Digital excellence isn’t optional in today’s financial landscape, it’s the standard. When one of the largest U.S. financial institutions needed to modernize more than 110 mission-critical internal applications, they partnered with Prolifics to turn years of tech debt, fragmented workflows, and compliance challenges into a scalable, secure, and future-ready ecosystem.
The Client: A Top U.S. Financial Institution Ready for Change
This institution serves millions of customers through extensive retail, commercial, and investment banking operations. But behind the scenes, over 110 internal applications were built on outdated platforms, including:
Microsoft Access
Visual Basic (VB)
Excel VBA
ASP
Lotus Notes
These legacy systems had become: ❌ Costly to maintain ❌ Difficult to update ❌ Misaligned with IT & security standards ❌ Bottlenecks to compliance and release management ❌ Barriers to innovation and business agility
The Challenge: Outdated Applications, Growing Operational Risk
The bank’s critical systems were creating:
Release management delays
Compliance inconsistencies
Integration issues with modern platforms
Rising maintenance costs
Increased operational risk
Limited scalability
Our Proven Approach to Modernization
1. Comprehensive Assessment
We evaluated 110+ applications across functionality, compliance, cost, and integration readiness.
2. Strategic Modernization Roadmap
Leveraged the DMAIC framework to define the right modernization path, modernize, consolidate, or retire.
3. Prioritization & Rationalization
Identified redundancies, consolidated applications, and optimized maintenance overhead.
4. Agile Execution at Scale
A 25+ member cross-functional Dev+QA team delivered iterative modernization with continuous compliance checks.
The Prolifics Solution: Modern, Secure, Scalable
✔ Migration Roadmap for 90+ Applications
A phased, efficient, and risk-controlled modernization plan.
✔ .NET Modernization Framework
Selected for scalability, maintainability, and enterprise alignment.
✔ Security & Compliance-Driven Development
Every application validated against internal SOA, audit, and security standards.
✔ DevOps Automation
Modern pipelines enabled faster, error-free, and repeatable releases.
✔ High-Quality Engineering & Testing
Dedicated QA ensured performance, data integrity, and seamless user adoption.
✔ Governance & Knowledge Transfer
Ensuring long-term independence, sustainability, and operational excellence.
The Results: A Modern Banking Backbone
The bank’s leadership reported measurable, business-driven outcomes:
⬆ Improved Compliance
All applications aligned with enterprise IT, security, and SOA standards.
⬆ Faster Release Cycles
Automated pipelines and standardized releases accelerated time-to-market.
⬇ Reduced Risk & Costs
Legacy systems were consolidated and modernized, cutting maintenance overhead.
⬆ Enhanced Agility
A future-ready platform capable of supporting innovation, automation, and evolving regulatory needs.
See how we helped them streamline operations, reduce risk, and accelerate innovation.
Get the complete story, including the challenges, strategy, execution model, and transformational results
In 2026, as enterprises accelerate deployments of generative AI (GenAI) models and large language models (LLMs), managing security, compliance, cost, and reliability becomes a paramount challenge. That is where Databricks AI Gateway stakes its claim, offering a unified, enterprise-grade control plane to govern, observe, and scale AI usage across the organization.
What is Databricks AI Gateway?
Databricks AI Gateway, also known as Mosaic AI Gateway, provides a central API-based entry point for all AI model interactions, whether they involve foundation models, open-source LLMs, custom models, or AI agents. With a single unified API for AI models, development teams no longer need to manage multiple endpoints or build bespoke integrations for each provider.
This unified access simplifies the adoption of new LLMs such as GPT-5 or the migration across providers. It enables model switchovers, experimentation, or fallback strategies without refactoring application logic.
Core Capabilities That Matter
1. Unified model access across providers: Whether the AI model is from OpenAI, Meta, an open-source ecosystem, or a custom internal build, AI Gateway routes all calls through a consistent interface, reducing integration complexity.
2. Governance, security, and compliance: Built-in guardrails allow organizations to enforce policies such as filtering PII, blocking unsafe content, applying role-based access, defining permissions, and setting rate limits, supporting enterprise AI governance at scale.
3. Observability and monitoring: Every request and response, along with metadata such as token usage, model version, and identity information, is logged into the lakehouse via inference tables. Teams can audit, debug, evaluate model quality, analyze cost, and produce compliance reports using native platform tools, strengthening GenAI observability and monitoring across applications.
4. Production-grade reliability and traffic management: AI Gateway supports load balancing, provider fallback logic, and dynamic AI model traffic management across multiple LLMs. Applications stay online even if a provider experiences downtime or rate limiting.
5. Cost and usage control: Centralized tracking of AI usage across all models, teams, and applications enables better financial discipline and simplifies budgeting and chargeback models.
Why It Matters for Partners and Customers
For enterprises and service providers aiming to embed GenAI at scale, AI Gateway addresses critical challenges such as fragmentation, risk management, governance, scalability, and cost unpredictability.
• Faster time to value: With a unified API, partners can integrate GenAI features into applications faster, without building custom connectors for each model.
• Safe and compliant AI adoption: Guardrails and audit trails support regulatory, privacy, and internal compliance needs especially important for industries that require strong enterprise AI governance.
• Scalable operations: Central monitoring and governance ensure policy consistency as AI usage expands across teams and geographies.
• Flexibility and future readiness: The ability to switch between proprietary models, open-source models, and custom-trained models ensures organizations avoid vendor lock-in.
Use Cases: Where AI Gateway Excels
• Enterprise-wide GenAI rollouts across marketing, analytics, R&D, operations, and customer support. • Embedding GenAI into commercial products such as chatbots, code assistants, and summarization tools. • Regulated industries that require strict governance, audit logs, and PII controls. • A/B testing and model optimization across multiple LLMs using dynamic routing and AI model traffic management.
Conclusion: AI Gateway as a Strategic Enabler
With Databricks AI Gateway, organizations finally have a secure, scalable, and resilient foundation for rolling out GenAI without integration challenges, compliance risks, or cost overruns.
For partners such as system integrators and consulting firms, including Prolifics, AI Gateway creates an opportunity to build enterprise-grade GenAI solutions backed by governance, observability, and operational excellence.
As GenAI adoption accelerates, integrating Databricks AI Gateway into your architecture becomes a strategic decision that ensures reliability, compliance, and scalability.
Partner with Prolifics to Maximize Your AI ROI
If you are considering GenAI at enterprise scale but are concerned about compliance, cost, governance, or operational overhead, the combination of Databricks AI Gateway and Prolifics provides the ideal foundation.
Partner with Prolifics and let our data and AI experts help you design, deploy, and manage a secure and scalable AI architecture on Databricks. From data strategy to Lakehouse modernization to end to end GenAI applications and agents, we deliver measurable results.
Let us help you build the next generation of AI powered enterprise solutions with governance, performance, and impact.
Healthcare organizations have more data than ever, yet the systems that hold this information often remain fragmented and insecure. Medical records sit in isolated databases, and every provider protects their own version of a patient’s health story. This slows down treatment, limits collaboration, and raises privacy concerns.
The need for safe and frictionless data movement is stronger today than at any point in modern healthcare. This is why blockchain in healthcare data sharing is gaining attention. Blockchain is reshaping how organizations exchange information, improving trust, transparency, and patient control. Blockchain technology in healthcare organizations can exchange data with greater trust, transparency, and patient control.
The Problem with Today’s Healthcare Data Landscape
Despite its promise, blockchain adoption faces notable challenges. Scalability remains a critical concern, as healthcare generates high-volume, high-velocity data that current blockchain networks struggle to process efficiently.
Regulatory compliance adds complexity, as frameworks like HIPAA and GDPR demand strict oversight of sensitive data, a difficult fit for decentralized architectures. Additionally, integrating blockchain with legacy healthcare systems requires significant investment, technical alignment, and organizational readiness.
Key challenges include:
Siloed electronic systems across hospitals and clinics
Outdated security frameworks that struggle to protect sensitive records
Duplicate versions of patient histories across different providers
Minimal patients say in how their data is accessed or shared
High administrative time spent reconciling inconsistent data
These limitations make collaboration challenging and slow down medical decision-making. They also spark a larger conversation around privacy, which is why organizations are looking to blockchain technology in healthcare as a long-term solution.
What Blockchain Brings to Healthcare Data
One of the most promising uses of blockchain lies in Electronic Health Records (EHRs). A blockchain-enabled EHR system ensures that patient information remains consistent, tamper-proof, and accessible to authorized providers. Patients can manage consent dynamically, improving care coordination, a major step forward for blockchain for patient data security and transparent clinical workflows.
Blockchain also enhances prescription management, reducing fraud by preventing unauthorized alteration of prescription data. Patients and clinicians gain real-time visibility into medication histories, improving adherence and safety.
In clinical research, blockchain ensures transparent, immutable trial data. Researchers can securely share verified datasets across institutions, accelerating discovery and fostering global collaboration.
Key Use Cases of Blockchain in Healthcare
Blockchain is not just theoretical in healthcare; several practical applications are already gaining traction. Some of the most promising use cases:
1. Unified Patient Records & Cross-Provider Data Sharing
By storing or indexing patient data on a distributed ledger, blockchain enables a unified and up-to-date view of a patient’s history across multiple care providers, labs, insurers, and even across countries. This is particularly valuable when a patient changes providers or travels internationally.
Blockchain-based record systems allow authorized providers to securely access relevant information, strengthening patient data privacy blockchain protections.
Secure Data Sharing Without Borders
One of the biggest advantages of blockchain is its ability to connect healthcare systems across regions, countries, and platforms. It removes geographic and technical limitations by creating a unified architecture that any authorized participant can trust.
Why this matters:
Clinicians can access complete patient records regardless of where the person lives or travels
Researchers can collaborate with global datasets while protecting confidentiality
Emergency teams gain immediate access to life-saving information
Virtual care providers can rely on accurate, verified health histories
Blockchain enables true blockchain interoperability in healthcare. Through a decentralized healthcare data exchange, hospitals and partners can exchange information in real time while preserving privacy and security.
This is a major milestone for blockchain for electronic health records (EHR) and is shaping new models of decentralized health information exchange blockchain networks.
Real World Benefits for Patients and Providers
Blockchain is not only a technology shift. It is a practical improvement to how healthcare operates every day.
Key benefits include:
Faster clinical decision making through consistent, unified patient data
Stronger privacy using patient data privacy blockchain protections
Reduced fraud, since records cannot be tampered with
Better chronic care coordination across multiple providers
Streamlined pharmaceutical tracking to prevent counterfeit products
Lower administrative burden through automated verification
These outcomes reflect the true benefits of blockchain for patient data security and create more reliable patient experiences.
Compliance and Trust Through Blockchain
Healthcare organisations must follow strict regulations that protect patient confidentiality. Blockchain helps simplify compliance through strong access control and transparent record keeping. Many HIPAA compliant blockchain healthcare solutions now combine decentralised storage, encryption, and smart contracts to meet both security and regulatory requirements.
Blockchain strengthens compliance by:
Limiting unauthorized access to protected health information
Creating permanent, verifiable logs
Maintaining consistent security across distributed systems
Reducing the risk of data breaches during data exchange
This provides a strong foundation for trust across healthcare ecosystems.
The Future of Healthcare Data Sharing
Future research will focus on enhancing blockchain scalability through off-chain solutions and sidechains. Clear regulatory frameworks are needed to guide compliant deployment. Establishing common interoperability standards will ultimately enable seamless, secure data exchange across healthcare ecosystems.
As innovation continues, blockchain is poised to become a cornerstone of secure, patient-centered healthcare data management.
What the future holds:
Seamless collaboration across global care networks
Portable digital health identities for patients
More accurate diagnostics through unified datasets
Broader research capabilities with secure data contribution
AI-infused analytics that run on trusted, verified information
The future of blockchain in healthcare data sharing points to a world where healthcare is more connected, more accurate, and far more patient centred.
Key Takeaways
Blockchain is reshaping how healthcare organisations protect, access, and share data. With stronger privacy controls, improved interoperability, and decentralized architectures, blockchain is building a more secure and patient centered future.
Healthcare providers that embrace this shift are better positioned to collaborate, innovate, and deliver higher-quality care with confidence. If your organisation is exploring blockchain in healthcare data sharing, blockchain for patient data security, blockchain interoperability in healthcare, or patient data privacy blockchain, Prolifics can guide you every step of the way.
Our experts help you identify the right use cases, design secure architectures, and implement scalable blockchain solutions that deliver real business value.
Connect with Prolifics today and start building your next-generation healthcare data strategy.
When thousands of citizens rely on timely access to birth, death, and marriage certificates, system inefficiencies are not an option. This is where citizen services modernization becomes essential.
Our client, operating under the Department of Health, is the official custodian of vital statewide records. Yet their outdated, paper-heavy systems were slowing down service delivery, driving costs up, and frustrating constituents.
Prolifics stepped in to rethink, redesign, and modernize their service ecosystem, creating a seamless, secure, and scalable digital environment that puts citizens first.
The Challenge: Outdated Systems. Rising Demands. Operational Bottlenecks.
The client faced mounting pressure to modernize their legacy infrastructure. Key challenges included:
Inability to print critical documents on time, causing long delays
Heavy manual processes and dependency on physical inventory
Expensive security paper and outdated hardware driving up costs
Bottlenecks in approvals, verification, and data access
Increasing citizen expectations for faster, digital services
Our Approach: A Future-Ready Architecture for a Modern State Agency
Prolifics performed a comprehensive assessment of the client’s existing systems to design a scalable, secure modernization roadmap. We focused on:
Eliminating manual steps and redundant processes
Improving speed, reliability, and data integrity
Ensuring seamless integration with existing state systems
Guaranteeing zero downtime during migration
Building a flexible architecture ready for future expansion
What the Client Needed, and Prolifics Delivered
Key Priority Areas
System interoperability with existing health and state systems
Top-tier security & compliance for all vital records
Cost efficiency by eliminating outdated print and paper processes
Continuous operations with no disruption to citizen services
Scalability to handle increasing demand
The Prolifics Solution: Smart, Secure, and Fully Digital
We implemented a modern service-oriented architecture (SOA) that transformed how the agency processes and distributes critical records.
Transformation Highlights
✓ Modern, Modular Architecture
Engineered for high performance and built to support heavy transaction loads.
✓ End-to-End Digital Workflows
Automated creation, verification, and request handling—reducing manual errors and delays.
✓ Secure Document Management
Encryption, role-based access, audit trails, and strict data integrity controls.
✓ Optimized Printing & Distribution
Digital authorizations drastically cut paper costs and physical inventory handling.
A leading government healthcare authority in the Middle East partnered with Prolifics to modernise its outdated licensing ecosystem. Responsible for regulating healthcare providers and facilities, the organization faced slow processing, manual workflows, legacy constraints, and rising citizen dissatisfaction. They needed a future-ready, intelligent licensing platform, and Prolifics delivered.
The Challenge: Outdated Systems Slowing Public Service Delivery
The client operated on an aging SharePoint based licensing system, which created widespread operational bottlenecks:
Lengthy processing of medical and facility license applications
Manual verification steps causing errors and heavy administrative load
No real-time dashboards, analytics, or case tracking
Limited interoperability with other government systems
Declining citizen satisfaction due to slow service delivery
Microsoft Corp. is elevating Azure Copilot AI Agents by integrating AI-driven agents that enhance cloud operations with intelligent, scalable Agentic Cloud Ops capabilities. Microsoft has unveiled enhancements to Azure Copilot that bake in multiple specialised AI agents aimed at automating complex cloud infrastructure activities.
This evolution is the shift from a conversational assistant to a full-fledged agent-orchestration engine: users can now engage a suite of six dedicated agents (Deployment, Migration, Optimization, Observability, Resiliency, Troubleshooting) known collectively as Azure Copilot six agents, that not only advise but also act, subject to approval, on behalf of infrastructure teams.
This new “Agentic Cloud Ops” model is designed to streamline everything from legacy-app migration to cost & carbon-savings optimization, and from root-cause diagnostics to resiliency planning.
By integrating Copilot directly into the Azure console, CLI, and chat experience, Microsoft is reshaping cloud modernization with AI into something more intuitive, responsive and human-friendly. You talk. Copilot acts, with context, governance and confidence.
Why this matters to you
Speed & scale: With AI agents taking on planning, deployment and remediation chores, teams can shift from reactive firefighting to proactive optimization, enabling faster time-to-value.
Cost and emissions efficiency: The Optimization agent surfaces cost-saving and sustainability recommendations side-by-side, helping businesses align financial and ESG goals.
Improved observability & resilience: The Observability and Resiliency agents allow organisations to detect, diagnose and design for business-critical continuity, reducing downtime and risk.
Legacy-friendly migration: The Migration agent can autonomously discover legacy on-prem/.NET apps and propose IaaS/PaaS moves seamlessly.
Context-driven operations: By understanding user roles, service contexts and access rights, the orchestration engine ensures safe, compliant actions, preserving governance while boosting agility.
How Prolifics can unlock this potential for you
While Azure’s Agentic Cloud Ops capability is transformational, organisations still need the right partner to design the blueprint, tailor agents to unique business contexts, integrate workflows, and drive adoption. That’s where Prolifics steps in. We bring:
Deep expertise in cloud migrations, modernisation and operations
Proven practices for deploying intelligent agents into live infrastructure environments
A consultative approach that aligns agentic automation with your business objectives, regulatory posture and operational culture
A partnership-driven delivery model that empowers your team to harness the full potency of Azure’s new agentic platform
Ready to move from managing cloud ops to mastering them?
Reach out to Prolifics today and accelerate your move into the era of agent-driven cloud operations. Let’s help you turn the promise of Azure Copilot’s six-agent engine into measurable outcomes: faster deployments, lower costs, enhanced reliability and future-proofed infrastructure.
Contact Prolifics now → Let’s build your Agentic Cloud Ops strategy together.
The global Banking, Financial Services, and Insurance (BFSI) sector is entering a defining era of reinvention. After years spent modernizing legacy systems, scaling the cloud, and digitizing customer touchpoints, 2026 marks a pivotal shift from AI-Native Platforms in BFSI. Unlike traditional systems where AI is bolted on as a feature, AI-native platforms embed intelligence across the entire application stack, autonomously analyzing, predicting, optimizing, and acting in real time.
According to the IDC report titled, Unified AI & Agentic AI Platforms in Asia: Solution Insights for Technology Leaders, shows that, on average, only 23% of AI apps have gone from proof of concept (PoC) to production for Asia/Pacific. As AI capabilities evolve rapidly and more providers launch end-to-end platforms for AI-native application development, selecting the right AI platform requires a clear view of each vendor’s approach and how well the system aligns with current and future requirements.
This transformation goes far beyond efficiency gains. AI-native platforms are reshaping how banks build, run, and evolve applications; how insurers assess risk; how payment providers secure transactions; and how financial institutions deliver frictionless, hyper-personalized experiences. The BFSI organizations that embrace this shift will lead the industry. Those that do not risk irrelevance and will fall behind in how AI-native platforms are transforming banking and financial services.
The State of BFSI in 2026: Complex, Competitive, and Rapidly Evolving
The BFSI landscape is being reshaped by three powerful forces:
1. Rising Regulatory Demands
Compliance is no longer a static checklist. Frequent regulatory updates, complex audits, and the need for transparent AI decision-making require platforms that are explainable, adaptive, and traceable across every process reinforcing the role of AI-powered regulatory compliance in BFSI.
2. Customer Expectations at an All-Time High
Consumers now expect:
Real-time decisions
Personalized financial products
Autonomous servicing
Zero waiting times
High-trust digital interactions
AI-native systems meet these expectations through continuous learning and behaviour prediction, powered by autonomous decision intelligence in banking and richer human-machine collaboration in financial services.
3. Competition from Neo-Banks and FinTechs
Digital competitors operate with agility, built on cloud, microservices, APIs, and automation. Traditional banks need AI-native systems to achieve similar operational velocity and compete with growing trends in agentic AI in banking.
Together, these dynamics set the stage for intelligent, autonomous BFSI operations powered by AI-native platforms and autonomous decision intelligence in banking.
Why AI-Native Platforms Matter in BFSI
AI-native systems do not just support business operations, they reimagine them. By embedding machine intelligence across data pipelines, workflows, decisions, and digital experiences, AI-native platforms deliver transformation across the full enterprise spectrum — positioning financial institutions to answer key questions like what is the difference between AI-enabled and AI-native platforms in BFSI?
1. Hyper-Personalization at Scale
AI-native platforms analyze millions of customer signals,transactions, behavior, sentiment, location, interactions, with real-time processing. This enables:
Personalized product recommendations
Context-aware financial wellness insights
Intelligent credit decisioning
Autonomous conversational servicing
A global bank, for instance, is already using advanced NLP and semantic search to deliver highly contextual wealth management insights, reducing advisor research time and improving customer outcomes through improved human-machine collaboration in financial services.
2. Autonomous Back-Office Operations
The BFSI back office, once defined by manual processing, is now transforming through:
Intelligent document classification
Automated trade settlement
Machine-led loan underwriting
Smart claims adjudication
Predictive risk scoring
A leading financial services provider leveraged machine learning to boost mortgage underwriting productivity, reducing time spent reviewing documents while increasing accuracy an early example of autonomous decision intelligence in banking supported by AI-native application development practices.
3. Precision-Driven Fraud Detection
Fraud patterns evolve quickly, and only AI can keep up.
AI-native fraud systems use:
Multimodal data
Anomaly detection
Behavioral biometrics
Transactional analytics
A global bank achieved major gains by using ML to detect fraud faster, reducing false positives while minimizing investigation time showing how AI-native platforms are transforming banking and financial services through real-time insights.
4. Regulatory Compliance That Runs Itself
AI-native compliance systems continuously analyze regulatory changes, monitor operations, and flag risks. They ensure:
Automated KYC and AML
Real-time suspicious activity detection
Transparent audit trails
Automated documentation and reporting
For compliance auditors, AI can increase audit volumes from 4–5 daily to 20+ by augmenting human analysis using explainable AI governance for BFSI, forming a foundation for AI-powered regulatory compliance in BFSI.
From AI-Enabled to AI-Native: What’s Changing in BFSI Development?
Traditional BFSI development follows a human-led model: data → rules → decisions. AI-native shifts this to data → learning → autonomous optimization.
Key shifts include:
1. Autonomous Application Development
Agentic AI tools can generate code, test cases, APIs, data models, and integration scripts automatically. Developers become orchestrators, not coders supporting deeper AI-native application development practices.
2. Real-Time Human-Machine Interplay
AI-native platforms enable seamless switching between human and machine across BFSI operations:
Machine-driven for high-volume repetitive tasks
Machine-assisted for decision augmentation
Human-driven for complex, empathy-required interactions
This dynamic interplay strengthens human-machine collaboration in financial services and ensures the right balance of efficiency and judgment.
3. End-to-End AI Decision Pipelines
Every workflow payments, underwriting, onboarding, trade settlement runs on self-optimizing AI models reconfigured based on performance and context, showcasing how AI-native technology improves compliance, fraud detection, and automation in BFSI.
4. Intelligent Integration Across Systems
API-led connectivity allows AI systems to tap into:
Core banking
Payment rails
Policy administration systems
Risk engines
Cloud data lakes
This interconnected architecture allows AI to operate with full contextual awareness.
AI-Native Platforms in Action: Key BFSI Use Cases for 2026
Customer Servicing
Virtual agents resolve most queries autonomously.
Human agents get real-time insights for cross-sell and problem resolution.
Context-aware routing ensures premium or distressed customers engage with humans instantly.
Fraud Mitigation
Machine driven anomaly detection scores every transaction
Humans manage edge cases with rich AI insights
Trade Processing
Straight-through processing becomes default
AI predicts settlement failures, recommends actions
Recruitment and Workforce Management
AI shortlists candidates, evaluates video interviews, assesses sentiment
Human recruiters finalize high-complexity decisions
These use cases are already emerging, but by 2026, they will define BFSI operations.
Key Considerations for BFSI Firms on the AI-Native Journey
1. Adopt Enterprise-Wide AI Strategy
Isolated pilots do not drive transformation. BFSI leaders must define a unified AI vision across business lines, IT, compliance, and customer experience.
2. Ensure Trust and Explainability
Financial decisions require transparency. AI-native systems must:
Explain decisions
Provide audit-ready logs
Manage bias
Maintain regulatory alignment
3. Build Scalable Data Foundations
AI-native performance is only as strong as the data environment, data lakes, catalogs, lineage, governance, and real-time pipelines must be mature.
4. Manage Human-Machine Interplay
Organizations must define:
Where machines take the lead
Where human empathy is essential
When dynamic switching is required
5. Prioritize Governance and Change Management
Leadership buy-in, governance models, and a culture that embraces AI are critical to successful long-term adoption.
Conclusion: How Prolifics Accelerates BFSI Transformation with AI-Native Platforms
As BFSI companies prepare for 2026, the shift to AI-native platforms will be the single most important technology investment of the decade. But success requires more than tools; it requires strategy, engineering, governance, and industry expertise.
This is where Prolifics stands apart.
With deep BFSI experience, advanced AI capabilities, and proven accelerators, Prolifics helps organizations transition from legacy and cloud-first models to intelligent, AI-native ecosystems.
AI-driven modernization of legacy banking, insurance, and financial applications.
End-to-end implementation of agentic AI, GenAI, predictive analytics, and automation.
Domain-rich models tailored for payments, onboarding, fraud, underwriting, KYC/AML, and claims.
Enterprise-grade governance frameworks ensuring compliance, transparency, and security.
Accelerators like ADAM, Integration Frameworks, and DataOps kits enabling faster deployment and reduced cost.
Human-machine experience design that enhances customer engagement while maintaining regulatory trust.
Prolifics empowers BFSI organizations to reimagine every workflow, elevate customer experiences, and operate with unprecedented agility and intelligence.
In 2026 and beyond, AI-native BFSI platforms won’t just transform technology, they will transform the business itself. With Prolifics, the future of intelligent finance is already underway.
If you’re ready to begin your journey toward an AI-native future in banking, finance or insurance, we invite you to join us at the upcoming event:
Unlocking the Future of Banking & Finance with Agentic AI
📅 Date: December 4, 2025 📍 Location: IBM One Madison, 1 Madison Ave, New York, NY 10010 Hosted by Prolifics in collaboration with IBM Prolifics
At this exclusive event you will:
Discover real-world use-cases of agentic AI in banking and finance
See how full-stack AI-native platforms drive operational efficiency, compliance and scale
Connect with industry executives and experts shaping the future of BFSI
Reserve your seat today and join Prolifics in shaping the future of intelligent finance. Visit our Prolifics Events page for full details to register and take your first step toward transforming your organization with AI-native platforms.
Nonprofits need more than passion, they need technology that scales their mission. Exalt, a leading community-focused nonprofit, was ready to elevate donor engagement, streamline fundraising operations, and create a unified, data-driven development platform. Their legacy systems couldn’t keep up.
Prolifics partnered with Exalt to implement a modern, automated, and governance-led Salesforce Nonprofit Success Pack (NPSP) solution that transformed how the organization manages donors, campaigns, reporting, and long-term growth.
The Challenge
Exalt faced increasing complexity in managing donors, volunteers, campaigns, and reporting. Key issues included:
Disconnected spreadsheets and databases
Manual processes for acknowledgments, updates, and reporting
Inconsistent donor communications
Limited visibility into relationships and fundraising performance
Lack of standard data governance
Leadership sought a centralized CRM to:
✔ Consolidate donor, funder, and volunteer data ✔ Automate workflows and reduce manual tasks ✔ Improve reporting accuracy and transparency ✔ Establish governance and scalability for future expansion
Our Approach
Prolifics collaborated closely with Exalt to design a phased Salesforce NPSP implementation built on accuracy, governance, and user adoption.
Key steps included:
Stakeholder workshops to define needs and reporting expectations
Mapping existing workflows and identifying bottlenecks
MVP-first delivery for quick wins and early value
Iterative design reviews with Exalt’s teams
Sandbox testing and feedback-driven refinements
This ensured a solution that was both technically sound and aligned with real-world nonprofit operations.
Our Solution
Prolifics delivered a fully modernized Salesforce NPSP Development Platform featuring:
Unified Data Architecture
All donor, volunteer, and organizational records consolidated into Salesforce for real-time, single-source visibility.
Governance-Driven Design
Standard naming conventions, duplicate prevention logic, validation rules, and data ownership structure for long-term sustainability.
Automation & Efficiency
Automated gift acknowledgment workflows
Address and household synchronization
Standardized Opportunity management for donations, grants, and matching gifts
Campaign & Event Management
Tracking and reporting across campaigns, appeals, and events—centralized and transparent.
Relationship & Network Mapping
NPSP’s Relationship Viewer enabled Exalt to understand family ties, corporate links, and donor influence patterns.
Reporting & Analytics
Board-ready dashboards and metrics delivering accurate, real-time fundraising insights.
Training & Adoption Enablement
Customized training, a governance manual, and hands-on workshops ensured strong adoption and data hygiene across teams.
Key Outcomes
The implementation delivered measurable improvements across Exalt’s development operations:
Unified donor and fundraising data across teams
Automated donor acknowledgment workflows
Improved donor visibility through household and relationship tracking
Accurate board-level reporting with custom dashboards
Established governance and training for long-lasting system health
Ready to Transform Your Nonprofit Operations?
Partner with Prolifics to build scalable, future-ready digital solutions.
The financial services industry stands on the edge of a once-in-a-generation shift. For decades, organizations have relied on data-driven strategies to make smarter decisions, reduce risk, and improve customer experience. Today, those same organizations are rethinking what’s possible with an AI-first approach in finance, where artificial intelligence in financial services is no longer a supporting function but the foundation of strategy, innovation, and growth.
As market volatility rises and customer expectations evolve, AI-driven finance transformation has become the key differentiator. From intelligent automation and predictive insights to Generative AI in finance, this shift is reimagining every aspect of the financial ecosystem.
Defining the AI-First Approach in Finance
An AI-first approach in finance means embedding intelligence at the core of every financial decision, process, and interaction. It goes beyond adopting isolated AI tools; it’s about designing systems where AI and data analytics in finance drive continuous optimization, foresight, and responsiveness.
Traditional digital transformation focused on automation and efficiency. But an AI-first finance model creates systems that learn, reason, and improve autonomously. This evolution allows financial institutions to transition from being data-informed to becoming AI-native enterprises, where decision-making is predictive, not reactive.
AI Innovation in Banking and Finance
The future of finance with AI-first organizations is defined by agility, personalization, and intelligent automation. Across the financial sector, we’re witnessing AI innovation in banking and finance reshape how institutions serve their customers and manage their operations.
1. AI in Risk Management and Compliance
Risk management is no longer confined to manual checks or retrospective audits. With AI in risk management and compliance, institutions can now identify anomalies in real time, detect fraud before it happens, and ensure ongoing adherence to complex regulatory frameworks. AI-enabled fraud detection models analyze millions of transactions per second, reducing exposure while improving trust and transparency.
2. Predictive Analytics for Banking
Predictive models are becoming central to financial forecasting and market analysis. Through predictive analytics for banking, financial leaders gain deeper insights into credit scoring, liquidity management, and investment trends. The ability to anticipate change, not react to it, has become a core capability for leading AI transformation in the finance industry.
3. AI Automation in Banking
From intelligent document processing to customer service bots powered by Generative AI, AI automation in banking is transforming how institutions operate. Manual reconciliation, loan approvals, and onboarding processes are being reimagined through self-learning systems that deliver faster, error-free results, all while reducing operational costs.
The Technology Backbone of AI-Driven Finance Transformation
An AI-driven finance transformation relies on robust data architectures, seamless integration, and cloud scalability. The foundation begins with unified data platforms that enable AI and data analytics in finance to work together across front, middle, and back-office functions.
Modern banks and financial institutions are leveraging hybrid AI architectures that blend deterministic models with Generative AI in finance to create explainable, ethical, and scalable solutions. AI-first organizations prioritize data governance, transparency, and responsible AI, ensuring compliance while accelerating innovation.
Benefits of Adopting AI in Finance Operations
Organizations that embrace AI-first strategies are realizing significant benefits in efficiency, profitability, and resilience.
In short, the benefits of adopting AI in finance operations extend beyond cost savings they redefine how finance teams deliver value across the enterprise.
Reimagining Finance with an AI-First Approach
The truth is that AI-first finance is already reimagining established practices. Whether it’s algorithmic trading, AI-powered portfolio management, or real-time fraud prevention, innovation is no longer theoretical; it’s operational.
Generative AI is driving a profound transformation in financial services, enabling CFOs and analysts to simulate economic scenarios, generate forecasts, and produce insights in seconds. The organizations that treat AI as a strategic asset rather than a tool are positioning themselves for long-term advantage.
The Future of Finance with AI-First Organizations
As we look ahead, the future of finance with AI-first organizations will be defined by adaptive intelligence and continuous innovation. Finance leaders will rely on AI not just to optimize processes but to shape new business models and products.
From autonomous finance systems that self-correct to GenAI copilots supporting strategic decision-making, the next evolution of AI transformation in the finance industry is already underway.
Financial institutions that act now to integrate AI across their data, governance, and decision-making frameworks will lead this new era of resilience and growth.
Conclusion: The Path to an AI-Ready Financial Future
The journey to reimagining finance with an AI-first approach is not just about adopting new technology; it’s about transforming how financial organizations think, decide, and operate. At Prolifics, we help enterprises move from exploration to execution, combining AI innovation, governance, and integration frameworks that make finance smarter, faster, and more strategic.
Our approach bridges trusted data, intelligent automation, and human expertise, empowering financial institutions to predict outcomes, optimize decisions, and create truly adaptive business models.
In this new era, finance is no longer reactive; it’s predictive, autonomous, and visionary. The organizations ready to lead this transformation are those building toward an AI-ready future today.
A global leader in sustainable packaging, operating one of the world’s most complex logistics and supply chain networks, faced a critical challenge in its truck load planning process. The process was highly manual, dependent on expert planners, and lacked real-time agility, leading to underutilized trucks, higher operational costs, and slower order fulfilment.
To transform this, the organization partnered with Prolifics, IBM, and TrustNet Technologies to reinvent its planning workflow with an AI-powered automation strategy. Using IBM watsonx Orchestrate combined with Prolifics’ Agentic AI Framework, the team built an intelligent truck load optimization prototype that unified automation, advanced analytics, and human oversight.
The modernized solution introduced intelligent load planning, SAP-integrated data ingestion, geolocation-based routing, Human-in-the-Loop validation, and automated communication workflows, all supported by intuitive BI dashboards. The impact: faster planning cycles, improved truck utilization, greater operational visibility, and significantly reduced manual effort.
Continuous Improvement leaders across the organization praised the prototype for its accuracy, usability, and scalability, laying the foundation for enterprise-wide rollout.
At Prolifics, we turn Vision to Value, Faster, delivering AI solutions that combine precision engineering, scalable automation, and measurable business outcomes.
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