Data Engineering Services
Laying the Foundation for Smarter Business Decisions with Data Engineering
Our Data Engineering services

Redefined Large-Scale Document Processing for Accurate Insurance Submissions
- 80% improved accuracy in data extraction and validation
- Identifies missing fields and ensures data consistency
- Scalable for handling high-volume submissions
- Improved underwriting decisions with accurate data insights
MLOps-Enhanced Generative AI pipelines to boost underwriting accuracy by 20%
- API-enabled efficient data logging, increasing data accuracy by 10%
- Reduced biased and hallucinated outputs
- Detailed logging & monitoring reduced model errors by 30%
- Enhanced Gen AI pipelines contributed to over $20M in revenue

Developing smart solutions for every industry

Healthcare AI Solutions

Fintech AI Solutions

Retail AI Solutions

SaaS AI Solutions

Travel AI Solutions

Fitness AI Solutions

Insurance AI Solutions

Manufacturing AI Solutions
Developing effective Generative AI solutions for every industry
Healthcare

Healthcare AI Solutions
Retail

Retail AI Solutions
Fintech

Fitness AI Solutions
SaaS

SaaS AI Solutions
Travel

Travel AI Solutions
Fitness

Fitness AI Solutions
Insurance

Insurance AI Solutions
Manufacturing

Manufacturing AI Solutions
What our clients say:
Our Data Engineering tech stack

Our Data Engineering Approach: How We Deliver Value
Defining a Scalable Data Strategy
Seamless Data Acquisition & Integration
Structuring & Transforming Data for Insights
Architecting Reliable Data Storage
Enabling AI & Business Intelligence
Deployment, Monitoring & Optimization
Defining a Scalable Data Strategy
Seamless Data Acquisition & Integration
Structuring & Transforming Data for Insights
Architecting Reliable Data Storage
Enabling AI & Business Intelligence
Deployment, Monitoring & Optimization
Our collaboration partners




Leading brands we’ve worked with

Data Engineering Services
How do you ensure the data used for machine learning is of high quality?
Can you help me manage and analyze unstructured data (e.g., text, images, or videos)?
How do you handle data integration from multiple sources?
Will my data systems be ready for AI/ML applications?
How do you ensure data security, especially for sensitive data?
What is the typical timeline for training a machine learning model using the data infrastructure you provide?
Point of view

Agentic AI in Customer Experience: A New CX Era
Customer expectations are shifting faster than most businesses can respond. Today’s consumers demand experiences that are not only fast and frictionless but deeply personalized across every channel, at every touchpoint. While generative AI has already begun...

On-device LLMs: The Disruptive Shift in AI Deployment
Large Language Models (LLMs) have reshaped how we interact with technology, powering everything from smart search to AI writing assistants. But until now, their power has mostly lived in the cloud. That’s starting to change. On-device LLMs are bringing this...

How AI Medical Scribes Are Transforming Healthcare Workflows: Beyond Voice to EHR
Clinical documentation has come a long way, from handwritten notes to voice-to-EHR solutions that transcribe spoken words into digital records. But as healthcare demands grow, so does the need for smarter, more intuitive tools. Enter AI medical scribe: intelligent...

AI Automated BOM Generation: From Concept to Components in One Click
Building hardware isn’t just about great design; it’s about getting the right parts at the right time without mistakes. For engineers and product teams, generating a Bill of Materials (BOM) is one of the most critical and tedious steps in the product lifecycle. It...