANAGED HOSTING

Our hosting makes sure the solution runs 24/7 with necessary computing resources.

MODULAR SOLUTION

Evolve individual components easily with versioning and deployment capabilities

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Case Studies

FLIGHT DELAY PREDICTION

Improved situational awareness of air traffic for operations control center

More intelligent and efficient flight management

More time to react and prioritize for experts in the  operations control centre

Improves safety  and customer satisfaction

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WATER QUALITY PREDICTION

Smart water management that helps predict chemicals in water treatment

Improved forecasts and new insights about water quality

More intelligent data processing allows humans to focus on more value creating work

More efficient and accurate support for Ramboll’s clients

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AI-driven Business

Machine & Process industries

Predictive maintenance

digital twins

Visual quality control

Central banks & regulators

One objective that central banks have is to identify bank distress and bank vulnerability. A Machine Learning model can analyse data from historical distress events, and take into account more than a thousand risk indicators and bank interconnectedness factors. These types of solutions can be used at central banks to help financial analysts and regulators. A model we created for the European Central Bank demonstrated as much accuracy in its predictions as 7000 human analysts, outperforming all existing systems.

AUTONOMOUS VEHICLES

Computer vision

Sensor fusion and perception

object detection and tracking

Retail & Consumer services

Consumers are expecting increasingly more personalised shopping experience, especially when it comes to online shopping. If they don’t see something relevant for them immediately, chances are they’re going to leave. Machine Learning based recommendations systems can be used to tailor and personalise the shopping experience for each individual based on the consumers’ past interests and actions. This enables intelligent targeting not only within web but also through different marketing campaigns.

Energy &
infrastructure

Visual quality control

Energy consumption prediction

Private Financial Institutions

When making investment decisions, financial experts need to look for risks and opportunities by reading hundred-page documents, a time-consuming process prone to human error. An Natural Language Processing based AI model can learn the semantics behind key events and facts, and automatically extract the key risk-indicators for financial experts to evaluate. The AI generates a summarisation of its findings to be validated, improving process throughput time and gathering the entire department’s input into a single learning system.

MARITIME

Autonomous vessels and ports

Object detection

intelligent awareness

Impact & Sustainability

Impact investing – funding projects that generate investment returns, alongside socially and environmentally beneficial outcomes – holds many opportunities for AI. For example, Natural Language Processing and other AI techniques can be used to analyse a large variety of data sources for pre-investment vetting. Investing becomes more data-driven when scraping, aggregating and analysing the data regarding potential target investments is systematic. Also, a major manual bottleneck in the pre-investment vetting process is removed.

Mobile & telecom

network optimization & planning

network capacity prediction

Central banks & regulators

One objective that central banks have is to identify bank distress and bank vulnerability. A Machine Learning model can analyse data from historical distress events, and take into account more than a thousand risk indicators and bank interconnectedness factors. These types of solutions can be used at central banks to help financial analysts and regulators. A model we created for the European Central Bank demonstrated as much accuracy in its predictions as 7000 human analysts, outperforming all existing systems.

Internet & games

Product recommendation

DYNAMIC PRICING

Retail & Consumer services

Consumers are expecting increasingly more personalised shopping experience, especially when it comes to online shopping. If they don’t see something relevant for them immediately, chances are they’re going to leave. Machine Learning based recommendations systems can be used to tailor and personalise the shopping experience for each individual based on the consumers’ past interests and actions. This enables intelligent targeting not only within web but also through different marketing campaigns.

Smart devices

iot device security

Private Financial Institutions

When making investment decisions, financial experts need to look for risks and opportunities by reading hundred-page documents, a time-consuming process prone to human error. An Natural Language Processing based AI model can learn the semantics behind key events and facts, and automatically extract the key risk-indicators for financial experts to evaluate. The AI generates a summarisation of its findings to be validated, improving process throughput time and gathering the entire department’s input into a single learning system.

Medical & pharma

Support cognitive & diagnostic work

Computer vision aided surgery

Impact & Sustainability

Impact investing – funding projects that generate investment returns, alongside socially and environmentally beneficial outcomes – holds many opportunities for AI. For example, Natural Language Processing and other AI techniques can be used to analyse a large variety of data sources for pre-investment vetting. Investing becomes more data-driven when scraping, aggregating and analysing the data regarding potential target investments is systematic. Also, a major manual bottleneck in the pre-investment vetting process is removed.

Financial industry

NLP based document AI

Smart text analysis

Risk categorization

Central banks & regulators

One objective that central banks have is to identify bank distress and bank vulnerability. A Machine Learning model can analyse data from historical distress events, and take into account more than a thousand risk indicators and bank interconnectedness factors. These types of solutions can be used at central banks to help financial analysts and regulators. A model we created for the European Central Bank demonstrated as much accuracy in its predictions as 7000 human analysts, outperforming all existing systems.

Retail & Logistics

Product recommendation

DYNAMIC PRICING

parcel delay prediction

Retail & Consumer services

Consumers are expecting increasingly more personalised shopping experience, especially when it comes to online shopping. If they don’t see something relevant for them immediately, chances are they’re going to leave. Machine Learning based recommendations systems can be used to tailor and personalise the shopping experience for each individual based on the consumers’ past interests and actions. This enables intelligent targeting not only within web but also through different marketing campaigns.

Legal industry

Legal clause recommender

Smart document automation

Private Financial Institutions

When making investment decisions, financial experts need to look for risks and opportunities by reading hundred-page documents, a time-consuming process prone to human error. An Natural Language Processing based AI model can learn the semantics behind key events and facts, and automatically extract the key risk-indicators for financial experts to evaluate. The AI generates a summarisation of its findings to be validated, improving process throughput time and gathering the entire department’s input into a single learning system.

Advertising & Media

Real-time bidding

online experience optimization

Impact & Sustainability

Impact investing – funding projects that generate investment returns, alongside socially and environmentally beneficial outcomes – holds many opportunities for AI. For example, Natural Language Processing and other AI techniques can be used to analyse a large variety of data sources for pre-investment vetting. Investing becomes more data-driven when scraping, aggregating and analysing the data regarding potential target investments is systematic. Also, a major manual bottleneck in the pre-investment vetting process is removed.

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WHITE PAPERS

EXPLORE THE PRACTICAL USE OF AI AND MACHINE LEARNING TODAY

AI & RECRUITMENT - A VISION OF HOW AI IS IMPACTING RECRUITMENT TODAY

AI is becoming the de-facto technology that will augment all human driven operations in the near future. Today, the main source of AI-driven competitive advantage is by bringing an AI to work in human processes, and increasing every human operator’s efficiency by offloading low value yielding tasks to its learning automation. In this report Silo.AI will present an overview of AI’s impact to recruitment in large scale.

Download white paper

AI IN RETAIL - HOW TO USE AI TO IMPROVE PROFIT RETENTION AGAINST FRAUD IN THE RETAIL WORLD

The world of retail has been among the first to see AI- driven change take place in the everyday life of the consumer. Companies like Amazon and Alibaba have paved the way for changes in both the digital and the physical world. Not surprisingly, they are also considered to be among the frontrunners of Artificial Intelligence (AI). The first step into AI-driven transformation, however, is not to radically alter our shopping experiences with new digital pathways but to augment pre-existing ways of doing work with elevated levels of automation. This white paper is intended to outline how AI-augmented business produces measurable business benefit in both the digital and the physical world.

Download white paper

AI AND TRADITIONAL ANALYTICS – UNDERSTAND WHEN AND WHERE TO USE MACHINE LEARNING

Artificial intelligence and machine learning can complement and co-exist with the existing analytics methods based on rules, optimisation, simulation and statistical analysis. However, it is crucial to understand where and when it makes sense to implement AI solutions, and when it is better to use traditional analytics. After reading the white paper, you will understand:

  1. What AI and machine learning means in practice
  2. How using AI is different from rule-based systems, optimisation, simulation and statistical analysis
  3. How to implement data driven decision making with machine learning, and
  4. What type of use cases can be served with machine learning.
Download white paper

Get the white paper(s)

We serve organisations with different AI maturity levels.

Whether you want to get started with AI or expand your existing AI operations, build competitive advantage for your business with cutting edge artificial intelligence.

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