Getting Started with AI

You know your business will be transformed by AI soon. That is why you’re here.

What you might not know is how to get started, what AI can be used for in your business, or how to implement a particular machine learning algorithm. No need to worry: We have 130 AI and Machine Learning experts to help you.

Our Getting Started with AI package is designed to show you how you can use artificial intelligence to automate, maximise efficiencies and generate value within your company.

Mission

For Clients

Are you making the most of your data? Work with a machine learning expert on-demand. We are here to help you innovate by providing you with access to an exclusive network of data scientists.

There is currently much uncertainty about what constitutes a good data scientist. At the same time, organizations need to harness the power of their data in order to stay competitive. Brainpool wants to fill that gap.

We are a network of experienced data scientists from world-class institutes who are ready to help you in this journey.

For Data Scientists

Brainpool provides top academics and experienced professionals in data science with a platform to locate exciting industry projects on contract basis. Network with other experts.

Be an expert adviser for our clients who are working on everything ranging from recommendation engines to deep learning problems, optimization problems, data visualizations and social network analysis, to name a few.

Receive great compensation for high quality work. Immediate rewards. We encourage outside-the-box thinking among academics.

Our Services

Finance

Unlock consistent alpha by finding hidden patterns in financial time series.

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Marketing

Tap in to unexplored markets by having a deeper understanding of public opinion.

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Retail

We help clients turn massive customer data into insights and achieve outstanding retail performance with advanced analytics.

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Healthcare

Make healthcare work efficiently for everyone.

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Finance

Predictive Analytics: 
Build supervised algorithms that predict future prices and liquidity
movements, by extracting hidden features within data. Building
flexible models that learn from new trends by adapting to the free
flow market information. Determine assets that are mispriced as
found in niche markets.
Risk Management: Improve the accuracy of risk modelling by identifying key data features and nonlinear patterns within large datasets. Enhance your decision making with well-trained deep learning models, that ensure a more efficient means to manage risk. Build early warning systems that automate reporting, portfolio monitoring, and contingency plans.
Portfolio Optimization: Construct well-diversified portfolios using unsupervised learning, such as cluster analysis to identify assets with similar characteristics, and reinforcement learning to allocate capital dynamically in an ever changing environment. Use regularisation and semi-supervised learning for portfolios with label data more expensive to assemble.
Natural Language Processing (NLP): Make use of NLP to more intricately assess value in investment strategies such as long/short equity and discretionary with models that more accurately gauge market sentiment. Provide highly relevant answers to questions related to financial planning.
Fraud Detection and Identity Management: Machine learning in fraud detection has become the go-to technology for classification of fraudulent behaviour. Supervised learning methods such as neural networks have shown to be very effective at detecting fraud when trained on a large amount of financial statement data.
Credit-rating: Determine the credit-rating of a borrower from social media activities and transaction history. Build new credit markets with customer and clients that lack a long-term credit history. Use supervised learning to more accurately assess companies’ credit scores, e.g., using neural networks.

Marketing

Customer Segmentation: 
Improve customer segments and targeted advertising using machine
learning segmentation methods (e.g. cluster analysis, k-means,
Nearest Neighbour). Classify customers using Supervised Learning
Models, find new audiences using recommendation systems increase
efficiency of your media spend.
Behavioural Analysis: Find patterns in the way customers interact with your brand by using predictive modelling and forecasting. Optimise conversions, increase customer satisfaction.
Social Media - Early Opportunity Detection: Analyse real-time Twitter and Facebook data streams to capture current sentiments with respect to brands, products or adverts. Get a head start on sentiment outbreaks to uncover important opportunities and avoid PR crises.
Sales and Marketing Integration: Build an easy to navigate interface to measure an integrated impact of sales teams and marketing campaigns. Track direct correlation between media budgets and number of products sold.
Influencers Strategy: Improve efficiency of your campaigns using social network analysis. Find audience most susceptible to the message, and use this audience to amplify the impact of your campaigns. Find and target.
Implicit Survey Design: Improve accuracy of surveys by using established psychological tools and test instruments, such as gamification. Learn about your audience to make informed decisions.
Neuroscience (Beta): Optimise website usability and impact using eye tracking methods to. Investigate consumer’s perception of adverts using brain imagining (FMRI) analysis.

Retail

Predictive Sales: 
Build self-learning models that predict sales, help increase sales
revenue, and reduce storage costs. Using Linear Latent Variable
Models (LAVA) and/or Elastic Nets to estimate the latent factors
that highlight customers purchasing behaviour.
Big Data Analytics and Visualisation: Systematic analysis of big data is crucial when exploring under-performing streams of sell revenue. By deploying a combination of large scale analytics and data visualisation we can illuminate hidden campaign strategies, such as cross sales, which will alleviate such poorly performing skus.
Supply Chain: Implement statistical models with demand and supply uncertainty features that are inherent to the supply chain process. The perturbation of these model treat hidden externalities and generate a robust toolkit for modelling supply chain. Some additional areas where machine learning could help you’re your business is planning group problems, optimising stock levels, and warehouse automation.
Backtesting Campaign Strategies: Campaigns can be costly if they are not implemented correctly, and thoroughly backtested. Finely tuning tune back-testing models will help build a well-constructed cost effective campaign strategies, giving management at all levels the details and implications for deployment.
Targeted Campaign and Retail Segmentation: Have a nuanced view of public opinion and target customers more accurately with Multi-level Regression and Poststratification (MRP). Create retail segmentation with artificial neural networks (ANNs) giving you a better understanding of your customers shopping habits.

Healthcare

Healthcare bots: 
Improve your customer service with specialized chat bots that interact
with patients through chat windows. Automate scheduling follow-up
appointments with patients. Minimise human error by ensuring they are
directed to the appropriate healthcare department, and reduce kpi times.
Disease Identification/Diagnosis: Build state of the art classification algorithm for diagnosing patients based on mere mobile phone photos. Identify rare diseases with learning algorithms such as functional-gradient boosting (FGB), which self-report behavioural data to allow distinguishing between people with rare and more common chronic illnesses.
Personalized Treatment: Supervised learning will allow physicians to select from more limited sets of diagnoses. An example of this is the estimation of patient risk factors relative to symptoms and genetic information. Such models can be calibrated and trained on micro biosensors and mobile phone applications which will give more sophisticated health data to assess treatment efficacy. Reduce treatment cost and optimize individual patent health.
Drug Discovery: Machine learning in early-stage drug discovery can be used to estimate the success rate of initial screening of drug compounds relative to biological factors. The application of unsupervised learning (k-nearest neighbor algorithm) to precision medicine has identified mechanisms in multi-factor diseases, and created alternative treatments and therapies.
Clinical Trial Research: Selecting and identifying ideal candidates for clinical trails by sampling from a broader range of data to find features that are currently underutilised, an example of this could be social media and number of doctor visits. Use machine learning to improve the safety of the trialists by monitoring their health in real-time remotely.
Epidemic Outbreak Prediction: The monitoring and predicting of epidemic outbreaks has been performed successful by machine learning technologies for a number of years now. Collecting vast amounts of data from satellites, historical healthcare databases, and social media; one can train support vector machines and deep neural networks potential outbreaks such as malaria and ebola.

Who we work with

Brainpool gave us the expertise we needed to complete an urgent project for one of our biggest clients. It is our go-to place for AI and Machine Learning experts.

Justin Ibbett , CEO at Focal Data