Our client, a top IT Asset Management (ITAM) company, wanted a faster and more reliable way to manage their growing platform. They needed a partner who could help them scale, improve quality, and speed up software releases.
Prolifics made it happen.
The Challenge
The client faced big roadblocks:
Previous vendors couldn’t scale or meet quality needs.
They needed a stable .NET development team.
Release cycles were too slow for market demand.
Costs were rising, and flexibility was low.
They needed a real partner, not another short-term vendor.
Our Approach
We focused on speed, stability, and teamwork.
Here’s what we did:
Built a hybrid team with onshore and offshore experts.
Used agile methods for faster delivery and feedback.
Became a strategic partner, not just a service provider.
Created a strong, long-term roadmap with the client.
The Solution
We set up a full .NET Software Factory to support growth and innovation.
Key results:
Built and launched a major new platform version.
Improved performance, usability, and reliability.
Delivered continuous updates through agile sprints.
Designed a scalable model that adjusts with demand.
Cut costs without sacrificing quality.
The Results
Faster releases and higher product quality.
Smooth operations during growth periods.
Ongoing innovation and system improvements.
A trusted partnership that continues after eight years.
The client now runs one of the most advanced platforms in IT asset management, with Prolifics as their long-term partner.
Why Clients Choose Prolifics
End-to-end digital solutions.
Expertise in data, automation, AI, and integration.
Proven success across industries.
Flexible and cost-efficient delivery models.
We help organizations turn complex challenges into measurable results.
Let’s Build What’s Next
Looking to modernize your platform and speed up growth? Prolifics can help you design smarter workflows, scale faster, and reduce risk.
Talk to our experts today. Start Your Digital Transformation
When rapid growth began to challenge visibility and efficiency across multiple business units, a leading specialty chemicals manufacturer turned to Prolifics for a unified digital transformation. With 10 manufacturing units, 6 warehouses, and 3 R&D centers spread across India, the client needed more than just an ERP upgrade, they needed an intelligent backbone for their expanding enterprise.
Prolifics deployed SAP S/4HANA Cloud (Private Edition) to transform complex workflows into seamless, data-driven operations. From finance to production, sales, and supply chain, every process was connected, creating real-time visibility, automated compliance, and smarter decision-making.
The result? A fully digitised enterprise with unified operations, enhanced compliance, and future-ready scalability. What once required manual intervention is now automated and intelligent, positioning the client as a digital-first leader in the specialty chemicals industry, ready to leverage IoT, AI, and analytics.
Prolifics didn’t just implement technology; we engineered transformation. See how we turned complexity into clarity and vision into value.
About the Client A chemical manufacturer that makes phenols, xylenols, and other materials for life sciences and advanced industries. The company follows green chemistry and runs eco-friendly plants with zero liquid discharge.
The Challenge The client’s old systems:
Couldn’t give real-time data
Made demand forecasting hard
Had errors in cost tracking
Lacked end-to-end material traceability
Missed quality checks and compliance tracking
They needed one connected system to manage all operations accurately and efficiently.
Our Solution
Prolifics used SAP S/4HANA Cloud Private Edition to bring all departments together, finance, production, sales, and R&D, into one smart system.
Key Features
Real-time insights: Better visibility into all operations
Digital records: Easy access to certificates and reports
Automation: Faster approvals, invoicing, and compliance
Integration: Connected SAP and non-SAP systems
Accuracy: Automated weight capture and tax filing
What Changed
After implementation, the company saw:
Smarter Planning: Accurate forecasts reduced waste
Better Cost Control: Real-time financial data improved decisions
Full Traceability: Every product tracked end-to-end
Easy Compliance: Automated filings and audit-ready records
Higher Efficiency: Less manual work, faster processes
The banking and finance industry is no stranger to innovation. From ATMs to digital wallets, every technological leap has reshaped how institutions interact with customers and manage risk. Today, the next era of transformation is here, Artificial Intelligence (AI).
IDC’s latest report on Agentic AI: Driving a New Wave of Banking Transformation mentioned that 78% of Banks are actively exploring Agentic AI, and banks are using Agentic AI to improve customer experience (39%), operational efficiency (36%), and data-driven decision making (28%).
As financial institutions move from experimentation to tangible outcomes, IBM’s latest agentic AI capabilities are redefining what’s possible in banking, empowering institutions to innovate faster, operate smarter, and build resilient systems for the future.
From Experimentation to Impact: AI’s Maturity Moment in Banking
The early wave of AI in finance was marked by curiosity and pilot projects. Banks experimented with chatbots, credit risk models, and process automation, exploring the boundaries of machine learning. Now, the tide has shifted. AI has evolved from exploration to execution, where measurable business impact takes precedence over experimentation.
IBM’s hybrid cloud and automation-driven approach to AI enable banks to scale innovation while managing risk. The result? Financial institutions can now deploy AI solutions that optimize operations, improve compliance, and drive revenue, not just in labs, but across the enterprise.
Solving Real-World Challenges: How AI Transforms Financial Operations
AI is no longer confined to the data science department. Its influence now permeates every corner of the financial services ecosystem, from risk oversight to customer engagement. Let’s explore how IBM’s AI technologies are solving some of the industry’s most pressing challenges.
1. Overcoming Skill Shortages with Accelerated Code Development
With the rise of agentic AI, coding bottlenecks are becoming a thing of the past. IBM’s AI-powered development tools empower engineers to automate code generation, refactor legacy systems, and build new applications faster. This not only bridges the skills gap but also frees up technical teams to focus on innovation rather than routine maintenance.
2. Reducing Operational Downtime
AI’s predictive capabilities are being leveraged to detect anomalies in IT systems before they cause disruptions. Through AIOps and intelligent monitoring, IBM enables banks to minimize downtime, maintain uptime for mission-critical services, and deliver seamless digital banking experiences 24/7.
3. Enhancing Risk Management and Compliance
Regulatory compliance in banking is complex, but AI can make it manageable. IBM’s AI models use natural language processing to interpret new regulations, analyze transactions for suspicious patterns, and ensure real-time compliance. This proactive approach reduces the risk of fines and reputational damage.
4. Driving Digital Transformation
AI is the driving force behind modern, data-driven banking ecosystems. By integrating AI across hybrid cloud platforms, IBM helps financial institutions transition from traditional operations to intelligent systems that learn, adapt, and evolve. These AI-powered transformations enable faster decision-making and improved business agility.
5. Enhancing Customer Experience
In the experience-driven banking era, personalization is power. IBM’s AI solutions enable banks to deliver hyper-personalized experiences, predicting customer needs before they arise. From chatbots that handle inquiries instantly to recommendation engines that tailor financial products, AI humanizes banking interactions at scale.
6. Accelerating Application Modernization
Legacy systems are often the biggest barrier to innovation. IBM’s application modernization frameworks, supported by AI and automation, allow banks to modernize core systems without disrupting ongoing operations. This transformation improves agility, reduces costs, and enables integration with emerging fintech ecosystems.
7. Reshaping Onboarding and Employee Experience
AI isn’t just transforming customer journeys, it’s reshaping employee workflows too. From AI-driven onboarding assistants that accelerate new employee integration to digital learning systems that personalize upskilling, IBM’s AI makes banking a smarter place to work.
8. Boosting Efficiency Across Business Processes
With automation powered by IBM Watson and AI Operations, manual workflows can be digitized, monitored, and optimized. AI streamlines processes such as loan approvals, fraud detection, and data reconciliation, drastically improving turnaround times and accuracy.
IBM: A Century of Financial Innovation and Trust
IBM’s legacy in banking stretches back more than a century. Today, over 90 of the world’s largest banks trust IBM to drive transformation across core operations, security, and innovation. That deep partnership with financial institutions forms the foundation for the agentic AI revolution.
Through IBM Consulting, banks gain access to advisory, strategy, and implementation services tailored to the demands of modern finance. These capabilities span:
Core banking modernization
Payments transformation
ISV-enabled banking
Intelligent business operations
Risk management and regulatory compliance
Cybersecurity and fraud detection
IBM’s hybrid approach ensures banks can seamlessly integrate AI into their ecosystems, whether on-premises, in private clouds, or across multiple cloud providers.
IBM Technology: The Engine Powering Financial AI
Behind every AI success story in finance lies IBM’s cutting-edge technology portfolio. From data management and analytics to security and cloud computing, IBM provides the tools institutions need to modernize, innovate, and compete.
AI and Automation: Streamlining operations, improving productivity, and predicting outcomes.
Data and Cloud: Delivering flexibility and resilience through a hybrid cloud infrastructure.
Security and Compliance: Ensuring trust through end-to-end encryption and governance.
These technologies empower financial organizations to build adaptive, intelligent architectures, ones that can process billions of data points in real time, spot opportunities, and manage risks proactively.
Real-World Use Cases: AI in Action Across Banking & Finance
AI-Powered Fraud Detection
Banks are leveraging IBM’s AI algorithms to analyze billions of transactions daily, identifying anomalies that traditional systems might miss. This results in faster fraud detection and fewer false positives, protecting both customers and profits.
Predictive Risk Management
AI models simulate various market conditions, helping financial institutions anticipate risks before they manifest. Through data-driven insights, banks can safeguard investments and maintain portfolio stability even during volatile times.
AI in Payments and Customer Service
AI chatbots and virtual assistants are transforming customer interactions, offering instant responses and personalized support. Meanwhile, AI in payments enables smarter routing, reduced transaction failures, and improved cash flow management.
Customer Stories: AI Success in the Financial Sector
A Global Bank’s Compliance Revolution: Leveraging IBM Watson, a leading global bank automated 70% of its compliance tasks, reducing regulatory review times from weeks to hours.
A Regional Lender’s Digital Acceleration: Partnering with IBM Consulting, a mid-sized lender modernized its core systems with AI-driven automation, improving loan approval times by 45% and boosting customer satisfaction scores.
A Financial Services Leader’s Fraud Defense: Using IBM’s hybrid cloud and AI-based monitoring, this firm achieved a 60% reduction in fraudulent activity while cutting manual investigation hours in half.
Conclusion: The Power of Partnership — IBM + Prolifics
As the financial industry evolves, transformation demands both innovation and expertise. That’s where the partnership between IBM and Prolifics stands apart.
Prolifics combines its deep domain experience in digital transformation, data, AI, and automation with IBM’s powerful AI and hybrid cloud technologies to deliver end-to-end modernization solutions for banks and financial institutions. Together, they enable organizations to accelerate AI adoption, enhance compliance, and shape the future of intelligent banking.
The future of banking isn’t just digital, it’s intelligent, resilient, and agentic. And with IBM and Prolifics leading the charge, the next chapter of financial innovation is already being written.
Join Prolifics and IBM: Unlocking the Future of Banking & Finance with Agentic AI
Prolifics and IBM invite you to attend Unlocking the Future of Banking & Finance with Agentic AI, an exclusive session showcasing real-world banking use cases, IBM’s latest AI capabilities, and actionable insights for financial innovation and resilience.
This event offers a firsthand look at how IBM’s agentic AI, powered by automation and hybrid cloud, is redefining the industry’s digital core, with live demonstrations, customer stories, and expert discussions on the future of financial transformation.
At the Snowflake Summit 2025, the data cloud pioneer unveiled a new generation of AI-powered agents that promise to reshape how businesses interact with data.
Introducing Snowflake Intelligence and the Data Science Agent, the company aims to bridge the long-standing divide between raw data and actionable insight, putting analytics directly in the hands of anyone, not just data specialists.
Talking to Data, Not Coding It
Snowflake Intelligence represents a significant leap in simplifying enterprise analytics. Instead of relying on SQL experts or business intelligence teams, users can now query complex data sets in plain language. Imagine typing a request like “Show me last quarter’s top-selling product and the reason behind its performance.”
Behind the scenes, Snowflake’s Cortex AI engine interprets the request using large language models (including Anthropic’s Claude) to generate optimized SQL queries. It then scans both structured and unstructured sources, spreadsheets, CRM logs, PDFs, or support notes before synthesizing a narrative answer complete with visual insights. Users get contextual explanations, next-step suggestions, and even workflow automations, all within the Snowflake ecosystem.
By allowing data conversations instead of code writing, Snowflake Intelligence transforms data exploration into a natural dialogue. Moreover, because all processing happens within Snowflake’s governed environment, enterprises maintain compliance, data lineage, and security integrity. This is data democracy with enterprise-grade guardrails.
Automating the Science of Machine Learning
For technical teams, Snowflake’s Data Science Agent targets another pain point, building and deploying machine learning (ML) models. Instead of juggling notebooks, tools, and pipelines, data scientists can prompt the agent to “build a churn prediction model using 12 months of usage data.”
The AI then auto-generates an end-to-end ML pipeline inside a Snowflake Notebook, covering data preparation, feature engineering, model selection, and evaluation. Engineers can review and modify any step, and with one command, deploy it as a Snowflake ML Job. No exports, no external orchestration tools, no security compromises.
Snowflake’s platform even handles GPU orchestration, experiment tracking, and model serving, making ML pipelines reproducible, transparent, and production-ready.
A Shift Toward Conversational Intelligence
Snowflake’s vision aligns with the growing enterprise trend of AI-driven automation and no-code empowerment. From business leaders seeking instant insight to data scientists aiming to accelerate model deployment, these AI agents transform how organizations think about analytics. The result: faster decision-making, improved data literacy, and reduced dependency on specialized intermediaries.
The Future: From Static Dashboards to Dynamic Dialogue
With Snowflake’s AI agents, the company is redefining what data analytics means, moving from static dashboards to interactive, conversational systems. By embedding intelligence directly within its Data Cloud, Snowflake empowers enterprises to not just analyze but act on data in real time.
In a world where insight speed determines competitiveness, Snowflake’s AI agents mark a pivotal step toward truly democratized data intelligence, where every question, from any user, can spark a meaningful, data-backed answer.
Modernise ERPHow Prolifics Helped a Global Manufacturer Modernize Its Business
A leading manufacturer of precision-engineered valves and control accessories partnered with Prolifics to modernize its enterprise systems. With operations across the US, India, and multiple global regions, the client needed a more agile, connected, and data-driven foundation for growth.
The Challenge
The company was using an aging SAP ECC system that was:
Expensive to maintain and hard to scale
Slowing decision-making with overnight reporting
Built on complex, redundant data structures
Unable to integrate with modern applications
Limited in supporting new subsidiaries and operations
They needed a cloud-based, intelligent ERP solution to boost agility and performance.
The Prolifics Approach
Prolifics designed a step-by-step migration to SAP S/4HANA Cloud (Private Edition) to simplify architecture, improve speed, and support global scalability.
Key Steps
Assessment – Identified inefficiencies and integration gaps
Strategic Migration Plan – Ensured smooth transition and continuity
Cloud Enablement – Moved operations to a secure, cost-efficient environment
Data Optimization – Removed redundant data for faster processing
Analytics Integration – Deployed SAP Analytics Cloud for real-time insights
Standardization – Unified finance, production, and supply chain processes
Change Management – Ensured user adoption with training and support
The Solution
Unified Digital Core
SAP S/4HANA Cloud – Streamlined data and faster transactions
SAP DMS – Centralized, audit-ready digital documentation
SAP BTP – Supported workflow automation and future innovation
SAP Integration Suite – Connected internal systems and external partners
Business Areas Transformed
Finance and Controlling – Automated payables, reconciliation, and forecasting
Sales and Distribution – Unified global sales and real-time order management
Procurement and Sourcing – Simplified purchasing and improved collaboration
Production and Supply Chain – Supported MTS/MTO operations and logistics tracking
Quality Management – Digitized audits, certifications, and compliance tracking
Maintenance and Analytics – Centralized planning and real-time visibility
The Results
The migration unlocked measurable benefits across the enterprise:
✅ Real-Time Business Visibility – Leadership gained live dashboards and instant analytics ✅ Streamlined Data Management – Faster processing and more accurate insights ✅ Higher Operational Efficiency – Reduced manual work and faster execution ✅ Stronger Compliance – Audit trails and e-invoicing reinforced trust ✅ Greater Scalability – Easy to expand globally without added infrastructure ✅ Lower Operating Costs – Less downtime, fewer upgrades, and optimized maintenance
“Prolifics’ SAP expertise and disciplined execution made this transformation seamless. Our ERP is now a powerful engine for growth and innovation.” — Client Leadership Team
Transform Your ERP with Prolifics
Ready to migrate to SAP S/4HANA or streamline your ERP? Prolifics helps you modernize, automate, and scale your operations so you can move faster and achieve more.
Enterprises have mastered collecting data. The hard part now is understanding it at scale, across tools, clouds, and business domains. That is why metadata fabrics are gaining attention, because they create a connective intelligence layer that makes data findable, trustworthy, and ready for AI
Leading references describe data fabrics as architectures that ingest and analyze metadata to recommend integration, organization, and governance actions. A metadata fabric focuses that same power on the metadata itself, turning it into a living asset that guides how data is used, secured, and improved across the enterprise.
What is a metadata fabric in data strategy?
A metadata fabric is a unified, intelligent layer that collects technical, business, and operational metadata from all your platforms, then activates it in real time. Think of it as the orchestration brain for context, lineage, policies, and quality signals that every downstream analytics or AI workflow depends on.
In practice, it builds on the industry’s move from static catalogs to active metadata, where metadata is continuously processed to monitor health, recommend design changes, and even trigger actions in adjacent tools.
Types of Metadata is categorized into three main types:
Technical Metadata: Defines the structure of data, data types, formats, and schemas, helping engineers ensure proper data system design.
Operational Metadata: Tracks data processes, lineage, and timestamps, supporting data quality management, auditing, and logging.
Business Metadata: Adds business context through definitions, ownership, and classification, enabling governance and compliance.
Core principles
Unified view of metadata across warehouses, lakes, BI tools, pipelines, and SaaS apps.
Continuous processing that surfaces lineage, usage, and quality as they change.
Automation hooks that push recommendations or guardrails back into data platforms and catalogs.
Governance by design through a reusable metadata governance framework.
Metadata fabric architecture, the building blocks
A solid metadata fabric architecture typically includes four layers:
Connect and collect Connectors harvest technical metadata from databases and ETL, business metadata from glossaries, and operational metadata from query logs and monitors. Modern data fabric references emphasize that knowledge graphs and active metadata techniques make this practical at enterprise scale.
Unify and model The platform normalizes metadata into a common model, builds relationships, and maintains a knowledge graph. This enables consistent definitions, impact analysis, and enterprise search across domains.
Activate and automate AI-powered metadata management evaluates runtime patterns, flags quality or policy issues, and can recommend design changes or automate approvals and routings. Gartner calls this active metadata, which extends beyond documentation to orchestration.
Govern and measure A metadata governance framework sets ownership, controls, and stewardship workflows. Best practices include standards for naming and definitions, audits, access permissions, and role clarity between data owners, stewards, and platform teams.
Data fabric vs metadata fabric
Both are modern approaches, but they are not the same. A data fabric is an architectural way to connect and deliver data across a distributed landscape. It leverages active metadata and catalogs to route and optimize data access. A metadata fabric concentrates on unifying and activating the metadata layer itself, which then guides governance, discovery, lineage, and AI readiness across any data fabric, data mesh, or hybrid setup. In short, the data fabric moves and serves data
How metadata fabrics improve data governance?
Stronger governance comes from baking policies into the fabric, not bolting them on later. A metadata-driven data strategy uses the fabric to enforce definitions, automate classification, and continuously verify compliance. References on metadata governance emphasize objectives such as making data trustworthy and consistent, aligning with enterprise policies, and preventing unauthorized use. The fabric provides the telemetry and workflows to do this day to day.
Practical wins for governance teams
Automated PII detection tied to masking or access policies.
Business glossary terms linked to physical fields, so definitions travel with data.
Steward queues that open when lineage shows a risky upstream change.
Audit trails that prove who changed what and when.
Metadata fabric for data lineage and observability
Lineage is the backbone of trust. A metadata fabric consolidates transformation histories, joins them with usage logs, and exposes end-to-end lineage and observability so teams see blast radius, SLA risk, and ownership in one view.
Industry guides note that modern catalogs and fabrics trace lineage, support impact analysis, and use knowledge graphs to map relationships, which the metadata fabric elevates into everyday operational decisions.
AI readiness: Why active metadata matters
AI initiatives require curated, well-understood, and well-governed data. Active metadata gives you the feedback loops to reach that standard, for example, identifying canonical sources, surfacing data contracts, and detecting drift in upstream systems.
Analysts frame active metadata as continuous access and processing that can monitor, evaluate, recommend, and orchestrate in third-party tools. This is precisely the behavior your AI platform needs to stay reliable and cost-efficient.
Benefits of metadata fabric for enterprises
Leaders implement a metadata fabric to reduce cycle time, risk, and cost across the data lifecycle.
Faster analytics and AI through trustworthy discovery and governed Lower compliance risk via consistent policies and provable lineage.
Higher developer productivity because impact analysis shortens change management.
Better platform economics since observability exposes unused assets and redundant pipelines. Industry sources show that modern fabrics and catalogs improve access, standardize meaning, and accelerate safe reuse, which directly translates to value.
Metadata fabric integration with data catalog
Your catalog remains the daily workspace for analysts and stewards. The fabric supercharges it by feeding in relationships, runtime signals, and governance controls. Vendor and analyst materials consistently describe catalogs as central to data fabrics, with active metadata adding real time organization, lineage, and business context. The net result is a metadata fabric platform plus catalog that delivers guided discovery, policy-aware access, and one-click impact analysis across tools.
Implementation playbook, a phased path
Baseline and scope Inventory key systems and governance pain points. Start with two or three high-value domains and the most used analytics tools.
Connectors and collection Prioritize sources that yield the richest metadata, for example, ETL jobs, warehouse query logs, lake catalogs, BI semantic layers, and identity providers.
Model and knowledge graph Normalize technical, business, and operational metadata. Establish glossary ownership and start mapping terms to physical assets.
Activate high-impact use cases Choose two automation paths first, such as automated lineage with impact analysis and policy enforcement for sensitive data access. Analyst guidance on active metadata highlights monitoring, recommendations, and orchestration as early value drivers.
Governance operating model Codify your metadata governance framework, including roles, approval workflows, quality checks, audits, and metrics for adoption. Sources recommend clear standards, periodic reviews, and cross-functional participation from stewards and platform teams.
Measure outcomes and expand Track time to find data, policy violations prevented, incidents resolved faster due to lineage, and cost savings from retiring redundant assets. Iterate domain by domain.
Frequently asked comparisons
Data catalog vs metadata management A catalog is the user interface and library of assets, while metadata management is the broader process of collecting, governing, and activating metadata. You need both, with the fabric as the orchestration layer.
Data fabric vs metadata fabric A data fabric unifies data access and movement. A metadata fabric unifies and operationalizes metadata so the whole stack becomes observable, governable, and AI-ready. They complement each other.
Key Takeaway
When building a data fabric or mesh, make the metadata layer your priority. A well-structured metadata fabric architecture connects fragmented definitions, logs, and lineage into a trusted control layer for analytics and AI.
The leaders in this space will view metadata as intelligent, reusable assets rather than static documentation. Industry experts and technology providers align on this point: active metadata should be used to observe, guide, and automate improvements across your entire data landscape.
At Prolifics, we help organizations turn this vision into reality. Our experts design and implement intelligent metadata frameworks that unify data across hybrid and multi-cloud environments, enabling better governance, observability, and AI readiness.
With our accelerators, such as automated lineage and governance integrations, we transform metadata from a back-office function into a strategic enabler for decision-making and innovation.
A global consumer packaged goods (CPG) enterprise partnered with Prolifics to overcome inefficiencies caused by outdated SAP and EDI systems. Leveraging SAP Business Technology Platform (BTP), Prolifics designed a composable, event-driven, and API-first integration architecture that enhanced agility, vendor collaboration, and operational visibility.
Through reusable integration frameworks, automated workflows, and smart monitoring, the client achieved faster time-to-market, reduced operational costs, and future readiness for AI and IoT integrations.
This transformation not only improved scalability and data transparency but also accelerated vendor onboarding and innovation across global operations, a true testament to Prolifics’ “Vision to Value, Faster” approach.
Download the Case Study to discover how Prolifics helped this CPG leader modernize and future-proof its digital ecosystem.
Managing thousands of contracts manually can be a nightmare. For one leading communications provider, its Government Affairs Division needed a better way to track and renew franchise agreements across the U.S.
They turned to Prolifics to design a simple, automated system that keeps contracts organized, compliant, and audit-ready.
The Challenge
The division struggled with:
Unstructured storage for agreements
No alerts for contract expirations
Scattered documentation
Limited visibility across cities and regions
Risk of compliance gaps and missed renewals
Our Solution
Prolifics built a complete SharePoint-based contract management system with:
Document Organization
Moved all agreements into a structured SharePoint library
Added searchable metadata (region, city, expiration date)
Workflow Automation
Set up alerts for upcoming renewals
Added approval workflows for consistent processes
Reporting & Compliance
Custom dashboards for real-time tracking
Audit-ready document structure and history
The Results
The new system delivered a measurable impact:
Timely, consistent renewals nationwide
Strong compliance for audits
Clear visibility and forecasting
Recognition as a model division within the organization
“Enable has been in constant communication with us, which allowed changes to the project to be made quickly and efficiently.” — Michael Ruger, Vice President, Government Affairs
Why Prolifics
For over 45 years, Prolifics has helped organizations modernize their systems with precision and speed. Our partnerships with IBM, Microsoft, AWS, Salesforce, and Google Cloud empower clients to work smarter and faster.
Ready to Simplify Your Contract Management?
Turn contract complexity into clarity. Let’s discuss how we can automate your workflows and strengthen compliance.
Discover how the State of Tennessee’s Administrative Office of the Courts (AOC) partnered with Prolifics to modernize its claims processing system, cutting processing times from 45 days to under 10 days, increasing transparency, and enabling a cloud-first, automation-driven ecosystem.
Prolifics’ intelligent automation and modern architecture helped AOC enhance efficiency, compliance, and scalability, empowering faster, fairer justice delivery for citizens.
Key Outcomes
Reduced claim processing time from 45 to under 10 days
Achieved real-time visibility through automated dashboards
Scaled to process 10,000+ claims per month
Integrated financial, legal, and administrative systems seamlessly
Enhanced transparency and citizen trust through automation
Explore the Full Story
Learn how automation redefined justice administration and positioned the state’s AOC for a future of digital excellence.
