ExistBI’s Machine Learning consulting services division is experienced in applying AI and Machine Learning to business problems. We’ve worked with big enterprises, medium-sized companies and venture backed start-ups in the US, Canada, UK, Germany and Croatia.

Below are some examples of how machine learning can help your operations, sales/marketing, finance, human resource and product & innovation teams:

How can machine learning help your operations?

Machine learning consulting companies can help improve your operations in a number of ways. For example, it can be used to optimize supply chain management, predict equipment failures, or improve customer service. Companies can also use machine learning to analyze data from various sources to identify patterns, trends, and insights that can be used to make better business decisions. Additionally, machine learning can be used for automation of process, which can improve efficiency, reduce costs, and increase productivity.

How can a machine learning engineer help your sales team?

  1. Lead scoring and prioritization: Machine learning algorithms can analyze large amounts of customer data to identify patterns and predict which leads are most likely to convert. This allows sales teams to focus their efforts on the most promising leads and prioritize their follow-up.
  2. Sales forecasting: Machine learning algorithms can analyze historical sales data and market trends to forecast future sales. This helps sales teams set realistic targets and plan their activities accordingly.
  3. Personalization: Machine learning algorithms can analyze customer data to identify individual preferences, behavior patterns, and buying habits. This information can be used to personalize sales and marketing messages, leading to higher engagement and conversion rates.
  4. Sales process optimization: Machine learning algorithms can analyze sales data to identify areas where the sales process can be optimized. This can help sales teams improve their efficiency, reduce the time it takes to close deals, and ultimately increase revenue.
  5. Customer retention: Machine learning algorithms can analyze customer behavior to identify signs of churn, such as decreased engagement or reduced spending. This allows sales teams to take proactive measures to retain customers, such as offering promotions or personalized outreach.

How can a machine learning consultant help your finance team?

  1. Fraud detection: Machine learning algorithms can analyze large amounts of financial data to detect patterns and anomalies that may indicate fraudulent activity, helping finance teams to prevent fraud before it occurs.
  2. Risk management: Machine learning can help finance teams to identify and assess potential risks in their investments or lending portfolios. By using predictive modeling, machine learning can identify trends, forecast outcomes, and help to mitigate risks.
  3. Credit scoring: Machine learning can help finance teams to develop more accurate credit scoring models. By analyzing large amounts of data and identifying patterns, machine learning algorithms can help finance teams to make more informed decisions about lending and credit.
  4. Financial forecasting: Machine learning can help finance teams to predict future financial outcomes, such as cash flow, revenue, and expenses. By analyzing historical data and identifying patterns, machine learning algorithms can help finance teams to make more accurate financial forecasts.
  5. Process automation: Machine learning can help finance teams to automate routine tasks such as data entry, report generation, and invoice processing, allowing them to focus on more strategic and value-added activities.

How can machine learning consulting companies help your HR team?

  1. Recruitment and talent acquisition: Machine learning algorithms can help HR teams to identify the most promising candidates from a large pool of applicants, based on their resumes, cover letters, and other application materials. These algorithms can also help to reduce bias in the recruitment process by prioritizing qualifications and skills over demographic characteristics.
  2. Employee engagement: Machine learning can help HR teams to analyze data from employee surveys, social media, and other sources to identify trends and patterns that can help to improve employee engagement and satisfaction.
  3. Performance management: Machine learning can help HR teams to identify which employees are most likely to succeed in a given role and provide them with personalized training and development plans to help them reach their full potential.
  4. Predictive analytics: Machine learning can help HR teams to analyze large amounts of data to identify patterns and predict future outcomes, such as employee turnover, absenteeism, and productivity. This can help HR teams to proactively address issues before they become major problems.
  5. Employee retention: Machine learning can help HR teams to identify which employees are most likely to leave the organization, based on factors such as job satisfaction, performance, and career goals. This can help HR teams to develop targeted retention strategies to keep their best employees engaged and motivated.

How can machine learning help your Product & Innovation Team?

  1. Product recommendations: Machine learning can analyze customer data to recommend products and services that are likely to be of interest to them, increasing sales and customer satisfaction.
  2. Forecasting demand: Machine learning can help businesses to forecast demand for their products and services based on historical data, market trends, and other factors. This can help them to optimize production, reduce waste, and improve customer service.
  3. Quality control: Machine learning can help businesses to monitor the quality of their products and services in real-time, detecting defects and other issues before they become major problems.
  4. New product development: Machine learning can help businesses to identify market trends and customer needs, providing insights that can be used to develop new products and services that meet those needs.
  5. Personalization: Machine learning can help businesses to personalize their products and services based on customer preferences, increasing customer satisfaction and loyalty.

Overall, machine learning can help businesses to develop more innovative and customer-focused products and services, improving competitiveness and profitability. By analyzing large amounts of data and identifying patterns and trends, machine learning can help businesses to make better data-driven decisions, leading to more successful product development and innovation.

Our machine learning consulting services cover these sectors: Manufacturing, Retail/Brands, Pharmaceutical, Healthcare, Insurance, Financial Services & FinTech, Technology Platforms, Gaming, Telecoms, Hospitality, Engineering, Professional Services, Media & Communication, Education, Life Sciences, Public Sector and Logistics.

What we do

Scoping & Architecture Design

First, we need to understand your problem better. Once we determine there is a fit for Machine Learning, we will work closely together to prepare a roadmap, review the scientific literature, and determine requirements.

Data Collection & Exploration

Machine Learning needs data. If you have data needed to train the models, we will perform an exploratory analysis phase to find patterns and correlations. If you don’t, we will collect the data for you using online sources (if possible).

Model Development

We run thousands of experiments in parallel to develop a machine learning model. A model is the core of a machine learning system – trained on historical data it can predict the future trends or understand the semantics of a text.

Machine learning development services and full-stack application development

We integrate the model with a REST API or a front-end application, developing all necessary features to access the model in a user-friendly way. Scalable and with the state-of-the-art security.

What is Machine Learning?