Build modern data platforms that power analytics & AI
Design scalable, secure, and intelligent data platforms that unify enterprise data, automate pipelines, enable real-time analytics, and create a trusted foundation for AI and business intelligence.
A single, trusted data foundation for the whole enterprise
Most organizations do not have a data problem — they have a fragmentation problem. Data sits in ERP, MES, LIMS, spreadsheets and cloud applications that never agree with each other, so every report starts with a debate about the numbers. Nexgensis engineers the platform underneath: unified ingestion, governed transformation, automated quality and curated data products that give analytics, BI and AI one dependable source of truth.
128
sources
1.4k
pipelines/day
< 5s
stream latency
End-to-end capability, from architecture to optimization
Every engagement is delivered by engineers who have run these platforms in production for regulated, high-volume enterprises.
Platform Architecture
Reference architectures for cloud-native data platforms sized to your workloads, budget and compliance posture.
Data Pipeline Development
Reliable, observable pipelines built for scale — with retries, alerting and lineage from day one.
ETL / ELT Automation
Automated ingestion and transformation frameworks that remove hand-built, brittle data jobs.
Real-Time Streaming
Event streaming with Kafka and cloud-native services for sub-second operational insight.
Data Warehousing
Dimensional and vault models tuned for BI performance and predictable query cost.
Data Lakes
Governed, low-cost storage for raw and semi-structured enterprise data at any volume.
Data Lakehouse
Unified lakehouse design combining warehouse reliability with lake flexibility and openness.
Data Integration
Connect ERP, MES, LIMS, QMS, CRM and legacy systems into one coherent data estate.
API Integration
REST and GraphQL integrations that expose trusted data products to applications and partners.
Business Intelligence
Semantic layers, certified datasets and dashboards leaders actually trust and use.
Cloud Migration
Move legacy warehouses to Azure, AWS or GCP with automated conversion and parallel validation.
Data Governance
Policies, ownership, quality rules and controls embedded into the platform, not bolted on.
Master Data Management
Golden records for products, materials, customers and sites across every source system.
Metadata Management
Catalog, lineage and glossary so every metric has a definition and a traceable origin.
Eight stages from discovery to continuous optimization
Stage 01
Business Discovery
Map decisions, KPIs, data owners and pain points before a single pipeline is written.
Stage 02
Architecture Design
Target-state platform blueprint covering storage, compute, security and cost model.
Stage 03
Data Ingestion
Batch and streaming connectors onboarded with schema handling and quality gates.
Stage 04
Data Transformation
Modular, tested transformation logic with version control and documented business rules.
Stage 05
Data Storage
Warehouse, lake or lakehouse layers optimised for performance and retention policy.
Stage 06
Analytics & AI
Curated data products powering BI, forecasting and machine learning workloads.
Stage 07
Monitoring
Freshness, quality and cost observability with alerting to the right owners.
Stage 08
Continuous Optimization
Ongoing tuning of queries, storage and orchestration to keep spend and latency down.
Cloud-native foundations engineered for scale and uptime
We design the target-state platform first — storage, compute, identity, networking and cost model — then build it with infrastructure as code so every environment is reproducible.
Cloud-Native Architecture
Elastic, serverless-first designs on Azure, AWS or GCP with infrastructure as code.
Secure Authentication
SSO, managed identities and role-based access aligned to enterprise policy.
Scalable Infrastructure
Compute that scales with workload peaks without re-engineering the platform.
High Availability
Multi-zone resilience, backup and recovery targets defined with your IT team.
Performance Optimization
Partitioning, caching and query tuning for fast dashboards at predictable cost.
Enterprise Monitoring
Unified observability across pipelines, storage, cost and data quality signals.
Sources
ERP · MES · LIMS · APIs · IoT · files
Ingestion
Batch + streaming connectors, CDC, schema drift handling
Transformation
Modular, tested models with version control
Lakehouse storage
Bronze · silver · gold layers with retention policy
Analytics & AI
Semantic layer, BI, feature store, ML serving
Pipelines that are observable, tested and hard to break
Batch or streaming, every pipeline ships with validation, quality rules, alerting and documented lineage.
412
cataloged datasets
99.5%
quality score
100%
lineage coverage
0
open access findings
Trusted data, provable controls
Governance is engineered into the platform so compliance evidence is a by-product of normal operation rather than a quarterly scramble.
Data Catalog
A searchable inventory of every dataset, owner and certified definition.
Metadata Management
Technical and business metadata captured automatically at pipeline runtime.
Data Lineage
Column-level traceability from source system through to the dashboard figure.
Data Quality
Rule-based checks with thresholds, scorecards and exception workflows.
Access Control
Role and attribute-based permissions enforced consistently across layers.
Compliance Monitoring
Controls mapped to GxP, ISO 27001, GDPR and internal audit requirements.
Audit Logging
Immutable records of who accessed or changed what, and when.
Data Privacy
Masking, tokenisation and residency controls for sensitive data domains.
How we build, every single time
Agile Delivery
Value shipped in short increments, with working data products every sprint.
Automation First
Anything repeated is codified — ingestion, testing, deployment and monitoring.
Scalable Architecture
Designed for tomorrow's volumes without a rebuild in eighteen months.
Operational Excellence
Runbooks, SLAs and clear ownership so the platform stays healthy in production.
Security by Design
Least privilege, encryption and auditability built into the first release.
Faster Time to Value
Accelerators and templates that shorten the path from kickoff to first insight.
Collect. Transform. Publish.
A simple flow underneath a sophisticated platform — data enters once, is governed once, and is published everywhere it is needed.
Step 1
Collect
- Enterprise Applications
- Databases
- APIs
- IoT Devices
- Cloud Sources
- Third-Party Systems
Step 2
Transform
- Data Cleaning
- Validation
- Enrichment
- Modeling
- Aggregation
- Scheduling
Step 3
Publish
- Dashboards
- Reports
- AI Models
- Machine Learning
- Data APIs
- Business Intelligence
Engineered on the platforms your teams already trust
Cloud Platforms
- Microsoft Azure
- Amazon Web Services
- Google Cloud Platform
Databases
- SQL Server
- Oracle
- PostgreSQL
- MySQL
- MongoDB
Data Processing
- Apache Spark
- Databricks
- Hadoop
- Kafka
- Apache Airflow
Business Intelligence
- Power BI
- Tableau
- Looker
- Grafana
Integration
- REST APIs
- GraphQL
- Azure Data Factory
- Apache NiFi
- Talend
Value across every data stakeholder
Business Teams
Better decisions using trusted enterprise data.
Product Owners
Monetize data with analytics and AI.
Data Scientists
Reliable, high-quality data for machine learning.
Governance Teams
Maintain compliance, security, and privacy.
IT Teams
Modernize infrastructure and reduce complexity.
What a modern data platform delivers
One
Single source of truth
10x
Faster analytics
AI
AI-ready data foundation
-70%
Reduced data silos
99%
Improved data quality
Auto
Automated pipelines
Real-time
Faster decision making
-35%
Lower operational costs
An end-to-end data engineering partner
From architecture and integration through governance, analytics, AI readiness and ongoing optimization.
Data engineering across regulated and high-volume sectors
Manufacturing
Pharmaceuticals
Healthcare
Retail
Banking & Financial Services
Chemical
Energy & Utilities
Logistics
Modernizing enterprise data infrastructure
A global manufacturer ran reporting on a decade-old on-premise warehouse fed by hundreds of hand-maintained scripts. Nexgensis designed a cloud-native lakehouse, automated ingestion from ERP, MES and LIMS, rebuilt transformation logic as tested and version-controlled models, and layered catalog, lineage and access controls across the estate. Reporting moved from overnight batches to near real-time, and the data science team gained a governed feature store for AI workloads.
6x
Faster data processing
-80%
Reduced manual work
+42%
Improved data quality
4x
Faster business reporting
Frequently asked questions
Transform your data into a competitive advantage
Partner with Nexgensis to design modern, scalable, and intelligent data platforms that power analytics, AI, automation, and enterprise growth.