use cases

Building a Scalable Data Strategy and AI Roadmap

Probelm

A large healthcare organization wants to develop a comprehensive data strategy and roadmap to support its digital transformation goals. Key challenges include:

Disparate data sources across departments (ERP, CRM, legacy systems)
Lack of centralized governance, leading to data quality and trust issues
Manual ETL processes slowing time-to-insight
An undefined AI enablement journey, with no clear use cases or supporting infrastructure

The organization seeks to streamline data integration, improve data quality, establish governance, and lay the foundation for AI-driven decision making.

Solutions

Asoft Consulting LLC proposes a 3-phase framework to define and implement a sustainable data and AI strategy.

Phase 1: Data Strategy & Roadmap Development

Activities:

Stakeholder workshops to define business goals and pain points
Audit of existing data assets and infrastructure
Define use cases for data analytics and AI
Create a roadmap for data architecture, governance, and AI adoption

Deliverables:

Current state assessment
Future state architecture
Roadmap with prioritized initiatives

Phase 2: Data Management & Integration Platform

Tech stack

ETL/ELT Tools: Azure Data Factory, Talend, Informatica, Apache NiFi
Data Lake: Azure Data Lake Gen2 / AWS S3
Databases: Azure SQL, Snowflake, Databricks Delta Lake
Orchestration: Apache Airflow, Azure Synapse Pipelines
Data Governance: Collibra, Microsoft Purview, Alation
Monitoring: Grafana, Prometheus

Activities:

Set up a centralized data lake/lakehouse architecture
Develop ETL/ELT pipelines to integrate structured and semi-structured data
Apply data quality and lineage frameworks
Build a metadata-driven catalog to promote data discoverability

Phase 3: AI Journey Enablement Tech Stack:

AI/ML Platforms: Azure Machine Learning, Databricks ML, TensorFlow, Scikit-learn
Model Management: MLflow, Kubeflow
BI Tools: Power BI, Tableau for augmented analytics
MLOps: Azure DevOps, Jenkins for pipeline automation

Activities:

Identify AI use cases (e.g., churn prediction, fraud detection, recommendation systems)
Create a feature store and reusable ML components
Establish MLOps pipelines for scalable model deployment
Drive adoption through POCs and value realization workshops

Customized Data Lake Solution Across Hybrid & Multi-Cloud Environments

Probelm

A global enterprise operating across financial services, insurance, and retail domains was facing significant challenges in managing its growing data assets:

Data silos existed across business units (BU) in different geographies (North America, EMEA, APAC).
Inconsistent data formats, security policies, and integration protocols made centralized analytics difficult.
Some business units required on-premises storage due to regulatory and compliance requirements, while others were cloud-native.
Lack of a unified data access layer hampered real-time decision-making, analytics, and AI adoption across the enterprise.

Solutions

Asoft Consulting LLC proposes 

Asoft designed and implemented a customized hybrid Data Lake solution that integrated on-premise systems, private cloud infrastructure, and public cloud services to enable seamless, secure, and scalable data operations.

Key Solution Features:

Modular Data Lake architecture with centralized metadata and distributed storage
Multi-region and multi-BU data ingestion, transformation, and governance
Data virtualization layer for unified access without data movement
Security, encryption, and access policies tailored to regional compliance needs (e.g., GDPR, HIPAA)

Layer	Technology Used
Data Ingestion	Apache NiFi, Talend, Azure Data Factory, AWS Glue, Kafka
Storage & Lake	HDFS (on-prem), Azure Data Lake Gen2, AWS S3, GCP Cloud Storage, MinIO (private)
Data Processing	Apache Spark, Databricks, Azure Synapse, EMR, Snowflake
Metadata Management	Apache Atlas, AWS Glue Data Catalog, Unity Catalog
Data Governance	Collibra, Immuta, Ranger, Azure Purview
Security	IAM, OAuth2.0, VPCs, KMS, RBAC, network policies
Visualization & BI	Power BI, Tableau, Looker
Deployment	Terraform, Ansible, Kubernetes (AKS/EKS/GKE), Docker

Deployment Architecture:

On-Prem (EMEA): Handles sensitive financial data using HDFS and secured by local firewalls.

Private Cloud (North America): MinIO with Kubernetes for mid-tier services and B2B data apps.

Public Cloud (APAC): Azure and AWS used for scalable storage, ML model training, and BI workloads.

Global Metadata & Access Layer: Unified through data cataloging and virtualization.

Metric	Result
Data Accessibility	95% increase in data availability across BUs via a single access layer
Analytics Acceleration	Time to insights reduced by 60% across key dashboards
Cost Optimization	30% cost savings by tiered storage strategy (cold/hot, on-prem/cloud)

Leveraging Data Science for Predictive Maintenance in a Manufacturing Company

Probelm

A leading manufacturing company specializing in automotive components faced operational inefficiencies and frequent machine downtimes. The company operates multiple production lines and relies heavily on machinery to meet tight production schedules. The unexpected breakdowns not only disrupted production but also increased maintenance costs and delayed deliveries, affecting customer satisfaction

Solutions

The company partnered with a data science consultancy to implement a Predictive Maintenance solution using advanced data science techniques. The steps included:

Data Collection and Integration:
Collected real-time data from IoT sensors installed on machines (e.g., vibration, temperature, and RPM).

Integrated historical maintenance records and downtime logs with sensor data for a comprehensive dataset.

Data Processing and Feature Engineering:
Cleaned and preprocessed the data to remove noise.

Identified key features correlated with machine failures, such as unusual temperature spikes or erratic vibration patterns.

Predictive Modeling:
Developed machine learning models (Random Forest and Gradient Boosting) to predict the likelihood of machine failure within a specified time window.

Used anomaly detection algorithms to flag unusual patterns in real time.

Implementation and Visualization:
Deployed the predictive models on a cloud platform, enabling real-time monitoring of machine health.

Created user-friendly dashboards for the operations team, providing actionable insights such as “critical maintenance required” or “scheduled checks recommended.”

Proactive Maintenance Scheduling:

Aligned maintenance activities based on predictive insights, optimizing technician schedules and ensuring necessary spare parts were available.

40%

Machine downtime was reduced

30%

3 Maintenance costs decreased

20%

Production output increased

Data Management for a USA-Based Law Firm

Probelm

Client: Leading Law Firm in the USA

The firm faced several challenges related to data management:

Disorganized and siloed data sources: Made it difficult to access case-critical information efficiently.
Compliance risks: Arising from inefficient and inconsistent data handling practices.
Manual processes: In data management led to human errors and delays in case preparation.

Solutions

Data Management Solution

Centralized Data Repository: Consolidated scattered data into a secure, unified platform.
Automated Data Workflows: Streamlined data entry, retrieval, and reporting processes.
Advanced Security Protocols: Ensured compliance with legal and data protection standards.
Real-Time Dashboards: Enabled quick insights for case tracking and decision-making.

Impact

50% Reduction in Data Retrieval Time: Faster access to case-critical information.

Improved Accuracy: Minimized errors with automated workflows.

Enhanced Compliance: Strengthened adherence to legal data management regulations.

Boosted Productivity: Freed up staff to focus on high-value legal tasks instead of manual data handling.

This transformation highlights how effective data management drives operational efficiency and ensures compliance for law firms.

Automating contract management for a Mid-sized law firm

A leading mid-sized law firm, they routinely deal with high volumes of contracts, requiring a legal automation platform to generate global templates of identified contracts and maintain a central repository of contracts. The company’s legal and compliance teams were burdened with manual tasks, such as contract review, risk assessment, and regulatory tracking.

Probelm

With different business units spread across the world and all the contracts stored physically, the client was due to

Double effort required to create repetitive contract drafts due to lack of centralized templates.

Difficult to trace multiple versions of contracts across different business units and regions.

Inefficient contract obligation and renewal management due to incomplete data and offline handling.

Excessive manual effort caused frequent errors and inefficient use of lawyer time.

Lack of a systematic approach led to operational inefficiencies, compliance risks, and resource drain.

Solutions

Implemented a legal automation platform that integrated AI and NLP to streamline legal and compliance processes.

Used NLP algorithms to extract key details from contracts and legal documents—such as clauses, obligations, deadlines, and risks—automating the contract review process.

Leveraged AI-powered analytics to assess risks comprehensively by analyzing internal and external data sources.

Delivered actionable insights to proactively mitigate compliance risks and ensure regulatory adherence.

70%

Faster contract negotiations

60%

Faster contract negotiations

20%

Boost in Lawyer productivity

Accelerating Data Modernization and BI Capabilities

Probelm

The client is an independent market leader in specialty filter solutions and scientific services.
Serves critical industries including healthcare, automotive, and aerospace.
Known for innovation in product design, testing, and manufacturing.
Faced challenges in leveraging large volumes of data for strategic decision-making.
Initiated a transformation to adopt modern BI tools and data modernization practices.

Solutions

We designed and implemented a comprehensive data management solution, including:

Centralized Data Repository: Consolidated scattered data into a secure, unified platform.
Automated Data Workflows: Streamlined data entry, retrieval, and reporting processes.
Advanced Security Protocols: Ensured compliance with legal and data protection standards.
Real-Time Dashboards: Enabled quick insights for case tracking and decision-making.

Impact

50% Reduction in Data Retrieval Time: Faster access to case-critical information.

Improved Accuracy: Minimized errors with automated workflows.

Enhanced Compliance: Strengthened adherence to legal data management regulations.

Boosted Productivity: Freed up staff to focus on high-value legal tasks instead of manual data handling.

This transformation highlights how effective data management drives operational efficiency and ensures compliance for law firms.

Point-of-Sale (POS) Automation Using UiPath

Probelm

The client’s manual POS processes caused inefficiencies, frequent errors, and delays in reconciliation, invoicing, and reporting, leading to compliance risks and slower transaction times, ultimately impacting customer satisfaction. They needed an automation solution to improve efficiency, accuracy, compliance, and the overall customer experience.

Solutions

RPA-Powered POS Automation by Asoft and UiPath

Asoft partnered with UiPath to implement an RPA-powered solution that automated critical POS processes.

Key Steps Included:

Automating Daily Reconciliation:
- Bots automatically reconciled transaction data from POS to back-end ERP systems.
- Real-time discrepancy detection and error alerts were integrated.
Invoice and Receipt Automation:
- Invoices and receipts were generated and delivered automatically.
- Integrated with email systems for sending digital receipts to customers.
Data Validation and Reporting:
- Bots validated transaction data and generated daily, weekly, and monthly reports.
- Automated compliance reporting ensured regulatory adherence.
Customer Feedback Collection:
- Feedback was captured and analyzed from POS systems for actionable insights.
Real-Time Inventory Updates:
- Automated synchronization between POS and inventory systems ensured accurate stock levels.

70%

Reduced reconciliation time

500+ Hours

Saved hours monthly

60%

Saved hours monthly

40%

Reduced Operational costs

Automating Invoice Reconciliation Using UiPath

Probelm

Amex’s manual processes for category vulnerability management faced issues such as data inconsistencies, delayed risk detection, inefficiency in handling large datasets, compliance challenges, and scalability limitations. These challenges increased risks and hindered operational efficiency, prompting the need for an automated solution to enhance accuracy, streamline processes, and enable proactive risk mitigation.

Solutions

Asoft developed an automated solution for managing category-level vulnerabilities using UiPath bots and intelligent automation.

Automated Data Extraction and Categorization:
- UiPath bots extracted transaction and vendor data from multiple sources.
- Data was categorized using predefined business rules and machine learning models.
Anomaly Detection:
- Bots detected irregularities or mismatches in category-level data.
- Cross-referenced historical records and compliance standards.
Proactive Risk Alerts:
- Real-time alerts flagged potential vulnerabilities.
- Enabled early intervention and risk mitigation.
Streamlined Reporting:
- Automated generation of detailed, compliant reports.
- Reports served both internal teams and external regulators.
Exception Management Workflow:
- Exception cases were routed via a structured workflow.
- Unresolved issues were escalated with complete audit logs for faster resolution.
Integration with Existing Systems:
- UiPath bots integrated with Amex’s existing data, compliance, and reporting systems.
- Enabled end-to-end automation and enhanced operational efficiency.

70%

Reduced vulnerability detection and resolution

1000+ Hours

Saved hours monthly

30%

Reduced Operational costs

Automating Invoice Reconciliation Using UiPath

Probelm

Zurich Insurance’s manual invoice reconciliation process faced frequent errors, time-intensive operations, delayed payments, and scalability challenges, resulting in strained vendor relationships and compliance risks. An automated solution was needed to enhance accuracy, efficiency, and scalability while ensuring regulatory compliance

Solutions

Asoft implemented an end-to-end automation solution for invoice reconciliation using UiPath.

Data Extraction and Validation:
- UiPath bots extracted invoice data from emails, scanned documents, and vendor portals using OCR.
- Automated validation of invoice details against purchase orders (POs) and contracts in the ERP system.
Automated Matching and Reconciliation:
- Invoices were matched with POs, receipts, and payment records using predefined business rules.
- Discrepancies were flagged for manual review, reducing human intervention.
Exception Handling:
- Implemented a robust exception-handling workflow.
- Unresolved mismatches were escalated to relevant teams with detailed logs.
Reporting and Audit Trail:
- Automated generation of daily and monthly reconciliation reports for management and compliance.
- Created a full audit trail to ensure transparency and meet regulatory standards.
Integration with Systems:
- UiPath bots integrated with Zurich’s ERP, finance, and vendor management systems.
- Enabled seamless, end-to-end automation of the reconciliation process.

80%

Reduced processing time

1200+ Hours

Saved hours monthly

40%

Reduced Operational costs

Transforming HR Processes with Generative AI

Probelm

Leading Pharmaceutical Company

Lengthy and inefficient onboarding processes: Slowed down employee integration and productivity.

Limited personalization in training programs: Resulted in reduced engagement and learning effectiveness.

Delayed responses to employee queries: Negatively impacted satisfaction and overall efficiency.

Solutions

Solution: Generative AI-Driven HR Transformation

Automated Onboarding: Streamlined the onboarding journey with AI-powered workflows.

Personalized Training Programs: Leveraged AI to create tailored learning paths for employees.

AI Chatbot: Deployed a conversational AI bot for real-time resolution of employee queries.

27%

Improved employee satisfaction

200 Hours

Saved hours monthly

40%

Reduction in onboarding time

GenAI Powered AIOps

Probelm

One of a large clients based out of US in manufacturing faced challenges with delayed incident responses, impacting operational efficiency and potentially leading to prolonged downtime for critical systems, The reliance on manual intervention for routine IT tasks created inefficiencies, diverting valuable resources from strategic activities and hindering overall productivity for teams

Solutions

The implementation of GenAI Powered AIOps introduce a comprehensive set of features aimed at revolutionizing IT operations. Leveraging advanced predictive analytics and intelligent monitoring, the system incorporates anomaly detection for real-time issue identification, ensuring that potential problems were proactively addressed. Dynamic thresholds and context-aware alerts were employed to enhance the precision of the monitoring system, minimizing false positives and negatives. The introduction of automated remediation capabilities, including self-healing systems and streamlined workflows, significantly reduce the need for manual intervention, leading to faster incident resolution. Continuous learning and adaptation mechanisms, such as adaptive models based on historical data and a feedback loop for refinement, allowed the system to evolve over time, improving accuracy and efficiency in managing the dynamic IT environment.

27%

Improved Incident Response

1200+ Hours

Saved hours monthly

38%

Reduced Downtime

Mobile App for Financial Dashboards

Probelm

Challenges Faced by the Finance Organization

Delayed Decision-Making: Lack of real-time insights when stakeholders are away from their desks.

Data Silos: Prevented a unified view across financial operations.

Inflexible KPI Tracking: Difficulty in monitoring revenue, expenses, and cash flow in real time.

No Alerts/Notifications: Absence of threshold-based alerts or anomaly notifications.

Poor Mobile Experience: Traditional dashboards not optimized for mobile, affecting usability.

Solutions

Developed a mobile application to provide real-time, secure, and interactive financial analytics for finance professionals and decision-makers.

Key Features:

Real-Time Financial Data Access: Instantly view up-to-date financial KPIs and metrics.

Customizable Dashboards: Personalize views to focus on the most relevant data.

Predictive Analytics and AI Insights: Gain foresight with AI-powered trend analysis and forecasting.

Push Notifications and Alerts: Receive timely updates on anomalies, thresholds, or significant events.

Secure Access and Compliance: Ensure data privacy and meet financial regulatory requirements.

Interactive Visualizations: Explore financial data through intuitive, touch-friendly charts and graphs.

Integration with Financial Tools: Seamlessly connect with ERP, accounting, and reporting systems.

Technical Requirements

<li>Backend: Integration with ERP and financial systems (SAP, Oracle Financials).</li>
<li>Frontend: Mobile-responsive UI/UX for both iOS and Android platforms.</li>
<li>Security: Multi-factor authentication and end-to-end data encryption.</li>
<li>Data Integration: RESTful APIs for seamless and real-time data synchronization.</li>
<li>Analytics Engine: AI/ML modules for real-time insights, anomaly detection, and forecasting.</li>

Enhancing Mobile App Performance for a USA-Based Law Firm

Probelm

Client: Leading Law Firm in the USA

Frequent App Crashes: The mobile app experienced instability, leading to a loss of client trust.

Device & OS Compatibility Issues: Inconsistent performance across platforms hampered user experience.

Security Vulnerabilities: Potential threats to sensitive client data due to weak security protocols.

Solutions

We conducted a comprehensive mobile testing strategy, including:

Functional Testing: Ensured seamless app performance across various devices and OS versions.

Security Testing: Identified and resolved vulnerabilities to safeguard client information.

Usability Testing: Enhanced user experience with intuitive navigation and design.

Automation Frameworks: Implemented for faster, repeatable, and efficient testing cycles.

Impact

Improved App Stability: Reduced crash rates by 40%, ensuring reliability.

Enhanced User Satisfaction: Positive feedback from clients due to a seamless and secure mobile experience.

Faster Release Cycles: Testing automation reduced deployment time by 30%, enabling quicker updates.

Transforming IT Service Management with ServiceNow

Probelm

Client: Leading IT Services Partner

Manual inefficiencies slowing down IT Service Management (ITSM) processes.

Frequent delays in issue resolution impacting operational performance.

Need for a scalable, robust solution to support growth.

Solutions

Our experts implemented ServiceNow ITSM, delivering

Automated Workflows: Streamlined IT processes by eliminating manual tasks.

Robust QA Testing: Ensured the solution was scalable, secure, and resilient.

Impact

Faster IT Resolution Times: Enhanced response and resolution speeds across the organization.

Improved System Stability: Minimized downtime and bolstered operational efficiency.

Future-Ready Framework: Delivered a scalable ITSM framework tailored to evolving business needs.

Java Development for Financial Services – Global Payments Client

Probelm

Client Challenges

Increasing transaction volumes exposed limitations in existing backend systems.

Processing delays and reliability issues impacting user experience.

Need for advanced security measures to safeguard sensitive data.

Solutions

We developed secure, scalable Java-based backend systems, featuring:

High-Performance Architecture: Engineered to handle millions of daily transactions.

Advanced Security Protocols: Ensured compliance and data protection.

System Optimization: Enhanced processing speed and reliability for seamless transactions.

Impact

Faster Processing Times: Significant reduction in transaction delays.

Improved Reliability: Robust systems ensured seamless operations, even during peak loads.

Enhanced Security: Instilled trust among millions of users globally.

This collaboration showcases how our cutting-edge solutions drive efficiency, security, and scalability, empowering businesses to exceed user expectations.

Streamlining Project Management for a Professional Services Leader

Probelm

Client: Leading Professional Services Firm

Managing large-scale projects with multiple stakeholders was challenging due to lack of visibility and coordination.
Delays and budget overruns impacted project outcomes and stakeholder satisfaction.

Solutions

We implemented Agile project management practices and tools, delivering:

Real-Time Dashboards: Provided transparent tracking of milestones, timelines, and budgets.

Enhanced Collaboration: Facilitated seamless communication among stakeholders.

Agile Methodologies: Ensured flexibility and adaptability for dynamic project needs.

Impact

On-Time, On-Budget Delivery: Met every milestone within the defined parameters.

20% Increase in Project Satisfaction: Improved transparency and collaboration delighted stakeholders.

Streamlined management of large-scale, multi-stakeholder projects, setting a new benchmark for success.

This project underscores how the right methodologies and tools transform complex project management into a collaborative success story.

Transforming Task Management for a Professional Services Client

Probelm

Client: Professional Services Firm

Lack of streamlined task tracking led to inefficiencies and delays in project delivery.

Limited visibility into task progress affected team collaboration and accountability.

Solutions

We implemented Jira to revolutionize task management, featuring:

Custom Workflows: Tailored to align with the client’s operational requirements.

Dynamic Dashboards: Provided real-time task tracking and performance insights.

Enhanced Collaboration: Simplified communication and coordination across teams.

Impact

25% Reduction in Task Completion Times: Streamlined workflows accelerated project timelines.

Significant Boost in Productivity: Teams collaborated more effectively, driving operational efficiency.

Improved Task Visibility: Empowered teams to deliver better results with confidence.

use cases

Building a Scalable Data Strategy and AI Roadmap

Customized Data Lake Solution Across Hybrid & Multi-Cloud Environments

Leveraging Data Science for Predictive Maintenance in a Manufacturing Company

Data Collection and Integration: Collected real-time data from IoT sensors installed on machines (e.g., vibration, temperature, and RPM). Integrated historical maintenance records and downtime logs with sensor data for a comprehensive dataset.

Data Processing and Feature Engineering: Cleaned and preprocessed the data to remove noise. Identified key features correlated with machine failures, such as unusual temperature spikes or erratic vibration patterns.

Predictive Modeling: Developed machine learning models (Random Forest and Gradient Boosting) to predict the likelihood of machine failure within a specified time window. Used anomaly detection algorithms to flag unusual patterns in real time.

Proactive Maintenance Scheduling:

40%​

30%​

20%

Data Management for a USA-Based Law Firm

Client: Leading Law Firm in the USA

Data Management Solution

Automating contract management for a Mid-sized law firm

70%

60%​

20%

Accelerating Data Modernization and BI Capabilities

Point-of-Sale (POS) Automation Using UiPath

RPA-Powered POS Automation by Asoft and UiPath

Key Steps Included:

70%​

500+ Hours​

60%

40%

Automating Invoice Reconciliation Using UiPath

70%

1000+ Hours​

30%

Automating Invoice Reconciliation Using UiPath

80%​

1200+ Hours​

40%

Transforming HR Processes with Generative AI

27%

200 Hours

40%

GenAI Powered AIOps​

27%​

1200+ Hours​

38%

Mobile App for Financial Dashboards

Challenges Faced by the Finance Organization

Key Features:

Enhancing Mobile App Performance for a USA-Based Law Firm

Client: Leading Law Firm in the USA

Transforming IT Service Management with ServiceNow

Client: Leading IT Services Partner

Java Development for Financial Services – Global Payments Client ​

Client Challenges

Streamlining Project Management for a Professional Services Leader​

Client: Leading Professional Services Firm

Transforming Task Management for a Professional Services Client​

Client: Professional Services Firm

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Data Collection and Integration:
Collected real-time data from IoT sensors installed on machines (e.g., vibration, temperature, and RPM).

Integrated historical maintenance records and downtime logs with sensor data for a comprehensive dataset.

Data Processing and Feature Engineering:
Cleaned and preprocessed the data to remove noise.

Identified key features correlated with machine failures, such as unusual temperature spikes or erratic vibration patterns.

Predictive Modeling:
Developed machine learning models (Random Forest and Gradient Boosting) to predict the likelihood of machine failure within a specified time window.

Used anomaly detection algorithms to flag unusual patterns in real time.

40%

30%

60%

70%

500+ Hours

1000+ Hours

80%

1200+ Hours

GenAI Powered AIOps

27%

1200+ Hours

Java Development for Financial Services – Global Payments Client

Streamlining Project Management for a Professional Services Leader

Transforming Task Management for a Professional Services Client