Enterprise Data Engineering Services & Data Pipeline Solutions

From real-time pipelines to unified data platforms — we build and deploy data systems that enable analytics, AI, and operational intelligence at scale across your entire enterprise.

We do not just move data. We build systems that make data usable for real business decisions.

Most vendors move data. We build the infrastructure that makes data trusted, governable, and AI-ready.

A Pipeline Vendor Delivers:

A CaliberFocus Data System:

Pipeline engineering, unified platform architecture, and AI-ready data foundations

Pipeline Engineering & DataOps

Automated, reliable, production-grade data movement

We build data pipelines the way software engineers build applications — with version control, automated testing, CI/CD deployment, monitoring, and self-healing logic. DataOps discipline means your pipelines stay reliable, not because someone watches them, but because they are engineered to.

ELT/ETL pipeline development — scalable transformation workflows with orchestration (Airflow, Prefect, dbt), dependency management, and error handling
CI/CD for data pipelines — automated testing, version-controlled transformations, staged deployment, and rollback across environments
DataOps implementation — applying software engineering discipline to data: code reviews, testing frameworks, documentation standards, and monitoring
Batch and micro-batch orchestration — complex multi-dependency workflows with SLA monitoring, alerting, and automated retry logic
Pipeline observability — lineage tracking, data quality assertions, freshness monitoring, and anomaly detection across every pipeline stage
Legacy pipeline modernization — migrate fragile, undocumented ETL jobs to maintainable, monitored, production-grade data pipelines

Unified Data Platform Architecture

One version of the truth across every enterprise system

Fragmented data across ERP, CRM, EHR, and data marts is not a data problem — it is an architecture problem. We design and build unified data platforms that consolidate sources, enforce consistency, and serve every downstream consumer — analytics, AI, reporting, and operations — from a single trusted foundation.

Data lakehouse architecture — combining the flexibility of data lakes with the reliability of warehouses on Databricks, Snowflake, or Delta Lake
Cloud data platform implementation — AWS (Redshift, Glue, S3), Azure (Synapse, Data Factory, ADLS), GCP (BigQuery, Dataflow, GCS)
Data mesh architecture — domain-oriented data ownership with federated governance for large, multi-team enterprise environments
ERP, CRM, and EHR consolidation — unified data models that reconcile Dynamics 365, Salesforce, Epic, Cerner, and SAP into a single semantic layer
Data warehouse modernization — migrate on-premise warehouses (SQL Server, Oracle, Teradata) to cloud-native platforms with zero data loss
Semantic layer and data modelling — consistent business metrics, KPI definitions, and dimensional models that every team trusts and uses

AI-Ready Data Foundation

The data infrastructure that makes AI work in production

87% of AI projects fail because the data foundation wasn’t ready. We build the specific data infrastructure that AI systems require — feature stores, ML-grade preprocessing pipelines, embedding infrastructure, and real-time feature serving — so your AI systems have reliable, current, and correctly structured data at training time and inference time.

Feature stores — centralized, versioned feature engineering with point-in-time correctness for ML training and real-time inference serving
ML data pipelines — automated preprocessing, normalization, validation, and dataset versioning for repeatable model training
Embedding and vector data infrastructure — document ingestion pipelines, chunking strategies, and embedding storage for RAG and semantic search
Real-time feature serving — low-latency feature retrieval for online ML models that need current data at inference time
Data lineage for AI — full audit trails from raw source to model input, supporting model reproducibility and regulatory explainability
AI data quality frameworks — domain-specific validation rules, statistical profiling, and anomaly detection for ML training datasets

Five layers. From raw ingestion to governed, AI-ready data serving.

Ingestion Layer

Batch and streaming ingestion from ERP, CRM, EHR, APIs, databases, IoT sensors, and SaaS platforms. Change data capture (CDC), event streaming (Kafka, Azure Event Hubs), and file-based ingestion — all monitored and observable.

Transformation Layer

Data cleaning, normalisation, enrichment, and business logic transformation. ELT-first design using dbt, Spark, or cloud-native transform services — with automated testing, documentation, and version-controlled SQL.

Storage Layer

Purpose-built storage tiers: raw zone (landing), curated zone (clean), and serving zone (aggregated). Lakehouse architecture with Delta Lake or Iceberg for ACID transactions, time travel, and unified batch/streaming access.

Serving Layer

Data marts, semantic layers, feature stores, and real-time APIs that serve analytics, BI, ML models, and operational systems. Every downstream consumer gets data in the format and latency they require.

Governance Layer

Metadata management, lineage tracking, data cataloguing, quality monitoring, access controls, and compliance audit trails. Governance embedded in the platform — not retrofitted after deployment.

AI without reliable data is unreliable AI. Every model, agent, and intelligence system we build depends on what gets built here.

Data Engineering

Pipelines, platforms, feature stores, and governance — the foundation.

AI & Analytics

ML models, GenAI systems, and BI platforms consume clean, structured, governed data.

Decisions & Operations

AI agents, operational dashboards, and automated decisions act on real-time, reliable data.

Real deployments across healthcare, finance, manufacturing, and enterprise operations.

Healthcare & Life Sciences

EHR data consolidation — unify Epic, Cerner, and Athena data into a single analytics-ready platform for clinical and operational reporting

RCM data pipelines — real-time claims, eligibility, and remittance data flows that feed ImpactRCM.AI agent workflows and AR analytics

Clinical AI feature stores — patient demographics, lab values, and diagnosis histories as ML features for readmission and risk models

HIPAA-compliant data lakes — PHI-governed data platforms with field-level encryption, audit trails, and role-based access for regulated analytics

Financial Services

Trade and transaction data platforms — high-volume, low-latency pipelines for real-time risk, compliance, and fraud detection systems

Core banking consolidation — unified data model across core, CRM, and digital channels for 360-degree customer and portfolio analytics

Regulatory reporting pipelines — automated data lineage and audit trails for BCBS 239, Basel, IFRS 9, and other compliance frameworks

ML feature infrastructure — credit, fraud, and churn model feature stores with point-in-time correctness for model training and inference

Manufacturing & Operations

IoT sensor data pipelines — high-throughput time-series ingestion from factory floor sensors for predictive maintenance and quality monitoring

Supply chain data consolidation — ERP, WMS, and supplier data unified for demand forecasting and inventory optimization models

Production analytics platform — real-time OEE dashboards, yield tracking, and defect rate monitoring from manufacturing data streams

Operational AI data foundation — ML-ready datasets for equipment failure prediction, route optimization, and capacity planning models

Enterprise & SaaS

Multi-tenant data architecture — scalable data platform design for SaaS products delivering per-customer analytics and AI features

ERP modernisation data layer — migration of on-premise SQL Server or Oracle data warehouses to Snowflake or Databricks with zero data loss

Self-service analytics foundation — governed data models, semantic layers, and certified datasets that enable business teams to build their own reports

Product analytics pipeline — event tracking, user behaviour data, and feature usage streams that feed product intelligence and ML recommendations

40%

Reduction in equipment downtime via IoT data and predictive analytics

70%

Reduction in manual data pipeline maintenance effort

10×

Faster analytics query performance after warehouse modernization

Zero

Data loss during mission-critical pipeline migrations

Cloud Data Platform & Architecture

Lakehouse, warehouse, and modern data infrastructure built on top of your engineering foundation

ML & Predictive AI

The ML systems that consume clean, governed, feature-engineered data from this platform

Data for AI & Feature Engineering

ML-grade data preparation, feature stores, and AI-ready pipeline specialisation.

Data Governance & Quality

Governance frameworks and quality management that operate across your data platform.

AI is only as good as the data it runs on. Let’s start with the infrastructure.

Vendor-agnostic. Best tool for the job. No forced migrations.

Domain	Tools & Platforms We Work With
Orchestration	Apache Airflow · Prefect · Azure Data Factory · AWS Glue · dbt Cloud
Stream Processing	Apache Kafka · Apache Spark Streaming · Azure Event Hubs · AWS Kinesis · Flink
Data Warehouses	Snowflake · Databricks · Azure Synapse · Google BigQuery · Amazon Redshift
Transformation	dbt · Apache Spark · Azure Synapse Pipelines · Dataform · Fivetran
Storage & Lakehouse	Storage & Lakehouse Delta Lake · Apache Iceberg · Azure Data Lake · AWS S3 · Google Cloud Storage
Data Quality	Great Expectations · dbt Tests · Monte Carlo · Soda · Bigeye
Cataloguing & Lineage	Apache Atlas · Microsoft Purview · Alation · Collibra · OpenMetadata
Feature Stores	Feast · Tecton · Hopsworks · AWS Feature Store · Vertex AI Feature Store

Industries we serve

We collaborate with global technology leaders to deliver secure and scalable growth-driven digital solutions. Our partnerships strengthen our ability to innovate, accelerate transformation, and drive measurable business impact for our clients.

Case Studies

We used generative AI to automate documentation, compliance checks, and medical coding. The solution improves accuracy, cuts manual effort, speeds turnaround, and ensures regulatory compliance in clinical use.

0 +

200 +

When patient data was summarized clearly, documentation felt less burdensome. With CaliberFocus, clinician satisfaction rose from 58% to 81% without changing how teams work.

Dr. Rebecca HallCMIO, Healthcare

Better documentation and fewer audit issues delivered real savings. With CaliberFocus, billing compliance improved to 98.6%, reducing risk while easing the burden on clinicians.

Sophia WilliamsCTO, Clinical care

We gained clear visibility into student performance. Engagement rose, scores improved, and administrative effort dropped by nearly 30 percent, giving educators time to teach.

Andrew MillerDirector, Education Technology

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Data Engineering & Pipelines

Data Engineering Systems That Power AI and Enterprise Decisions

The difference that matters

From data pipelines to data systems

A Pipeline Vendor Delivers:

A CaliberFocus Data System:

What we build?

Three production-grade data capabilities

Pipeline Engineering & DataOps

Unified Data Platform Architecture

AI-Ready Data Foundation

How we build it?

Production data platform architecture

Ingestion Layer

Transformation Layer

Storage Layer

Serving Layer

Governance Layer

Why this is the Foundation?

Data engineering → AI → decisions

Data Engineering

AI & Analytics

Decisions & Operations

Where this works?

Data engineering in production — by industry

Healthcare & Life Sciences

EHR data consolidation — unify Epic, Cerner, and Athena data into a single analytics-ready platform for clinical and operational reporting

RCM data pipelines — real-time claims, eligibility, and remittance data flows that feed ImpactRCM.AI agent workflows and AR analytics

Clinical AI feature stores — patient demographics, lab values, and diagnosis histories as ML features for readmission and risk models

HIPAA-compliant data lakes — PHI-governed data platforms with field-level encryption, audit trails, and role-based access for regulated analytics

Financial Services

Trade and transaction data platforms — high-volume, low-latency pipelines for real-time risk, compliance, and fraud detection systems

Core banking consolidation — unified data model across core, CRM, and digital channels for 360-degree customer and portfolio analytics

Regulatory reporting pipelines — automated data lineage and audit trails for BCBS 239, Basel, IFRS 9, and other compliance frameworks

ML feature infrastructure — credit, fraud, and churn model feature stores with point-in-time correctness for model training and inference

Manufacturing & Operations

IoT sensor data pipelines — high-throughput time-series ingestion from factory floor sensors for predictive maintenance and quality monitoring

Supply chain data consolidation — ERP, WMS, and supplier data unified for demand forecasting and inventory optimization models

Production analytics platform — real-time OEE dashboards, yield tracking, and defect rate monitoring from manufacturing data streams

Operational AI data foundation — ML-ready datasets for equipment failure prediction, route optimization, and capacity planning models

Enterprise & SaaS

Multi-tenant data architecture — scalable data platform design for SaaS products delivering per-customer analytics and AI features

ERP modernisation data layer — migration of on-premise SQL Server or Oracle data warehouses to Snowflake or Databricks with zero data loss

Self-service analytics foundation — governed data models, semantic layers, and certified datasets that enable business teams to build their own reports

Product analytics pipeline — event tracking, user behaviour data, and feature usage streams that feed product intelligence and ML recommendations

What you can expect?

Outcomes from production data systems

40%

70%

10×

Zero

Why CaliberFocus?

What makes our data engineering different?

AI-First Data Engineering

Governance Built In, Not Bolted On

DataOps as Standard Practice

Production Economics Focused

Connected services

What gets built on this data foundation?

Cloud Data Platform & Architecture

ML & Predictive AI

Data for AI & Feature Engineering

Data Governance & Quality

Ready to build the data foundation your AI needs?

The toolchain

Tools & platforms we work with

Industries we serve

Healthcare

Industrial Manufacturing

Banking and Finance

Retail and Ecommerce

Pharma & Life Sciences

Logistics and Supply Chain

Energy and Utilities

Media and Entertainment

Travel and Hospitality

Education & EdTech

Application innovation backed by deep engineering..

Measurable Results

True Partnership Model

Enhancing
Clinical Care,
Fewer Readmits!