We utilize algorithmic techniques to derive value from data within a business framework
We Integrating end-to-end pipeline of Data > Modeling > Decisioning > Deployment > Monitoring > Enhancements within your business context, that yields measurable impacts on revenue and cost. Our goal is to optimize value by harnessing AI to address your business challenges. With Priorise's advanced analytics, enhance decision-making, boost customer satisfaction, reduce churn, and optimize operations for increased business efficacy.
What we Do?
ML Platforms Set Up
Tailored ML platforms designed to meet unique business needs, providing flexibility and scalability for various applications.
Predictive Modeling & Forecasting
Using machine learning techniques to predict future outcomes and trends based on historical data.
Clustering & Segmentation
Expertise in constructing Large Language Models (LLMs) utilizing advanced techniques to process and understand extensive text data.
Anomaly Detection
Involves labeling or tagging data with relevant information to make it understandable for machines, facilitating machine learning model training. It ensures the accuracy and quality of training datasets, enabling AI systems to learn patterns and make accurate predictions, thus playing a crucial role in various AI applications across industries.
ML Operations
In any AI-driven enterprise, speedier development cycles, enhanced application and model performance, and cost-effectiveness are imperative. Achieving this requires robust processes, complemented by appropriate tools and skilled personnel. Priroise's ML Ops and DevOps services adhere to software engineering best practices, facilitating the consistent building, testing, deployment, and management of data products, ML models, and applications. This approach fuels innovation and fosters growth within organizations.
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Effective Case Studies
AI, Ml & Modelling, Sales Analytics
Improving Sales Conversions with Customer Segmentation Models & Gen AI
AI, Ml & Modelling, Sales Analytics
Improving Business Performance with Advanced Attribution Analytics
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Insights & Intelligence
November 8, 2024
- Admin
- Comments Off on Understanding Market Basket Analysis to Unlock Customer Insights
Understanding Market Basket Analysis to Unlock Customer Insights
Discover how Market Basket Analysis (MBA) reveals customer buying patterns to boost sales,…
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October 28, 2024
- Admin
- Comments Off on Top Reasons Why IT Staff Augmentation Is the Solution for Your Business Needs
Top Reasons Why IT Staff Augmentation Is the Solution for Your Business Needs
Explore how IT staffing augmentation helps businesses overcome challenges in hiring skilled talent,…
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June 17, 2024
- Admin
- Comments Off on Importance of Sales Analytics
Importance of Sales Analytics
In today’s data-driven business environment, the role of sales analytics has become increasingly…
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Priorise has been a tremendous partner for Salesforce. They have played a crucial role in our efforts to develop an internal application that involves numerous AI and ML components, intricate data engineering, and complex tasks. Priorise has stood out remarkably dependable throughout our partnership. One of their outstanding qualities is their ability to efficiently assist us in recruiting and onboarding new resources. Read More
Ali Nahvi
Senior Technical Product Manager, AI and Analytics - Salesforce
Over the past few years of managing Priorise staff, they have been instrumental in accelerating our roadmap from end-to-end through their project management and data science capabilities. Their project management acumen has added much-needed structure to our product roadmap, instilling a sense of rigor and discipline in our scrum team's execution. Read More
Catherine Blair
Senior Manager, Global Enablement Strategy - Salesforce
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Frequently Asked Questions
- How can I deal with missing values in a predictive model?
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Dealing with missing values in a predictive model can be handled by techniques like imputation, removing incomplete cases, or using algorithms that manage missing data. Properly managing missing data ensures accurate predictions.
- What are some of the uses for predictive modelling and analytics?
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Predictive modelling and analytics are used for forecasting, risk management, customer segmentation, and optimizing operations. They allow businesses to anticipate trends and make proactive decisions in areas like marketing and sales.
- How do I use clustering to segment customers?
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Clustering helps in customer segmentation by grouping similar customers based on behavior, preferences, or demographics. It enables businesses to target specific segments with personalized marketing strategies, improving customer engagement.
- What are the known ways of anomaly detection using machine learning?
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Anomaly detection methods using machine learning include techniques like isolation forests, autoencoders, and statistical models. These help in identifying outliers or unusual patterns in data, essential for fraud detection or operational monitoring.
