Why Data Engineering?

Modern organisations run on data, but legacy systems, fragmented metadata, and manual workflows slow down analytics and innovation.

Unlock Analytics Readiness

Prepare data faster for analytics, AI, and reporting by automating key engineering workflows.

Make Data Discoverable

Generate and maintain rich technical and business metadata so teams can actually find and trust data.

Modernise Faster

Convert legacy SQL, ETL, and procedural code into modern, cloud-native architectures with AI assistance.

Govern at Scale

Improve visibility, governance, and compliance across large, distributed data estates.

Book a Data Engineering Discovery Session

Explore Data Engineering Solutions

Faster

Time to Value

Lower

Total Cost

Production-Ready

Data Pipelines

Built for AI

Architecture

AI Data Engineering Solutions | GenAI Protos

GenAI Protos delivers AI data engineering solutions - LLM-powered pipelines, RAG setup, SQL migration, vector database integration, real-time ingestion, and data governance at enterprise scale.

Modern Data Engineering for AI, Analytics & Growth

DATA ENGINEERING

3X Data Engineering (3XDE) helps organizations build scalable, reliable, and cost-efficient data foundations that power AI, analytics, and business outcomes.

AI-Powered Synthetic Data Generator

AI-Powered Synthetic Data Generator is a privacy-focused platform that creates realistic, schema-compliant synthetic data and anonymized PDFs without exposing sensitive information. It uses AI-driven workflows to preserve data relationships, detect PII, maintain document layouts, and validate output quality across structured data and documents.

Synthetic Data AI

Data Privacy

Schema Compliant Data

PDF Anonymization

Intelligent Data Dictionary

Intelligent Data Dictionary is an AI-powered platform that connects directly to enterprise databases, analyzes schemas and sample data, and automatically generates contextual data documentation. It identifies PII, profiles data quality and patterns, and translates technical metadata into business-friendly definitions and insights for improved discovery and governance.

AI Data Dictionary

Data Governance

PII detection

Data Profiling

Fast Data Catalogue

Fast Data Catalogue is an AI-powered platform that automatically discovers, documents, and explains enterprise data across structured and unstructured sources. It connects databases, file systems, and cloud storage, scans and classifies data assets, identifies PII, and creates a searchable catalogue to accelerate data discovery, governance, and AI readiness.

AI Data Discovery

Automated Cataloging

Automated discovery

PII Visibility

Siloed & Inconsistent Data

Data scattered across teams and systems leads to poor visibility and trust.

Unified data integration

Standardized data models

360° data visibility

Complex & Brittle Pipelines

Hard-to-maintain pipelines cause frequent failures, delays, and high costs.

Scalable pipeline design

Modular & reusable components

Robust orchestration

Poor Data Quality

Inconsistent, incomplete, and untrusted data impacts decision-making and AI.

Data quality frameworks

Validation & monitoring

Observability & alerting

Slow Time to Insights

Traditional architectures create latency and limit real-time analytics.

Modern data architectures

Optimized data flows

Streaming & real-time

High Data Costs

Inefficient storage, compute, and pipeline design drive up cloud costs.

Cost-efficient architectures

Right-sizing & optimization

Automated cost governance

AI & ML Data Readiness

Lack of curated, feature-ready data slows down AI/ML initiatives.

Feature engineering pipelines

Vector & embedding stores

ML-ready datasets

Data Integration

Point-to-point integrations, manual processes

Scalable, automated ingestion from any source

Faster integration, higher reliability

Pipeline Management

Complex scripts, fragile workflows

Orchestrated, modular, and reusable pipelines

Lower maintenance, fewer failures

Data Quality

Manual checks, inconsistent rules

Automated validation, monitoring, observability

Data Architecture

Legacy EDW, rigid schemas

Modern lakehouse & cloud-native architectures

Analytics Performance

Slow queries, high latency

Optimized models, caching & real-time pipelines

AI/ML Readiness

Data not prepared for AI/ML

Feature-ready datasets & vector pipelines

What are the best AI tools for data engineering?

The right AI tools depend on your data stack and engineering requirements. AI can assist with SQL development, code conversion, pipeline generation, data quality, documentation, metadata management, and data discovery. We build and integrate AI-powered data engineering solutions around your existing architecture and workflows.

Which AI is best for data engineering?

There is no single AI model that is best for every data engineering workload. The right approach depends on factors such as your data platforms, programming languages, security requirements, workload complexity, and deployment environment. We help organizations select and implement the right models, tools, and architecture for their data engineering needs.

Can AI convert SQL code to PySpark for data modernization?

Yes. AI can assist with converting legacy SQL and procedural data-processing code into PySpark and other modern frameworks. We build modernization workflows that accelerate code conversion, validation, testing, and migration while maintaining the logic and performance requirements of existing data pipelines.

How can AI help build a vector database architecture for enterprise data?

AI can support the design of vector pipelines that transform enterprise data into embeddings, manage metadata, and enable semantic search and retrieval. We design vector database architectures around your data sources, retrieval requirements, security model, and AI applications.

Can you build custom AI-powered data engineering solutions for enterprises?

Yes. We build custom solutions for data integration, pipeline automation, data quality, data cataloguing, SQL modernization, metadata management, vector pipelines, and AI/ML data readiness. Solutions can be designed around your existing cloud, on-premise, or hybrid data environment.

Build a Stronger Data Foundation. Power Everything That Matters.

From robust pipelines to AI-ready data platforms, 3X Data Engineering (3XDE) helps organizations turn data into a strategic advantage.

left-content

Book a Data Engineering Discovery Session →