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Outdated systems and antiquated technologies
are a competitive disadvantage. We are disruptors of
the outdated . Our digital transformation solutions
span from cloud to Gen AI, setting you up to
innovate today and tomorrow.

Salesforce

Data Engineering

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ServiceNow

AI & Automation

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Real Outcomes. Measurable Impact.

Examples of how we apply Salesforce, ServiceNow, Data, and AI to deliver real business results..

Selected Use Cases

Measured Impact Across Platforms and Industries

25% Reduction in Hardware Spend

Problem: Asset tracking was managed through spreadsheets with unclear ownership, over-ordering, and audit

What We Did: Implemented full lifecycle asset governance with real-time tracking, normalization, and automated workflows

  • N
    25% reduction in hardware spend
  • N
    Zero audit findings
  • N
    Real-time asset visibility
ServiceNow
30% Reduction in Software Spend

Problem: Limited visibility into software usage and entitlements led to unused licenses, audit exposure, and reactive compliance.

What We Did: Implemented software asset lifecycle management with license tracking, reconciliation, and compliance automation.

  • N
    25% reduction in hardware spend
  • N
    Reduced Shadow IT
  • N
    Audit-Ready Compliance
  • N
    Real-time monitoring
ServiceNow
50% Faster Ticket Reporting

Problem: Users avoided ticketing systems due to friction, leading to delays, poor data capture, and service desk overload

What We Did: Enabled voice-driven ticket creation and real-time interaction within ServiceNow

  • N
    50% faster issue reporting
  • N
    Reduced service desk burden
  • N
    Faster intake
ServiceNow
Improved Pipeline Visibility & Sales Adoption

Problem: Sales reps managed deals outside Salesforce due to complex quoting, leading to poor pipeline visibility and low adoption.

What We Did: Implemented Agentforce-driven intake with automated opportunity and quote creation, integrated with email

  • N
    Increased Salesforce adoption for deal tracking
  • N
    Improved pipeline visibility and accuracy
  • N
    Reduced manual data entry
  • N
    Standardized intake across sales process
Salesforce
AI-Driven Quoting and Opportunity Creation

Problem: Manual quote intake across Excel/PDF formats caused delays, inconsistencies, and incomplete deal data

What We Did: Built AI-driven intake workflow using Agentforce to collect missing data and auto-create opportunities and quotes

  • N
    Faster quote turnaround
  • N
    Improved data consistency
  • N
    Reduced manual effort
  • N
    Increased deal conversion readiness
Salesforce
Unified Customer View Across Systems

Problem: Customer data was fragmented across multiple systems, limiting visibility and coordination across teams

What We Did: Integrated Salesforce with ERP and external systems to create a unified customer data model

  • N
    Single source of truth for customer data
  • N
    Improved cross-team collaboration
  • N
    Better customer insights and engagement
  • N
    Reduced duplicate data and inconsistencies
Salesforce
70% Faster Reporting

Problem: Data was fragmented across multiple systems, making reporting slow, inconsistent, and manual

What We Did: Unified data into a scalable Snowflake platform with real-time pipelines and centralized reporting

  • N
    70% Faster Reporting
  • N
    30% reduction in data infrastructure costs
  • N
    Single Source of Truth
  • N
    Real-time Analytics
Data Engineering
Planning Cycle Time Reduced from Hours to Seconds

Problem: Planners relied on fragmented systems and manual reconciliation, slowing decision-making and limiting visibility into risks

What We Did: Built a multi-agent AI system on Databricks enabling natural language queries, real-time data access, and automated reasoning

  • N
    Planning cycle time reduced from hours to seconds
  • N
    Early risk detection before customer impact
  • N
    Improved root-cause analysis and decision-making
  • N
    Increased planner efficiency at scale
ServiceNow
Data Engineering
40% Increase in Real-Time Data Availability

Problem: Data was siloed across ERP, CRM, and legacy systems with inconsistent reporting and low trust in metrics

What We Did: Migrated 10TB+ data into Databricks and implemented automated pipelines, governance, and centralized reporting.

  • N
    40% increase in near real-time data availability
  • N
    70% improvement in data trust
  • N
    30% improvement in process efficiency
  • N
    $100K annual savings from ELT optimization
  • N
    Centralized data governance and security
Data Engineering
99.6% Auto-Reconciliation Rate

Problem: Manual invoice processing across multiple formats caused delays, errors, and significant manual effort

What We Did: Built AI-powered invoice reconciliation using OCR and NLP across structured and unstructured formats

  • N
    99.6% auto-reconciliation rate
  • N
    600+ hours saved monthly
  • N
    15% improvement in accuracy
AI & Automation
4x Increase in Call Handling Capacity

Asset Tracking was managed through spreadsheets with under-ownership and over-ordering. and audit risk

  • N
    4x more calls handled per hour
  • N
    100+ automated outbound calls daily
  • N
    24/7 availability
ServiceNow
AI & Automation
50% Reduction in Data Migration Effort

Problem: Manual schema mapping, SQL compatibility issues, and data validation created long migration cycles and high risk

What We Did: Deployed AI-driven migration accelerator for schema mapping, code conversion, and automated validation

  • N
    40–50% reduction in migration effort
  • N
    Faster migration cycles
  • N
    Reduced risk of production errors
  • N
    Improved data quality and validation accuracy
AI & Automation
50% Reduction in Data Migration Effort

Problem: Manual schema mapping, SQL compatibility issues, and data validation created long migration cycles and high risk

What We Did: Deployed AI-driven migration accelerator for schema mapping, code conversion, and automated validation

  • N
    40–50% reduction in migration effort
  • N
    Faster migration cycles
  • N
    Reduced risk of production errors
  • N
    Improved data quality and validation accuracy
Multi-Platform
AI-Driven Quoting and Opportunity Creation

Problem: Manual quote intake across Excel/PDF formats caused delays, inconsistencies, and incomplete deal data

What We Did: Built AI-driven intake workflow using Agentforce to collect missing data and auto-create opportunities and quotes

  • N
    Faster quote turnaround
  • N
    Improved data consistency
  • N
    Reduced manual effort
  • N
    Increased deal conversion readiness
ServiceNow
Multi-Platform
Guided Selling and Workflow Automation

Problem: Sales teams lacked guidance on next-best actions and relied on manual updates

What We Did: Implemented guided selling with AI-driven recommendations and workflow automation

  • N
    Improved sales productivity
  • N
    More consistent deal execution
  • N
    Reduced manual CRM updates
  • N
    Better forecasting inputs
Multi-Platform

Find a Use Case That Matches Your Situation

We can walk through a relevant example based on your current systems and priorities.