Artificial Intelligence
& Machine Learning
Our team will guide you to find meaningful and actionable analytics from your data.
Forecast the future by using techniques such as predictive modeling, predictive pattern matching, multivariate stats, forecasting, and regression analysis.
Identify the recommended actions based on the forecast, using techniques such as neural networks, graph analytics, heuristics rule engine, complex event processing, operations research, and optimization.
Build models that are capable of understanding and interpreting unstructured data streams, such as free text and audio. Then, perform the appropriate set of actions based on the interpreted data.
Analysis & Strategy
Working with SMEs (subject matter experts) and stakeholders to identify the problem to be solved, define the hypothesis, and visualize what the final model would look like (classifier or predictor).Data Ingestion
Identify the data sources that are best suited to solve the problem or validate the hypothesis. Build the right pipelines to get access to the data.Evaluation
Determine the criteria for success and be specific as we determine the baseline model.Feature Selection
Work with SMEs to determine the baseline features that are most correlated to the output that you want to predict.Model Selection
Evaluate various algorithms to identify the model that best meets or supersedes the evaluation criteria.Experimentation
To further validate or improve the model, test the models in production and with newer data that was not used in the model building.Machine learning can tackle a wide array of tasks from classification of observed behaviors to prediction of spending patterns.
Cut your costs and reduce human error by using robust ML models. Uncover your operational capabilities by leveraging machine learning algorithms and models. Some examples include:
- Classification of customer churn
- Automated customer interactions
- Fraud prediction
- Tailored user experience
AI-driven virtual assistants and chatbots can gather valuable marketing data for your business and enhance your users’ experience. Not to mention, they can have hundreds of conversations simultaneously. Enhance your technological capabilities by embracing the future of customer service.
Will your patient cancel their future appointments? Can late payments be handled without third-party intervention? Will your sales prospect take action? Our data science can use models to help predict the future and help you to drive innovation one step at a time.
Manage uncertainty and risk by embracing the new level of security and performance with custom machine learning algorithms.
- Portfolio management
- Claims management
- Fraud detection
- Stock market forecasting
- Algorithmic trading
Drive employee retention rates. Leverage algorithm to attract, screen, engage and hire the best talent.
- Applicant monitoring & assessment
- Improved recruitment
- Reduced human biases
- Fake CV handling
Acquire new customers. Build, analyze, and optimize your marketing performance one step at a time.
- Customer behavior analysis
- Personalized marketing campaigns
- User-driven content generation
- Buying patterns recognition
Simplify lead generation and create smarter sales pipelines with thoroughly qualified prospects and data-driven approach.
- Prospect data analysis
- Enhanced sales forecasting
- Real-time marketing feedback
- Automated routine
FAQ
- How do AI services work?
Newfire’s AI services allow you to quickly configure and deploy business solutions using pre-built AI/ML models, in real-time. Using industry-specific chunks of data, AI services make it simple to scale without investing in IT infrastructure, hosting environments, and senior in-house developers.
- What is AIaaS?
AIaaS (Artificial Intelligence as a Service) is an offering with out-of-the-box AI solutions. It’s easy to set up a platform, and there’s no need to further invest in infrastructure or in-house developers. AIaaS can be scaled up or down according to your goals and business requirements.
- What is MLaaS?
MLaaS (Machine Learning as a Service) is a blend of ML tools as a feature of cloud technologies. It offers tools for data processing, visualization, natural language processing, predictive analytics, and deep learning. The main benefit of MLaaS solutions is that there’s no need to build infrastructure or hire in-house data scientists for model training and data processing stages. You pay for exact predictions, not for the IT infrastructure.
- Can AI improve healthcare?
AI has many implications in healthcare, but we believe that it’s some of its best applications are for:
- Early disease detection
- Help with treatment plans
- Data-backed decision making
- Associated care
- Remote health monitoring
- Easier access to medical services
- What is the role of ML in healthcare?
Machine learning (ML) is a set of practices that automates and improves the way computers learn from data. For healthcare, ML allows glimpsing at comprehensive data analytics, predictive analysis results, and real-time patient data processing. Because of this, ML increases the efficiency of new treatment options, which may have been unavailable before due to lack of data analytics and unification.
