Enterprise Data Engineering
for
AI Ready
Enterprises
Fragmented data is the single biggest reason AI programmes stall. We build the data backbone scalable, governed, real-time so your AI initiatives can actually reach production.
Data Engineering Is the Foundation of Enterprise AI
AI is only as powerful as the data behind it. Most enterprise AI projects fail in production – not because the models are wrong, but because the data feeding them is unreliable, incomplete, or too slow.
We fix the data foundation first. Then the AI actually works.
- Data trapped in disconnected systems and silos
- Legacy ETL that breaks whenever something changes
- Poor data quality making model outputs untrustworthy
- Reporting cycles too slow for operational decisions
- Cloud migrations that stall because the architecture wasn't designed for AI
- No real-time data capability for time-sensitive operations
Trusted by Leading Enterprises
Enterprise Data Engineering Services – Every Capability.
From batch pipelines to real-time streaming to full AI-ready lakehouse platforms – we build what your enterprise actually needs, not generic templates.
AI-Ready Data Pipeline Engineering
Pipelines designed from day one for AI workloads – feature extraction, data validation, and model-ready formats built into the pipeline architecture, not bolted on later.
- Batch and real-time streaming pipelines
- Event-driven and trigger-based architectures
- Data transformation and enrichment
- Automated validation and quality gates
- Pipeline monitoring and alerting
ETL & ELT Development Services
Modern ETL and ELT that actually keeps pace with enterprise change. We don't build brittle pipelines that need daily babysitting – we build ones that handle schema drift, volume spikes, and new sources without breaking.
- Cloud-native ELT on Snowflake, Databricks, BigQuery
- Legacy ETL modernization and migration
- Workflow automation and orchestration
- Data extraction from ERP, CRM, APIs, files
- Pipeline performance optimization
Cloud Data Engineering Services
Deep expertise across AWS, Azure, and GCP – not generalist cloud consulting, but engineers who have built production data platforms on all three and know the tradeoffs firsthand.
- Cloud data platform architecture and build
- Cloud migration and data modernization
- Serverless and containerized data processing
- Multi-cloud and hybrid data architectures
- Cost optimization and resource governance
Real-Time Data Engineering
When batch is too slow. IoT data, transactional streams, event-driven microservices, operational telemetry – we build real-time pipelines that give your business sub-second data where decisions need it most.
- Apache Kafka and Flink streaming pipelines
- IoT data ingestion from sensors and edge devices
- Real-time dashboards and operational intelligence
- Event processing and stream analytics
- Change data capture (CDC) pipelines
Data Integration Engineering
Enterprise systems don't talk to each other by default. We make them. ERP, CRM, SaaS platforms, homegrown databases, legacy applications – connected, synchronized, and governed.
- ERP integration (SAP, Oracle, Microsoft)
- CRM and SaaS data connectors
- API-driven data integration patterns
- Master data management pipelines
- Legacy system data extraction
DataOps & Pipeline Automation
Data engineering is only reliable when it's treated like software engineering. We bring CI/CD, automated testing, observability, and governance to your data pipelines – so they don't fail quietly at 2am.
- Automated pipeline testing and validation
- Data observability and lineage tracking
- Deployment automation with version control
- Data quality monitoring and alerting
- Incident response and SLA management
Pipelines That Are Intelligent by Design
Traditional pipelines move data. Ours understand it detecting anomalies, classifying information, mapping schemas, and flagging problems before they reach the model or the dashboard.
It's the difference between a pipeline that passes data through and one that actively improves the quality of everything it touches.
Talk AI Data Strategy →Intelligent Data Quality
AI-powered detection of missing values, anomalies, duplicates, and schema drift – automatically, at pipeline speed.
Automated Data Classification
AI identifies sensitive data, business entities, and data relationships across your entire data estate without manual tagging.
Intelligent Schema Mapping
Speed up integrations by 60-80% using AI-assisted schema discovery and mapping across legacy systems, cloud platforms, and enterprise apps.
Predictive Pipeline Monitoring
Identify pipeline failures before they cascade ML-based anomaly detection on throughput, latency, and data patterns in real time.
End-to-End Data Engineering Architecture
From source system to AI model to business decision – designed as one coherent system, not a patchwork of tools.
- ERP / SAP / Oracle
- CRM / Salesforce
- IoT Sensors
- Cloud Applications
- Databases / DBs
- Documents / Files
- APIs / Webhooks
- Legacy Systems
- ETL / ELT Pipelines
- Real-Time Streaming
- Data Transformation
- Data Quality Checks
- Orchestration
- Data Integration
- Schema Management
- Pipeline Monitoring
- Data Lake
- Data Warehouse
- Lakehouse (Delta)
- Feature Store
- Data Catalog
- Governance Layer
- Access Controls
- Data Lineage
- Machine Learning
- Generative AI
- BI Dashboards
- Predictive Models
- AI Applications
- AutoML
- Data Science
- Automation
- Operations
- Revenue
- Cost
- CX
- Risk
- Innovation
Why Choose XFactr.ai for Data Engineering Services?
We're not just data engineers. We're an AI and data company that builds data platforms specifically to power AI – that changes what we optimize for at every level.
AI-First Engineering Approach
We don't build data platforms and then wonder how AI will use them. AI requirements drive every architecture decision from day one - feature stores, model-ready schemas, real-time feeds built in by default.
Enterprise-Scale Architecture
We've built systems handling millions of records daily across complex, multi-region environments. We know what breaks at scale before you hit it.
Cloud-Native Across AWS, Azure & GCP
Not cloud-agnostic in theory - actually experienced across all three. We pick the right platform for your workload, not the one we have the most certifications in.
Faster Path to Production AI
With the right data foundation in place, AI programmes that typically take 12+ months can reach first production outcome in 8-12 weeks. The foundation is the fast path.
Deep Industry Experience
Healthcare, manufacturing, energy, retail, financial services, construction, maritime - we understand the operational constraints and data complexity of your industry, not just the generic cloud patterns.
Security and Governance by Design
RBAC, data lineage, PII detection, encryption at rest and in transit, compliance automation - built into the architecture from the start, not retrofitted when the audit comes.
Modern Technology Stack
Snowflake, Databricks, Delta Lake, Apache Kafka, Flink, dbt, Airflow, Apache Spark - we work in the platforms your teams already know and the ones you're moving to.
Business Outcome Focus
Every data engineering project we scope is anchored to a business metric - reporting speed, model accuracy, cost per record, time to insight. We measure the outcome, not the pipeline uptime.
Built on the platforms enterprises already trust.
Trusted by industry leaders.
Hulet Smith
CEO, RehabMart.com
XFactr.ai did not just build technology for us. They helped transform how we think and how we grow.
Rick Szczodronski
CPO, Building Data Company
An amazing service! and The cloud migration project was seamless by making the process more easier.
John M
CEO, Landscaping Company
The Tech team is very responsive and they made sure we understood everything along the way.
Data Engineering for Industries Where the Stakes Are High
Generic data pipelines don't work in regulated, high-volume, operationally complex industries. We understand what's different about your sector.
Predictive Maintenance Data Pipelines
- Real-time IoT sensor data from production equipment
- Machine data integration with MES and ERP systems
- Feature engineering pipelines for predictive AI models
- Streaming anomaly detection for early fault identification
Real-Time Asset and Grid Intelligence
- Sensor and SCADA data ingestion at high frequency
- Operational data integration across asset management systems
- Real-time data feeds for grid balancing and fault detection
- Historical data lakes for long-range predictive models
Unified Commerce and Customer Data
- Transaction, inventory, and customer data unified at scale
- Real-time demand signals feeding forecasting models
- Personalization data pipelines with sub-second latency
- Multi-channel data integration across digital and physical
Secure Clinical Data Engineering
- HIPAA-compliant pipelines from EHR and clinical systems
- Patient data integration with strict access controls
- De-identification and privacy-preserving transformations
- Data foundations for clinical AI and analytics applications
Real Enterprise Data Engineering – Real Outcomes
Not benchmarks. Not demos. Production data engineering systems built for enterprises where the data has to be right.
AI-Ready Data Pipeline Across 15,000+ Oil Wells
Disparate sensor data, dynamometer readings, and operational records across a 15,000+ well fleet - no unified data layer, no AI capability, no real-time visibility.
Built a multi-modal data pipeline ingesting sensor telemetry, time-series well data, and operational context into a unified lakehouse - designed from day one for the ML models running on top.
Data Foundation Powering $20M to $250M+ Growth
Fragmented customer, inventory, and order data across siloed systems - no single view, no forecasting capability, no operational intelligence to support rapid growth.
Rebuilt the entire data layer - unified customer data, inventory intelligence pipelines, demand forecasting infrastructure, and operational dashboards that scale with the business.
Kongsberg Digital: Offshore Sensor Data at Scale
Terabytes of offshore equipment telemetry with no real-time processing capability - batch processing couldn't deliver the low-latency data deep learning models required.
Built high-throughput real-time streaming pipelines from offshore sensors through to the ML feature layer - enabling sub-minute data freshness for predictive maintenance models.
How We Deliver Enterprise Data Engineering
Six stages. A clear output at every step. Nothing built until the architecture is agreed.
Nothing deployed until it's tested.
Data Assessment
Audit current data landscape, sources, quality, and gaps. Understand what you have before designing what you need.
Architecture Design
Define the target data architecture – platform selection, pipeline patterns, governance model, and AI readiness requirements.
Pipeline Development
Build pipelines iteratively in sprints. Working data in motion before the end of sprint one – not at the end of the programme.
Testing & Optimization
Automated data quality testing, performance benchmarking, and resilience testing before anything touches production.
Deployment
Production deployment with CI/CD automation, monitoring dashboards, runbooks, and SLA documentation in place from day one.
Continuous Improvement
Ongoing DataOps – monitoring, retraining data quality models, schema updates, cost optimization, and new source onboarding.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
Enterprise Knowledge Base
Explore answers about Enterprise Data Engineering, AI, Cloud Platforms, Governance and Delivery.
Build your AI-ready data foundation.
Talk to our AI and data specialists about where to start - and how fast you can get to production.