Data Engineering for AI That Turns Raw Data into Real Results

We build scalable data pipelines, cloud data platforms and MLOps workflows that make your data AI-ready, so your machine learning and GenAI projects reach production faster.

Years of AI & Software Delivery

100+

Enterprise & Startup Projects Delivered

In-House

Dedicated AI Research Team

Global

Clients Across India, USA & Global Markets

Your AI Is Only as Good as Your Data

Data engineering for AI collects, cleans and delivers data so machine learning models can use it reliably. Without it, even the best AI models fail.

Our Data Engineering Services

End-to-end data engineering services, from architecture to production, built for analytics, machine learning and generative AI.

Data Pipeline Development

Batch and real-time data pipelines with orchestration, validation and monitoring, built to run reliably at any scale.

ETL & Data Integration Services

Extract data from databases, SaaS tools and APIs, then transform it into one consistent, trusted source of truth.

Data Lake & Data Warehouse Solutions

Modern data lakes, warehouses and lakehouses on Snowflake, Databricks, BigQuery and Redshift.

Cloud Data Engineering

Build, migrate and optimize data platforms on AWS, Azure and Google Cloud with full cost control.

Big Data Engineering

Process terabytes to petabytes of data quickly with Apache Spark and distributed computing.

Real-Time Data Streaming

Streaming pipelines with Kafka and Flink for fraud detection, recommendations, IoT and live dashboards.

MLOps Services

Automated feature stores, training pipelines, model deployment and monitoring to keep models accurate in production.

RAG Pipeline & Vector Databases

Document ingestion, embeddings and vector databases that let your GenAI apps answer from your own business data.

From Raw Data to AI-Ready Data in 5 Steps

AI-ready data is accurate, consistent and structured so machine learning models can use it directly.

Collect
Clean
Prepare
Engineer Features
Deliver

• **Collect:** Connect databases, SaaS apps, APIs, documents and IoT devices. • **Clean:** Remove duplicates, fix missing values and standardize formats. • **Prepare:** Transform, label and split data for machine learning training. • **Engineer Features:** Build reusable features in a central feature store. • **Deliver:** Serve fresh, versioned data to models and LLM apps in real time.

How Our Data Engineering Consulting

Works

A clear, agile process with full visibility at every stage.

Discovery & Data Audit

We assess your data sources, quality gaps and AI goals.

Architecture Design

We design a scalable architecture and choose the right tech stack.

Build & Integrate

Our engineers build pipelines and integrations in short sprints.

Test & Validate

We test data quality, performance, security and cost.

Monitor & Scale

Ongoing monitoring, optimization and support as your data grows.

Technologies We Work With

Proven, enterprise-grade tools for every layer of your data platform.

DevelopmentAWS

DevelopmentMicrosoft Azure

DevelopmentGoogle Cloud

Data Engineering Solutions for Every Industry

1 of 5

E-commerce & Retail

Connect sales, inventory and customer data for demand forecasting and personalization.

Manufacturing

Process IoT sensor data for predictive maintenance and quality control.

SaaS & Technology

Power product analytics, in-app AI features and LLM copilots.

Healthcare

Unify patient, lab and claims data for predictive diagnostics with HIPAA-ready pipelines.

FinTech & Banking

Real-time transaction streaming for fraud detection, credit scoring and risk models.

E-commerce & Retail

Connect sales, inventory and customer data for demand forecasting and personalization.

Manufacturing

Process IoT sensor data for predictive maintenance and quality control.

SaaS & Technology

Power product analytics, in-app AI features and LLM copilots.

Healthcare

Unify patient, lab and claims data for predictive diagnostics with HIPAA-ready pipelines.

FinTech & Banking

Real-time transaction streaming for fraud detection, credit scoring and risk models.

E-commerce & Retail

Connect sales, inventory and customer data for demand forecasting and personalization.

Why Businesses Choose KriraAI as Their Data Engineering Company

AI-First Approach

Every pipeline is designed for machine learning and GenAI, not just reporting.

Flexible Ways to Work With Us

Dedicated Team

A full data engineering team working as an extension of yours.

Fixed Price Project

Clear scope, timeline and budget for well-defined projects.

Time & Material

Flexible hourly engagement for evolving needs and ongoing support.

Ready to Make Your Data AI-Ready?

Share your data challenges and get a custom architecture, tool stack and roadmap from our data engineers within 48 hours.

Frequently Asked

Questions

Data engineering services cover designing, building and maintaining systems that collect, store, transform and deliver data. They include data pipeline development, ETL, data integration, data warehousing and cloud data platforms, giving analytics teams and AI models clean, reliable data.

Data engineering for AI prepares data specifically for machine learning and generative AI. It covers collecting data from many sources, cleaning and labeling it, engineering features and delivering it to models through automated pipelines.

Machine learning models learn from data, so data quality directly decides model accuracy. Data engineering keeps training and inference data complete, consistent, current and available at scale, which cuts the time spent on data preparation.

Data engineers build the infrastructure and pipelines that move and prepare data. Data scientists use that prepared data to find insights and build machine learning models. Data engineering makes data usable; data science turns it into predictions.

An AI data pipeline is an automated workflow that moves data from source systems through cleaning, transformation and feature engineering, then delivers it to machine learning models for training or real-time predictions.

MLOps services automate how machine learning models are trained, deployed, monitored and updated. You need MLOps if you run multiple models, retrain them regularly or need to catch model drift in production.

A RAG (retrieval-augmented generation) pipeline lets a large language model answer using your own business data. It ingests documents, creates embeddings and stores them in a vector database, making GenAI answers more accurate and reducing hallucinations.

Cost depends on the number of data sources, data volume, real-time needs, cloud platform and engagement model. A single ETL project costs far less than a full enterprise data platform. We share a fixed estimate after a free data audit.

A simple data pipeline with a few sources takes two to four weeks. A complete cloud data platform with a data warehouse, streaming and MLOps usually takes two to six months.

Yes. Startups get a lean, cost-effective data stack that scales later. Enterprises get legacy migration, data governance and large-scale big data engineering support.

Data Engineering for AI That Turns Raw Data into Real Results

Your AI Is Only as Good as Your Data

Scattered Data

Poor Data Quality

Slow, Fragile Pipelines

Our Data Engineering Services

Data Pipeline Development

ETL & Data Integration Services

Data Lake & Data Warehouse Solutions

Cloud Data Engineering

Big Data Engineering

Real-Time Data Streaming

MLOps Services

RAG Pipeline & Vector Databases

From Raw Data to AI-Ready Data in 5 Steps