PULAN AI

Transform Raw Data into High-Quality, AI-Ready Datasets

Our data cleansing services ensure your data is accurate, consistent, and free of errors, providing a solid foundation for reliable and high-performing AI models.

Trusted by Innovators Across Industries

Why P-Cleanse

Garbage In, Garbage Out. Quality In, Performance Out.

The quality of your AI is directly tied to the quality of your data. We ensure your data is a pristine asset, not a liability.

Enhanced Data Accuracy

Eliminate inconsistencies, errors, and noise to create a pristine dataset that improves the accuracy and reliability of your AI models.

Bias Mitigation & Fairness

Identify and address skews and imbalances in your data to build fairer and more ethical AI systems that perform equitably across different demographics.

Improved Model Performance

High-quality, clean data is the foundation of high-performing models. Reduce training time, improve generalization, and achieve better results with clean data.

How It Works – Four Steps to Success

Our streamlined four-step process ensures high-quality results with fast turnaround times.

1. Data Profiling

Analyze your raw data to identify quality issues, inconsistencies, missing values, and outliers.

2. Cleansing & Normalization

Apply customized scripts and rules to correct errors, de-duplicate records, and normalize data formats.

3. Validation & Enrichment

Validate data against predefined rules and enrich it with external sources to enhance its value.

4. Quality Reporting

Receive a detailed report on the data quality improvements and the final cleansed dataset.

Customer Success Stories

Discover how leading companies have achieved breakthrough results with Pulan AI's data annotation services.

Financial Services

99.8% Data Consistency

A wealth management firm achieved 99.8% data consistency across its client records by cleansing and standardizing over 10 million entries.

Healthcare

30% Reduction in False Positives

By cleansing patient data and removing noisy signals, a medical research group reduced false positives in their predictive models by 30%.

E-commerce

25% Uplift in Product Data

An online retailer uplifted their product catalog quality by 25% by de-duplicating listings and standardizing attributes, improving search and recommendations.

Blog & Resources

Stay ahead of the curve with our latest articles on AI trends, technologies, and best practices.

The Silent Killer of AI Projects: Dirty Data

A Practical Guide to Data Normalization and Standardization

Automated vs. Manual Data Cleansing: Finding the Right Balance

Ready to Build the Future?

Let's discuss how our data solutions can accelerate your AI initiatives.