AI data preparation services for GenAI development
We prepare structured and unstructured data to improve AI performance. Our services include data cleaning, formatting, structuring, and labeling to create reliable training datasets.
Our Generative AI development expertise
data preparation for AI
How we prepare data for AI models
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1. Data collection
We source and validate structured and unstructured data from multiple channels, ensuring it meets AI model requirements.
This process covers:
- Integrating data from databases, APIs, real-time streams, and external sources
- Validating data to remove incomplete, outdated, or irrelevant information
- Ensuring compliance with GDPR, financial, and industry-specific regulations
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2. Data cleaning
We remove errors, inconsistencies, and missing values to improve dataset quality and integrity.
This includes:
- Handling missing values using imputation, interpolation, or removal strategies
- Detecting and addressing outliers to prevent skewed model performance
- Standardizing formats across multiple data sources to ensure consistency
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3. Data transformation
We convert raw data into structured, machine-readable formats to enhance AI processing.
It involves:
- Tokenization, lemmatization, and vectorization for NLP applications
- Normalizing numerical data, encoding categorical variables, and structuring text for AI training
- Adapting datasets to fit deep learning models for specific industry use cases
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4. Data reduction
We optimize datasets by removing redundant or irrelevant features while preserving critical information.
This stage focuses on:
- Applying dimensionality reduction techniques such as PCA, t-SNE, or feature selection to simplify datasets
- Identifying and removing irrelevant or low-impact variables
- Reducing memory and processing overhead while maintaining accuracy
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5. Data splitting and validation
We create balanced and representative training, validation, and test datasets to prevent bias and overfitting.
This process covers:
- Creating balanced splits to prevent class imbalances and overfitting
- Implementing cross-validation techniques to fine-tune model parameters
- Ensuring the datasets reflect real-world distributions for accurate AI predictions
They trusted our expertise
We prioritize ethical AI, privacy and compliance
Security & ethics in AI data preparation
Privacy-preserving AI training
We apply federated learning and differential privacy to process data without exposing raw information, ensuring secure AI model training across distributed environments.
Data governance
We implement strict data handling policies that comply with GDPR, financial regulations, and industry standards. Our approach includes data anonymization, encryption, and access control to protect sensitive information.
Ethical AI practices and bias detection
We identify and mitigate bias in AI datasets to ensure fair and transparent AI decisions. Our methodology includes bias audits, dataset balancing, and fairness-aware AI training techniques.
Testimonial
What our clients say
By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.
Why choose us
We are your partner for AI data preparation and labeling
Advanced AI data processing
Industry standards compliance
Domain expertise
Get in touch
Let’s talk
Book 1-on-1 consultation
Grzegorz Motriuk
Head of Sales | Application Development
Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.
FAQ AI data preparation and labeling
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What is AI data preparation?
AI data preparation is the process of collecting, cleaning, structuring, and labeling raw data to make it usable for machine learning models. It ensures that AI systems learn from high-quality, well-organized data, improving accuracy and performance.
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Why is data labeling important for AI?
AI models need labeled data to recognize patterns and make accurate predictions. Manual, AI-assisted, or fully automated labeling helps train models for tasks like image recognition, speech processing, and text classification.
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How do you ensure data quality?
We use automated cleaning, deduplication, and validation techniques to remove errors, inconsistencies, and noise. Our bias detection and correction methods ensure that datasets are balanced, fair, and optimized for AI training.
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Why choose Deviniti as AI data preparation company?
At Deviniti, we specialize in AI data preparation and labeling, ensuring that businesses have clean, structured, and high-quality datasets for machine learning. Our expertise includes data collection, cleaning, structuring, annotation, and bias detection to improve AI model accuracy and reliability.
We support industries such as finance, legal, and enterprise AI, delivering compliant, secure, and AI-ready data solutions.