Our Sectors
Manufacturing
- Quality Control
- Monitoring Production
- Datalake Design
- Automatic Alerting
- Data Streaming
Fundraising
- Donor Clustering
- Campaign Monitoring
- Database Optimisation
- Custom Analysis
Pharmaceutical
- Quality Assurance (QA)
- Process Optimization
- Quality by Design (QbD)
- Product Quality Preview (PQR)
- Formulation Design
Why work with us?
Our working process
The working process of Kiwi involves several key steps to extract valuable insights from data. Here’s a detailed breakdown of the typical workflow
Understanding Client Objectives
- Understand and define the client’s objectives, challenges, and specific questions they want to address with data science.
- Clarify project goals, scope, and success criteria through collaboration with stakeholders.
Data Preparation and Exploration
- Identify and gather relevant data sources, both internal and external.
- Clean and preprocess the data to address issues like missing values, outliers, and inconsistencies.
- Conduct exploratory data analysis to gain initial insights and understand data patterns.
Model Development and Training
- Select appropriate machine learning algorithms based on the nature of the problem.
- Split the dataset into training and testing sets.
- Train and fine-tune the model using the training data to achieve optimal performance.
Validation and Deployment
- Validate the model using a separate testing dataset to ensure its generalization to new, unseen data.
- Implement the model into the client’s systems, ensuring seamless integration.
- Deploy the solution into production, making it accessible for regular use.
Monitoring, Iteration and Support
- Set up monitoring mechanisms to track the performance of deployed models over time.
- Gather feedback from users and stakeholders to identify areas for improvement.
- Iteratively update models and processes based on feedback and changing business requirements, maintaining a continuous improvement cycle.
