Generative AI Data Infrastructure

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Generative AI Data Infrastructure

Generative AI Data Infrastructure

Our Gen AI Data Infrastructure expertise aims to convert unstructured data into high-quality and AI-ready resources that power machine learning and generative AI pipelines. This is accomplished through AI dataset management, governance frameworks, and scalable processing technologies.

AI Data Management Infrastructure Solutions

VOLTERA provides proven-by-experience solutions for transforming, optimizing, and managing data specifically for artificial intelligence and training data optimization in model development and deployment.

01 Design AI Data Infrastructure

Quickly build a working model to test the core viability of your generative AI application development concept with minimal investment.

02 Prepare LLM Data

Develop a functional and no-frills version of the AI solution that demonstrates core value and can be tested by real users. This MVP helps align the go-to-market strategy with real-world user needs.

03 Manage AI Training Data

Develop a functional and no-frills version of the AI solution that demonstrates core value and can be tested by real users. This MVP helps align the go-to-market strategy with real-world user needs.

04 Build ML Data Pipelines

Leverage our deep technical knowledge and full-stack development capabilities to evaluate your generative AI project’s feasibility, potential challenges, and optimal approach.

05 Govern AI Model Data

Use agile methodologies to continuously refine the AI solution through rapid development, testing, and beta-testing feedback cycles.

06 Label AI Training Data

Use agile methodologies to continuously refine the AI solution through rapid development, testing, and beta-testing feedback cycles.

07 Scale AI Training Infrastructure

Design the AI system with flexible infrastructure that supports from idea to product transformation, enabling seamless growth in complexity and user demands.

Data Infrastructure for AI in Industries

These solutions are specialized data management platforms designed to transform industry-specific raw data into AI-ready resources while addressing unique sector challenges. Each solution enables advanced machine learning and predictive modeling tailored to specific sector requirements.

Healthcare AI Data Infrastructure

Develop secure, HIPAA-compliant data pipelines for medical datasets
Implement advanced anonymization and AI data privacy techniques
Enable AI model training for precision diagnostics and predictive healthcare analytics

Finance AI Data Platform

Create secure and regulatory-compliant financial data management systems
Support risk modeling and algorithmic trading data preprocessing with cross-domain data integration
Ensure strict data governance and integrity for financial machine-learning models

Manufacturing AI Data Hub

Design comprehensive sensor and process data collection infrastructure
Develop advanced data preprocessing techniques for industrial IoT datasets
Enable predictive maintenance and quality control AI model training

Autonomous Vehicles Data System

Build high-performance sensor data fusion and management platforms
Support simulation and real-world driving scenario dataset processing
Facilitate ML model training for autonomous perception through computational resource optimization

Research AI Data Network

Create scalable, cross-disciplinary research data management platforms
Integrate multi-source scientific datasets with advanced interoperability
Support collaborative AI model development through enterprise AI data strategy

Telecom AI Data Infrastructure

Develop network performance and customer interaction AI data center infrastructure
Enable intelligent service optimization through advanced data analytics
Support predictive customer experience and network management AI models

Scale your AI without the headaches – our data infrastructure makes it easy and efficient.

Success Stories in Generative AI

Check out a few case studies that show why VOLTERA will meet your business needs.

Healthcare

Data Science

Data Insights & Forecasting

Stock relocation solution

The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.

10% productivity boost 7% increase in sales

Mark S.

Partner Pharmacy network,

View case study →

Data Science

Sales automation

Data Insights & Forecasting

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

5% customer retention boost 25% profit growth

Christopher Loss

CEO Dayrize Co, Restaurant chain,

View case study →

Retail

Sales automation

Data Engineering

Performance Measurement

The Retail company struggled with controlling sales and monitoring employees’ performance. We implemented a software solution that tracks sales, customer service, and employee performance in real-time. The system also provides recommendations for improvements, helping the company increase profits and improve customer service.

17% increase in sales 15% revenue boost

Amir R.

CEO, CEO Fashion Retailer

View case study →

Retail

Supply-chain automation

Web and mobile development

Supply chain dashboard

The client needed to optimize the work of employees by building a data source integration and reporting system to use at different management levels. Ultimately, we developed a system that unifies relevant data from all sources and stores them in a structured form, which saves more than 900 hours of manual work monthly.

900h+ manual work reduced 100+ system integrations

Michelle Nguyen

Senior Supply Chain Transformation Manager Unilever, World’s Largest Consumer Goods Company

View case study →

Would you like to explore more of our cases?

Data Infrastructure for AI Technologies

Arangodb

Neo4j

Google
BigTable

Apache Hive

Scylla

Amazon EMR

Cassandra

AWS Athena

Snowflake

AWS Glue

Cloud
Composer

Dynamodb

Amazon
Kinesis

On premises

AZURE

AuroraDB

Databricks

Amazon RDS

PostgreSQL

BigQuery

AirFlow

Redshift

Redis

Pyspark

MongoDB

Kafka

Hadoop

GCP

Elasticsearch

AWS

Overfitting? Data leakage?

Not on our watch. We’ve got the tools to keep your AI sharp.

AI Data Infrastructure Process Steps

Our goals are streamlined data handling and optimization, ensuring that data flows seamlessly from ingestion to actionable AI outputs while maintaining quality, security, and scalability.

Data Sourcing

Hunt down quality data from diverse sources – APIs, web scraping, databases, you name it. Ensure it’s reliable and relevant for training AI models.

01 Data Cleaning

Strip out the junk, fill gaps, and format the data into something your AI can actually learn from – think normalization, deduplication, and standardization.

02 Privacy & Compliance

Lock down sensitive info using encryption, anonymization, or differential privacy techniques to stay compliant with regulations like GDPR or HIPAA.

03 Scalable Storage

Set up storage and processing systems that can handle massive datasets and scale up as your AI needs more training fuel.

04 Bias Mitigation

Test your data for skewed patterns, then fix them with fairness-focused tools or rebalanced datasets to keep the model outputs ethical.

05 Real-Time Integration

Plug into live data streams or updates so your AI models stay sharp with the latest and greatest inputs.

06 Users' Feedback

Deploying prototype to select user groups and gather comprehensive insights.

06 Resource Optimization

Tune your computational resources and training pipelines for speed and efficiency—leverage distributed computing or GPU acceleration where needed.

07 Users' Feedback

Deploying prototype to select user groups and gather comprehensive insights.

06 Deployment & Monitoring

Roll out AI models into production and set up monitoring to catch performance issues or drifts in data over time.

08 Users' Feedback

Deploying prototype to select user groups and gather comprehensive insights.

06 The Challenges of Data Infrastructure for AI

VOLTERA creates adaptable and secure data infrastructure that underpins mitigation through automation and AI-powered solutions, which are crucial to addressing these challenges at scale.

Ensuring Real-Time Data Streaming & Processing

The infrastructure must support up-to-date AI model training by enabling efficient data ingestion and real-time processing.

Designing Scalable Systems for Growing ML Datasets

Handling increasing data size and complexity requires distributed storage, high-throughput processing, and optimized data pipelines.

Implementing Privacy-Preserving Techniques

Maintaining compliance with data privacy regulations involves techniques like differential privacy and secure multiparty computation.

Optimizing Computational Resources

Advanced scheduling, distributed processing, and model compression are essential to enhance efficiency and reduce costs.

Ensuring Real-Time Data Streaming & Processing

The infrastructure must support up-to-date AI model training by enabling efficient data ingestion and real-time processing.

Designing Scalable Systems for Growing ML Datasets

Handling increasing data size and complexity requires distributed storage, high-throughput processing, and optimized data pipelines.

Implementing Privacy-Preserving Techniques

Maintaining compliance with data privacy regulations involves techniques like differential privacy and secure multiparty computation.

Optimizing Computational Resources

Advanced scheduling, distributed processing, and model compression are essential to enhance efficiency and reduce costs.

MVP Development Service Possibilities

Our approach focuses on maximizing learning and minimizing waste through strategic, agile, and user-centric MVP product development with Generative AI.

AI Dataset Curation:

Collect, filter, and organize diverse data sources to create high-quality training datasets for machine learning models.

Training Optimization:

Refine and preprocess training data to improve model performance, reduce bias, and raise learning efficiency.

Data Storage:

Create scalable, resilient storage architectures that enable efficient data access, versioning, and management across distributed computing environments.

Automated Annotation:

Develop intelligent platforms that use machine learning to automatically label and classify training data with high precision and minimal human intervention.

Scalable Infrastructure:

Design high-performance computing environments with optimized GPU/TPU resources to accelerate model training and reduce computational bottlenecks.

Cross-Domain Integration:

Develop methodologies to merge and standardize datasets from multiple domains, making comprehensive and versatile AI training.

Data Augmentation Techniques:

Implement advanced techniques to synthetically expand and diversify training datasets to improve model generalization and robustness.

Predictive Data Quality:

Create intelligent monitoring and validation systems that proactively assess and predict training datasets’ effectiveness and potential biases.

All publications

February 21, 2025

17 min

Custom E-commerce APIs Integrations: Types, Pros, Cons & FAQs

E-commerce

February 21, 2025

17 min

Data Analysis Leads to 3.6% Weekly Sales Growth

E-commerce

February 21, 2025

16 min

Big Data in E-commerce: Stars in the Sky

E-commerce

FAQ

How can we optimize computational resources for large-scale AI model training?

What techniques ensure reproducibility and traceability in ML data pipelines?

How do you handle data heterogeneity across multiple sources for AI training?

What approaches minimize data leakage and overfitting risks?

How do you manage data versioning and lineage in complex ML projects?

Generative AI

Digital Transformation

Data Engineering

Custom Software Development

Web And Mobile Development

Data Science (AI/ ML)

Data Scraping

DevOps & Cloud Solutions

Generative AI Data Infrastructure