Our Cases

One of Latin America's largest industrial conglomerates, with operations spanning multiple business sectors and requiring unified data governance across diverse business units.

Fragmented SAP data silos across business units prevented cross-departmental insights. Legacy analytics tools were expensive and rigid. Teams lacked governed, self-service access to operational data at enterprise scale.

NearShift migrated SAP data to an Azure Databricks Lakehouse with Medallion Architecture. We implemented Unity Catalog with Row-Level Security (RLS) enforcing granular access controls per business unit. Data is distributed via Databricks SQL Serverless and Delta Sharing, and Databricks Genie was activated for natural language querying by non-technical users.

A leading logistics intelligence platform connecting shippers and carriers, managing highly heterogeneous data from multiple carrier systems requiring automated and reliable ETL processing.

Manual ETL processes for heterogeneous logistics data formats were taking 45 minutes to 1.5 hours to execute, creating bottlenecks in data availability and introducing manual error risk across carrier integrations.

NearShift automated extraction, transformation, and loading of heterogeneous logistics data using Alteryx and R. We built standardized processing pipelines capable of handling diverse data structures and formats from multiple carrier sources, deployed on AWS for scalable, reliable execution.

A leading financial market infrastructure organization, processing billions in daily transactions across capital markets in the Americas.

Migrating a massive, mission-critical legacy analytics environment from SAS to a modern cloud-native architecture — without operational downtime or compliance gaps in one of the most regulated financial environments in the Americas.

NearShift built a Data Lakehouse on Azure Databricks using the Medallion Architecture. We implemented Unity Catalog to centralize governance, enforce strict access control, and track full data lineage across all notebooks, pipelines, and business units. The migration was executed in structured waves to ensure zero disruption to live market operations.

A leading insurance and financial services organization with major credit card, digital banking, and consumer lending product lines serving millions of customers.

Core credit product data — covering credit cards, digital accounts, and consumer financing — was locked in a legacy SAS environment that was costly to maintain, slow to scale, and increasingly difficult to govern as the business grew its digital banking footprint.

NearShift migrated all major credit product data pipelines from SAS to an Azure Databricks Lakehouse. We implemented a three-layer Medallion Architecture (Bronze/Silver/Gold) using Azure Data Factory for ingestion and Databricks for processing. Delta Tables serve as the final layer, powering Data Marts and analytical views consumed directly by business users.

A regulated fintech operating as a central credit infrastructure platform, managing receivables registry and credit data for financial institutions nationwide under strict regulatory oversight.

As a regulated fintech handling sensitive credit data for multiple financial institution clients, the organization needed a centralized, governed, and audit-ready Data Lake capable of sustaining new product development from day one — while meeting strict compliance and data quality standards.

NearShift architected and built a Data Lakehouse on GCP using Databricks as the processing engine and Google Cloud Storage as the storage layer. We implemented a four-layer data architecture (transient, raw, trusted, refined), established CI/CD pipelines via Azure DevOps for continuous delivery, and designed the environment to scale as new data products and client integrations were added.

A leading digital investment platform providing investors with access to international financial markets and requiring scalable, intelligent customer service operations.

Legacy contact center infrastructure lacked AI/NLP capabilities, limiting automation, personalization, and operational efficiency for a high-growth financial services platform handling thousands of daily customer interactions.

NearShift deployed an Intelligent Virtual Assistant on Google Cloud CCaaS integrated with Salesforce CRM and real-time knowledge bases. We leveraged Generative AI and NLP for voice and text interactions, with qualified handoff to human agents maintaining full conversation context. Real-time sentiment analysis and automated conversation summarization were implemented via Agent Insights.

The cultural division of one of Latin America's largest financial institutions, managing one of Brazil's most comprehensive digital repositories of cultural knowledge.

A vast cultural knowledge base was difficult to search and navigate, limiting its usefulness for educators, researchers, and the general public. The institution needed an AI-powered interface to democratize access to its content at scale.

NearShift developed a GPT-4 powered conversational assistant on Microsoft Azure, integrated with the institution's digital cultural knowledge repository. We implemented automated weekly data ingestion, guard-rail prompts for accuracy, and a CI/CD pipeline via Azure DevOps. The system correlates entities, suggests related topics, and surfaces precise knowledge base references.

One of Brazil's largest electric utility companies, operating across generation, transmission, and distribution with large-scale Oracle-based operational databases.

Multiple Oracle database instances (CDBs with numerous PDBs) needed to be continuously replicated to GCP to enable modern analytics — without impacting production systems or introducing data latency.

NearShift implemented a two-stage replication pipeline using Fivetran HVR 6.0: a full initial load (Refresh) followed by continuous Change Data Capture (CDC) using archive-only mode to minimize production impact. HVR channels were configured for logical grouping, transformations, and audit column injection. A staging layer in Google Cloud Storage feeds final tables via the optimized Burst Integrate method.

Analytics and operations teams at a major Brazilian electric utility company, responsible for DEC and FEC regulatory indicators — critical quality metrics for electricity distribution, previously calculated manually.

DEC (Equivalent Duration of Interruption) and FEC (Equivalent Frequency of Interruption) indicators were calculated manually, creating bottlenecks, human error risk, and delays in regulatory and operational reporting.

NearShift built a fully automated pipeline on GCP using BigQuery and Dataform for transformation and KPI calculation. Historical data was migrated to production with full consistency validation. The solution was connected to Denodo for virtualized access and integrated with Power BI dashboards for daily operational reporting.

Engineering and asset management teams at a major Brazilian electric utility company, relying on SAP Plant Maintenance data platform.

Expanding the DEC/FEC automation framework to SAP PM data sources, which have different extraction patterns, data structures, and business rules compared to the initial implementation.

NearShift extended the existing GCP/BigQuery/Dataform architecture to ingest and process SAP PM data. We built dedicated transformation layers following the established Medallion pattern and delivered additional indicator visualizations requested by stakeholders during development.

A Brazilian sustainable energy company managing customer and operational data through Salesforce, requiring a modern, secure data platform for analytics and compliance.

Data extracted from Salesforce needed to be stored, processed, and shared with strict column-level encryption, key rotation, and role-based access controls — all within a scalable cloud architecture.

NearShift built a multi-layer Data Lake on Azure (Landing, Raw, Trusted, Refined) using Azure Data Factory for batch ingestion and Databricks with PySpark for streaming. We implemented column-level encryption with automated key rotation via Azure Key Vault, and exposed data through Databricks SQL Warehouse with user-specific decryption rules enforced at query runtime.

One of Brazil's largest natural gas distribution companies, managing thousands of IoT sensors and measurement devices across distribution infrastructure requiring centralized remote management.

Field technicians were making costly on-site visits to configure sensor tags and extract operational data manually. Device heterogeneity, low service availability, and absence of centralized tag management from SCADA systems created operational gaps and data blind spots.

NearShift deployed the BiT (BlueShift IoT) platform integrated with Azure IoT Hub and IoT Edge. We built a centralized web platform for device lifecycle management, remote tag configuration, edge rule processing, and OPC UA server parameterization — with real-time alarm monitoring for data capture and transmission failures.

A large-scale bioenergy producer operating across Brazil, with mission-critical data pipelines supporting operational decision-making.

Mission-critical data ingestion pipelines on AWS required 24/7 monitoring and rapid incident response. The client lacked internal capacity for continuous data operations, creating risks of undetected pipeline failures, data gaps, and rising cloud costs.

NearShift assumed full AMS (Application Management Services) responsibility: daily ingestion monitoring, automated controls for file arrival validation and record volume variance detection, structured incident management with root cause analysis and permanent remediation, and continuous performance tuning for cost optimization.

One of Latin America's largest oil and gas companies, operating PI System historians across refineries to support near-real-time operational analysis for thousands of process engineers and operations managers.

PI System historians with ~208,000 licensed TAGs needed to feed Azure cloud infrastructure for modern analytics — but the existing architecture had no pipeline for raw historical data migration or near-real-time streaming to the cloud.

NearShift developed a dual-mode data pipeline: near-real-time extraction from PI AF SDK streamed to Azure IoT Hub → Stream Analytics → Event Hub → Data Lake and Data Explorer. A full historical data migration for all TAGs was executed following the same cloud-native reference architecture.

One of the largest petrochemical companies in the Americas, supporting analytics operations across multiple countries through a centralized BI analytics platform.

A global analytics operation serving multiple geographies and business units required a modern, scalable platform that could support complex data models, real-time dashboards, and self-service analytics for international stakeholders.

NearShift built and maintained a comprehensive analytics factory on Azure using Databricks for data processing, Azure Data Factory for orchestration, Azure Analysis Services for semantic modeling, and Power BI for visualization. We delivered end-to-end support for modeling, visualization, and platform operations.

A national real estate registry organization managing high-volume interactions across a large network of notary offices through chat, email, phone, and messaging channels.

A high-volume, multi-channel contact center serving thousands of notary offices lacked AI-powered automation — creating unsustainable manual workloads, inconsistent service quality, and limited operational insights.

NearShift deployed an Intelligent Virtual Assistant for a high-volume service workflow, integrated with ServiceNow for real-time ticket and knowledge base access. Agent Assist was implemented for interaction summarization. Conversational Insights delivers 100% interaction coverage with sentiment analysis, topic extraction, and automated KPI reporting.

A leading water and sanitation services provider requiring migration of its Power BI Premium environment to Microsoft Fabric before contract expiration — including datasets over 10GB across multiple regions.

Migrating dozens of workspaces and thousands of reports to Microsoft Fabric within a highly constrained timeline, while solving the specific challenge of very large enterprise datasets that required cross-region migration without data loss or access disruption.

NearShift executed a structured migration plan leveraging Fabric Notebooks and Azure Storage Account for backup and restore of large datasets. All workspaces and reports were migrated sequentially with validation at each stage, maintaining full user access throughout the migration window.

A leading enterprise software provider running Databricks for data engineering and data science workloads across multiple environments requiring centralized governance.

Multiple Databricks workspaces lacked unified access control, data lineage visibility, and usage auditing — creating governance gaps that were incompatible with the organization's data-driven growth strategy.

NearShift implemented Unity Catalog across all Data Engineering and Data Science Databricks workspaces. We configured centralized access administration, enabled data lineage tracking for full visibility into pipeline dependencies and dataset usage, and deployed audit log storage and monitoring for complete access transparency.

Mining operations teams relying on radio communications across multiple industrial sites, with thousands of daily audio interactions containing operational intelligence that was previously unstructured and unanalyzed.

Extreme noise levels and mining-specific technical jargon rendered standard transcription models ineffective for radio communications — making it impossible to index, search, or analyze field communications at scale.

NearShift developed a custom fine-tuned Speech-to-Text model on Azure, trained on mining-specific audio to handle noise and domain vocabulary with far superior accuracy. We built an automated transcription API for all internal radio recordings, followed by classification, metadata extraction, and indexing — with a conversational interface for operational search.

A commercial analytics team operating a price recommendation model for industrial products, previously running on an on-premise Cloudera environment with limited scalability.

A production Dynamic Pricing ML model was constrained by on-premise Cloudera infrastructure — limiting scale, increasing maintenance costs, and preventing integration with modern real-time data streams.

NearShift migrated model training and deployment to Azure Databricks with an updated Spark 3.x runtime. We adapted Azure Data Factory pipelines to supply training data via ADLS Gen2, built Azure Functions for model serving with Azure Event Hub integration for real-time scoring calls, and managed full CI/CD via Terraform and Azure Pipelines.

A manufacturing division operating rolling mill facilities generating continuous sensor data from OPC Router-connected equipment and requiring real-time operational visibility.

Operational data from industrial production equipment was trapped in on-premises OPC Router systems with no path to cloud analytics — preventing real-time monitoring and operational dashboards for the business.

NearShift configured secure communication between OPC Router and Azure IoT Hub, routing raw sensor data to ADLS Gen2 Raw layer. Databricks directly consumes IoT Hub streams to generate analytical views across all Data Lake layers. Power BI dashboards were built on refined data for operational business users, with full CI/CD via Terraform and Azure Pipelines.

One of Brazil's largest residential construction companies, managing high volumes of real estate leads across WhatsApp and requiring personalized customer engagement at scale.

Managing high volumes of leads across WhatsApp while maintaining personalized engagement and real-time CRM data integrity was operationally unsustainable with human agents alone.

NearShift built SOFIA — a GPT-powered virtual seller integrated with WhatsApp, Zendesk, and Salesforce. SOFIA conducts personalized conversations, captures client preferences, schedules visits, and routes qualified leads to human agents. A file management platform allows non-technical teams to update property listings and tune SOFIA's behavior via dynamic prompts.

A global consumer goods company requiring a modern cloud analytics foundation supporting operations across multiple international regions, including finance, marketing, logistics, and sales.

Fragmented data across global regions and business functions prevented unified analytics. The company needed a cloud-native, scalable foundation that could support dozens of concurrent business users across multiple geographies.

NearShift architected a cloud-native analytics foundation on AWS with a Modern Data Warehouse on Snowflake for high-performance, multi-user analytics. Tableau Server was deployed on AWS EC2 for visualization. Generic, reusable ingestion and transformation scripts were automated via Jenkins and GitLab CI/CD pipelines — designed to support global expansion across all business functions.

One of Brazil's largest fashion retail chains, requiring real-time measurement of effective sales across physical and digital channels from SAP-based source systems.

Sales data locked in SAP On-Premise prevented real-time visibility into performance — limiting the ability of commercial and finance teams to make timely decisions based on current data.

NearShift built a cloud Data Lake on Azure using Databricks to process SAP sales data into a Gold layer updated daily. We delivered a corporate Data Warehouse, a self-service layer for analysts and data scientists, and Power BI reports structured as fact and dimension tables. Azure Data Factory orchestrates all pipelines with automated failure detection and retry logic.

A major pharmacy and health products retailer requiring migration of Power BI workspaces from Premium service to Microsoft Fabric capacity within a strict contractual deadline.

An imminent Power BI Premium contract expiration created a hard 90-day deadline to migrate all workspaces to Microsoft Fabric F64 capacity — with zero tolerance for access disruption during the transition.

NearShift planned and executed the full migration of all Power BI workspaces to Microsoft Fabric F64 capacity within the deadline. We transitioned licenses from Power BI Pro to Free tier with appropriate access profile adjustments, managed regional dataset challenges, and executed the migration sequence to preserve workspace integrity throughout.

One of the world's largest consumer goods companies, operating a complex multi-cloud analytics environment requiring sustained operational excellence across Azure and Oracle Cloud platforms.

A complex analytics environment spanning Microsoft Azure, Oracle Cloud, and legacy Hadoop infrastructure required proactive monitoring, incident management, and continuous improvement without disrupting business-critical reporting.

NearShift deployed proactive monitoring routines for early detection of issues across all analytics processes. We developed Power BI visualizations, managed cloud operations on Azure and Oracle Cloud, administered Hadoop environments, and delivered continuous improvements through a structured AMS engagement.

A leading health data analytics company in Brazil serving health plans, hospitals, and insurers — operating in a highly regulated environment with strict data governance and auditability requirements.

Data science teams were building high-value models but unable to move them into production. No standardized pipeline existed for feature engineering, model training, versioning, or deployment — creating AI bottlenecks in a regulated healthcare setting.

NearShift designed and implemented a full ML/OPS framework on Azure Databricks: Feature Engineering layer, standardized model training workflows, versioning, and a collaborative platform giving data scientists end-to-end autonomy to prepare, train, publish, and productize models — all within a governed, audit-ready environment.

A leading pharmaceutical manufacturer with a large commercial analytics operation dependent on prescription data — a critical metric for sales force management and market intelligence.

A legacy Data Warehouse Audit was generating critical prescription value metrics, but processing times were unacceptably slow — blocking commercial decision-making and limiting scalability for the analytics team.

NearShift executed a Proof of Concept migrating the Data Warehouse Audit workload to Databricks. We ingested 20 Parquet files, rebuilt the core prescription value metric, and established a Gold layer via Databricks SQL — available for Power BI consumption and for direct exploratory analysis by the client's data science team.

A major Brazilian specialty chemical manufacturer with compliance requirements and multiple business units, operating in a regulated environment where data integrity, lineage, and access control are non-negotiable.

Data siloed across the organization with no centralized governance, lineage, or access controls — preventing AI and analytics workloads from scaling in a regulated environment requiring full auditability.

NearShift delivered a three-wave Data Lakehouse: (1) Azure infrastructure and ingestion via Data Factory; (2) Medallion Architecture with Unity Catalog for centralized governance, full data lineage, and role-based access; (3) Data democratization via Delta Sharing, JDBC, and dashboards — with business users empowered to build a shared data glossary.

A leading soybean genetics and seed company — managing complex agronomic research data, distributor networks, and market intelligence across domestic and international operations.

Data from multiple agronomic, commercial, and logistics sources was disconnected — preventing cross-functional insights and limiting the client's ability to make data-driven decisions on crop development, market positioning, and distributor performance.

NearShift built a multi-layer Data Lake on Azure with Snowflake as the analytics layer. We designed ingestion pipelines from CSV sources through concept, logical, and physical modeling stages to a consumption layer, delivered a Soy Productivity Dashboard with 8 analytical panels, and built ML-powered Forecast models for product launch curves and portfolio analysis.