Data Preprocessing Services for AI/ML & LLMs

Clean, Labeled, Model-Ready Datasets for Production-Grade AI Systems

Stop burning your budget on models trained on garbage data. Get data preparation support — clean, labeled, model-ready datasets — so your AI performs exactly as intended in production.

Get Your Data Preprocessing Proposal

Success Stories

...it's all about results

Environmental Monitoring

Environmental Monitoring

Bounding Box Image Annotation to Enable AI-Powered River Monitoring

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Large Infrastructure Monitoring

Large Infrastructure Monitoring

Drone Image Annotation with 95%+ Labeling Accuracy

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Traffic Management

Traffic Management

35% Accuracy Improvement in Traffic Management System via Aerial Image Annotation

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Autonomous Drone Navigation

Enhancing Object Detection Algorithm Accuracy with Precise Drone Video Annotation

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Content Recommendation

Text and Video Labeling for Predictive Content Intelligence Platform

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AI DATA PREPROCESSING SERVICES

De-Risk Enterprise AI Investments with Clean Training Data

Prevent AI Data Incompatibility with Training Data Preprocessing Solutions

Machine learning is 10% modeling and 90% getting data into a state where modeling is possible. Our data preparation services handle that for you. We transform raw, real-world data (from documents, emails, support chats, databases, cloud applications, legacy systems, third-party platforms, etc.) into a clean, structured, training-ready format that enables machine learning algorithms to learn effectively.

Whether you're training predictive models or building LLM knowledge bases, here’s how we prepare your institutional data for AI training / ingestion:

  • Clean labels for supervised learning
  • Single unified "Golden Dataset" for AI training
  • No missing values, no duplicates, consistent schemas
  • Numerical representations that machine learning models can process
  • Features engineered at the right granularity for accurate predictions
  • Data that reflects the real-world distribution your model will encounter in production

PROCESS

Bridging the Gap between ‘Having Data’ and ‘Having Model-Ready Datasets’

Customizable Data Preprocessing Solution with a Proven Workflow

By removing the 'black box' from the data pipeline with a transparent data preprocessing framework (combining automation with expert-led validation), we deliver AI training datasets that are optimized and ready for immediate deployment.

SERVICES

End-to-End AI/ML Data Preprocessing Services

Delivering Consistent, Labeled, and Statistically Representative Training Datasets

Enterprise AI data preparation is complicated because data is fragmented across multiple disjointed systems. We operationalize the complete data preparation lifecycle, delivering reliable, production-grade ML data pipelines. Additionally, by integrating human-in-the-loop validation in this AI-ready data preparation workflow, we help enterprise teams reduce training cycles, minimize data drift, and maximize the return on their AI investment.

AI Data Collection Services

  • Centralized data repository creation from existing infrastructure (databases, legacy systems, third-party platforms)
  • Multi-modal AI data collection (image, text, video, audio)
  • Targeted web scraping, where needed
  • Human-in-the-Loop data validation

Data Integration Services

  • Map different schemas/data structures to create a unified data model
  • Join related entities (matching customers across CRM and billing systems using fuzzy matching algorithms)
  • Resolve conflicts in cases of overlapping data

Data Cleansing Services

  • Missing value imputation using domain logic or statistical methods
  • Remove exact and near-duplicate records
  • Standardize data (date formats, phone number patterns, address structures)
  • Handle outliers and resolve contradictory entries by implementing business rules

Data Transformation Services

  • Categorical encoding to convert text into numerical codes that algorithms can process
  • Standardize measurements to consistent units (currency, dates, addresses)
  • Convert nested data structures (JSON, XML) into tabular format
  • Parse and extract numerical values from unstructured text fields

Feature Engineering & Scaling Services

  • Predictive feature creation from existing data (like days since last purchase and average order value from signup date and transaction date)
  • Build domain-specific business-relevant calculations (customer lifetime value, churn risk, fraud scores)
  • Data normalization of all numerical features to comparable ranges
  • Ensure features are mathematically compatible with your chosen algorithms

Data Labeling Services

  • Assign accurate labels to image, text, and video data
  • Implement quality control on the labeled training datasets through multi-annotator consensus, expert review workflows, and inter-annotator agreement metrics
  • Resolve ambiguous edge cases using defined business criteria

Data Validation Services

  • Detect data drift and verify representative sampling
  • Validate business rules (positive revenue, valid dates, allowed values)
  • Confirm schema compatibility with model input requirements
  • Verify data completeness across all critical features
  • Outlier detection to ensure they are legitimate data points and not errors

Training Data Splitting Services

  • Partition the AI training dataset into independent training, validation, and test subsets
  • Stratified sampling to help the model learn from the minority class patterns
  • Temporal splitting (based on time-order) to prevent the leaking of future data into training
  • Group-aware splitting to prevent data leakage in the training, validation, and test sets

CLIENT SUCCESS STORIES

It's all about results.

The Proof is in the Pipeline

Discover how we’ve helped businesses across 50+ nations bridge the gap between "lab-ready" and "market-ready" AI/ML applications by solving their most complex training data challenges.

Bounding Box Annotation Services

Precise bounding box annotation for high-resolution aerial river images to train an AI-powered river flow obstruction detection system using the client’s proprietary data annotation tool.

1,500 to 2,000

Images Labeled per Week

98%

Labeling Accuracy Rate Maintained

<1%

Revision/Rework Rate
  • Service Image Annotation
  • Platform Client’s Proprietary Annotation Platform
  • Industry Environmental Monitoring / Forestry
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Aerial Image Annotation

Large-scale image annotation services for a drone-based infrastructure monitoring company developing an automated bird nest detection system on power grids.

15,000+

Images Annotated

95%+

Annotation Accuracy
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aerial image annotation

Helping a government agency improve urban traffic flow by boosting the accuracy of their AI system through aerial image labeling

35%

Increase in Model Accuracy

20%

Improvement in Traffic Flow Monitoring
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ai-model-snippet

Labeled over 100,000 frames in drone footage to improve the accuracy of object detection algorithms used for drone surveillance

30%

Boost in Object Detection Accuracy

20%

Increase in Overall Operational Efficiency

Expanded

Drone Tracking Capabilities
  • Service Video Annotation Services Infrared & Thermal Imaging Processing Bounding Box Annotation
  • Platform CVAT
  • Industry Security and Surveillance
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Data Labeling for a Predictive Content Intelligence Platform

Labeled over 2500 entertainment content (Movies, TV Series, Trailers) monthly to enable the accurate prediction of the target audience engagement rates and response.

65%

Improved AI Model Accuracy

60%

Less Content Categorization Errors

4-Month

Faster Model Development
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Security and Compliance

Your data security is our priority

ISO
Certified

HIPAA
compliance

GDPR

GDPR
adherence

Regular
security audits

Encrypted data
transmission

Secure
cloud storage

RELATED SERVICES

Beyond Data Preprocessing Services: Custom AI Model Training Data Support

From Raw Web Data Collection to Training Dataset Delivery & Model Evaluation

AI Data Collection Services

Multi-modal data collection via targeted web scraping

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Data Annotation Services

Labeling image, text, and video data

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Domain-Specific AI Training Data Services

AI Training Data for diverse use cases

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CONTACT US

Stop Training Your Enterprise AI on Noise

Eliminate the "Garbage In" Risk with a Specialized Data Preprocessing Company

AI failures are expensive. Ensure your project’s success with relevant training datasets.

Send us a sample of your data. We'll analyze it to identify preprocessing requirements, flag quality issues, and provide a detailed remediation plan—for free, with no strings attached. Try the sample before committing to our data preprocessing services.

FAQ - Frequently Asked Questions

AI Data Preprocessing Services

Data preprocessing services convert raw data into a clean, structured format that machine learning algorithms can process. It includes extracting data from source systems, correcting errors and inconsistencies, converting text to numeric values, engineering predictive features, assigning labels, and validating data quality. Our enterprise data preprocessing services handle the technical gap between how your business systems store data (optimized for transactions) and how AI models require data (numerical arrays with consistent schemas and no missing values).

Core training data preprocessing services utilize the following techniques:

  • Data integration: Merging data from multiple sources into a unified, consistent dataset
  • Data cleaning: Filling missing values, removing duplicates, fixing formatting errors, handling outliers
  • Data transformation: Converting categories to numbers (encoding), standardizing units, and parsing dates into timestamps
  • Feature engineering: Creating new variables that expose patterns (time-since-last-purchase from transaction dates, sentiment scores from text)
  • Feature scaling: Normalizing numerical ranges so large values don't dominate small but important features
  • Data splitting: Partitioning into training/validation/test sets while preserving statistical distributions
  • Data Labeling: Assigning labels to images, text, and videos for supervised learning

Your AI system will only be as good as its training data. Raw business data contains errors (missing values, duplicates, formatting inconsistencies), exists in incompatible formats, and lacks the engineered features that help models detect patterns. Using that chaotic data for machine learning training will result in AI that hallucinates, contradicts itself, or misleads your team. Our data preprocessing company delivers validated, training-ready datasets so your team can focus on building models instead of debugging data pipelines—and your AI actually works when deployed.

SunTec India's training data preparation services support a wide range of AI/ML/LLM training use cases across various industries and enterprises. Explore our client success stories to see how we've successfully delivered data preprocessing solutions.

  • Fraud detection (cleaning transaction histories, balancing fraud/legitimate examples)
  • Disease prediction (cleaning EHR data, standardizing diagnosis codes, engineering patient history features)
  • Recommendation systems (processing user behavior logs, creating filtering features)
  • Predictive infrastructure maintenance (cleaning sensor data, creating features to predict failure indicators, labeling equipment failures)
  • Claims processing (standardizing claims data, engineering risk indicators, detecting fraudulent patterns)

The choice of data preprocessing techniques and tools varies based on data volume, data type, and expected AI model outcomes. Instead of a one-size-fits-all approach, we select and configure the right stack for your project's specific needs. Here are some data preprocessing tools our teams use:

  • Python Libraries – Pandas, NumPy ( for tabular, text, and numerical data preprocessing)
  • Data Annotation Platforms – Labelbox, CVAT, Label Studio (for image, text, and video labeling at scale)
  • ETL & Data Pipeline Tools – Apache Spark, Talend (for large-scale data integration and transformation)
  • Data Quality Tools – Great Expectations, OpenRefine (for profiling, validation, and cleaning)
  • Cloud Platforms – AWS (S3, Glue, SageMaker), Google Cloud Dataflow, Azure Data Factory (for scalable, cloud-native preprocessing)
  • OCR & Document Processing – Amazon Textract, ABBYY (for extracting data from scanned documents and images)
  • Database & Query Tools – SQL, MongoDB, Elasticsearch (for structured and semi-structured data handling)
  • Version Control & Tracking – DVC (Data Version Control), MLflow (for tracking dataset versions and experiment lineage)

Yes. We document the complete history of every data point—source system, extraction timestamp, all transformations applied, and business rules used. You receive a full audit trail showing exactly where each feature originated and how it was modified. Our machine learning data preprocessing services also ensure regulatory compliance through a data lineage trail, while making it easier to debug model errors and explain predictions to stakeholders.

Yes. When preprocessing data for machine learning, we analyze training data for bias by examining demographic factors such as race, gender, and age, where relevant, and identifying issues including underrepresented groups, uneven error rates, and fairness violations. We provide fairness reports with impact ratios and recommend solutions such as resampling, reweighting, or generating synthetic data. For regulated industries, we comply with applicable standards, including HIPAA and the EU AI Act.

Yes. We analyze training data for demographic bias across protected attributes (race, gender, age) when relevant to your use case. This includes checking for representation gaps (undersampled groups), label bias (different error rates across groups), and violations of statistical parity. We generate fairness reports that show disparate impact ratios and recommend mitigation strategies (resampling, reweighting, and synthetic data generation). For regulated industries, we align with frameworks like NIST AI RMF and EU AI Act requirements.

Your internal data engineers are expensive and better deployed on model development and tuning. At the same time, tools like AWS SageMaker Data Wrangler are powerful. However, they don't solve the core problem: someone still needs to audit the data, define cleaning rules, handle edge cases, label content accurately, and validate output quality. By hiring a data preprocessing service provider, you get access to specialists with domain expertise who can make judgment calls on ambiguous data, utilize the tools better while catching edge cases and domain-specific errors that rule-based systems miss, and deliver production-ready AI training datasets on time.

Both. For one-time projects, we deliver a cleaned dataset you can use to train AI/ML solutions internally. For production systems, we build automated pipelines that monitor for data drift, trigger reprocessing when patterns change, and maintain consistency as new data arrives. Ongoing support includes updating feature engineering logic as business requirements evolve and relabeling when ground-truth definitions change.

We process data within your secure environment, so sensitive information never leaves your control. Before any human review, we apply appropriate de-identification techniques—pseudonymization for financial data, anonymization for customer records, aggregation for competitive intelligence. We comply with industry-specific regulations, including HIPAA (healthcare) and GDPR (EU operations). All team members sign NDAs, complete security training, and follow role-based access controls that limit data exposure to only what's necessary for preprocessing tasks.