ORLANDO, Florida., September 19, 2025 – We’re entering a new golden age in frontend engineering, one where your development toolchain is no longer weighed down by configuration fatigue or sluggish runtimes. The days of juggling ESLint + Prettier, drowning in config hell, and enduring slow linting are fading fast.
At Prolifics, we’re embracing the revolution. AI-driven and Rust-powered solutions are stepping in to streamline your workflow with astonishing speed and precision, fueling a future where development becomes more agile, consistent, and most importantly, developer-first.
What’s Broken with the Old Stack?
Snail-pace performance: Traditional tools struggle with large codebases, linting thousands of files can drag on for minutes, even with caching.
Endless config maintenance: We’ve all fallen into “config hell”, countless files, conflicting rules, dependency trees that spiral out of control.
Format vs lint collision: ESLint and Prettier often clash, forcing teams into endless debates about semicolon placements or line lengths.
Enter Biome: One Tool to Rule Them All
Biome, a high-performance toolchain written in Rust, eliminates these pain points by unifying linting and formatting under one roof. Here’s why the buzz is real:
Rust-fast: Developers report linting and formatting massive repos in under a second, compared to tens of seconds with traditional JavaScript tools.
Unified config: One biome.json replaces multiple ESLint, Prettier, and plugin configs, simplifying setup across the board.
Seamless migration: Use biome migrate eslint and biome migrate prettier to auto-generate equivalent configuration in seconds.
95–97% compatibility: Biome supports the majority of Prettier rules and common ESLint configurations, earning it accolades and even monetary prizes from the ecosystem.
VCS-aware intelligence: Only lint changed files by default, no need for separate tools like lint-staged, improving CI/CD speed and developer feedback loops.
Real-World Results That Speak Volumes
AckerWorx slashed linting time: from ~35 seconds down to just ~5 seconds over ~2,500 files—delivering near-instant feedback and smoother developer flow.
BloxBoom saw full-repo linting/formatting finish in under a second, anyone who’s waited minutes for tools to finish knows how game-changing that is.
But… What About Type-Aware Linting?
Yes, a caveat: ESLint with TypeScript (via typescript-eslint) offers type-checked lint rules, which Biome doesn’t fully replicate out of the box. However, teams report this is often an acceptable tradeoff given the massive gains in speed and simplicity. And for those rules that matter most, you can run both tools—using Biome for instant feedback and ESLint for deep type-aware checks.
Prolifics Is Ready, Are You?
At Prolifics, we don’t just follow trends, we spearhead them. We help forward-thinking teams adopt tools like Biome, ensuring:
Lightning-fast developer cycles
Fewer dependencies, fewer headaches
Cleaner, more maintainable pipelines
Seamless integration into CI/CD, editors, and monorepos
Ready to supercharge your frontend workflow?
Reach out to Prolifics today, and let’s help you transition from legacy toolchains to the AI-powered, Rust-driven future of development.
Artificial Intelligence (AI) isn’t just reshaping the future, it’s redefining banking today. From fraud detection to personal finance guidance, AI-driven shifts in banking are rapidly becoming the backbone of smarter, faster, and more secure banking operations. But what about 2026? How will banks step beyond chatbots and credit scoring to unlock the next frontier of customer experiences, efficiency, and profitability?
Gartner’s 2025 fintech trends highlight transformative technologies and strategic priorities shaping the financial services industry. Below are the critical trends, synthesized from Gartner’s research:
AI and Machine Learning (ML) in Finance
Generative AI (GenAI) and ML: Over 50% of finance leaders plan significant GenAI investments, driven by demand for real-time insights and automation. AI is reshaping AI-powered fraud detection, customer service, and decision-making processes.
AI-Driven ERP Systems: Cloud-based ERP systems with AI capabilities are prioritized for core finance operations, enabling real-time analytics and scalability. These innovations are core to AI-driven shifts in banking.
What is AI in Banking?
AI in banking refers to the application of machine learning (ML), natural language processing (NLP), predictive analytics, and automation technologies to streamline operations, enhance decision-making, and create personalized customer experiences. Unlike traditional digital systems, AI learns from massive datasets, customer transactions, market patterns, and compliance reports, and applies intelligence to predict risks, detect fraud, and improve service delivery.
In 2026, the future of AI in banking will move beyond being a “supporting tool” to becoming the central nervous system of modern banking, driving real-time, autonomous decisions that shape every customer interaction.
Revenue Growth – Personalized upselling and algorithmic trading unlock new income streams.
Compliance & Security – AI safeguards banks with automated reporting and real-time fraud detection.
7 AI Use Cases in Banking for 2026
Here are seven ways banks will deploy AI in 2026, and why you didn’t see some of them coming.
1. AI Chatbots for Tier-1 Support
Chatbots are nothing new, but in 2026, they’ll evolve into digital banking concierges. Powered by conversational AI and generative AI in finance, these bots won’t just answer FAQs. They’ll:
Execute transactions like bill payments or fund transfers.
Detect customer mood and adjust tone accordingly.
Act as first-line financial advisors for common scenarios.
For banks, this means slashing call center costs while delivering seamless, human-like interactions.
2. Credit Risk Analysis via ML Models
Banks have always measured credit risk, but AI makes it real-time and hyper-accurate. In 2026, advanced ML models will:
Incorporate alternative data sources (utility bills, e-commerce patterns, even social signals).
Identify hidden risks in seconds.
Enable dynamic credit scoring for instant loan approvals.
This reduces defaults and expands lending opportunities to underserved customers, a win-win for banks and borrowers.
3. KYC Document Automation
Know Your Customer (KYC) is often a bottleneck. Manual checks slow down onboarding and frustrate customers. AI in banking in 2026 will:
Automate document verification with computer vision.
Use NLP to cross-check data against regulatory sources.
Spot inconsistencies faster than humans.
Result: banks cut onboarding times from days to minutes, improving compliance while delighting customers.
4. Algorithmic Trading & Robo-Advisory
The stock market is no stranger to AI, but in 2026, algorithmic trading and robo-advisors will become mainstream banking services. AI-driven shifts in banking will enable systems to:
Process market news and sentiment in real time.
Execute trades automatically at optimal moments.
Provide customers with robo-advisors offering personalized, low-cost investment strategies.
5. Personalized Financial Planning
In 2026, AI will serve as your personal CFO, offering guidance based on:
Spending habits, transaction history, and lifestyle goals.
Predictive insights into future needs (mortgages, retirement, college funds).
Tailored investment suggestions aligned with individual risk profiles.
This type of hyper-personalization keeps customers loyal and turns banks into trusted partners, not just service providers.
6. Transaction Categorization & Budgeting
In 2026, AI won’t just categorize transactions (“groceries” or “rent”), it will provide actionable budgeting intelligence:
Flag overspending in specific categories.
Suggest ways to optimize savings.
Gamify budgeting with AI-powered fraud detection challenges.
The result: healthier financial habits for customers and higher engagement rates for banks.
7. Predictive Maintenance for ATMs
AI will monitor ATM networks in real time, predicting failures before they occur. With IoT sensors feeding data into predictive ML models, banks will:
Schedule proactive maintenance.
Reduce downtime.
Ensure consistent cash availability.
This strengthens customer trust, because no one likes the dreaded “out of service” message.
Challenges of AI in Banking
While the future is promising, AI adoption comes with hurdles:
Data Privacy Concerns – Handling sensitive financial data requires airtight governance.
Bias in Algorithms – AI must be transparent and fair to avoid discriminatory practices.
Integration Issues – Legacy systems often clash with modern AI models.
Regulatory Pressure – Compliance frameworks around AI are still evolving.
Banks that tackle these challenges with robust governance frameworks will be the ones to lead in 2026.
The Future of AI in Banking
Beyond the seven core use cases, the future of AI in banking will stretch into frontier innovations:
Generative AI for Customer Onboarding
Instead of filling forms, customers will interact with GenAI agents that pre-populate data, explain policies, and guide them through onboarding in plain language.
Explainable AI (XAI)
Regulators demand transparency. Banks will use XAI to show customers how decisions are made, from loan approvals to fraud alerts, building trust.
AI-Driven ESG Risk Scoring
Environmental, Social, and Governance (ESG) metrics will influence credit and investment decisions. AI will assess ESG risks in real time, guiding sustainable banking.
Autonomous Finance
Imagine banking that runs itself, AI automatically reallocates your portfolio, pays your bills, and adjusts budgets with minimal input. By 2026, autonomous finance will move from concept to reality.
Real-Time Fraud Defense
AI won’t just detect fraud, it will prevent it instantly, blocking suspicious transactions before they are processed. Banks will leverage deep learning models to stay a step ahead of cybercriminals.
Transform Your Banking Operation with AI, Powered by Prolifics
The AI revolution in banking isn’t optional, it’s inevitable. The question is: will your institution lead the change or struggle to catch up?
At Prolifics, we help banks accelerate digital transformation with end-to-end AI solutions:
Automated KYC & Compliance – Simplify onboarding and reporting.
Hyper-Personalized Customer Journeys – Increase loyalty and lifetime value.
GenAI & Predictive Analytics – Drive new growth opportunities.
With decades of experience in financial services and proprietary accelerators like ADAM, Prolifics enables banks to unlock the full potential of AI while navigating complexity, compliance, and cost.
AssetVue, a leading IT asset management provider, needed more than just a vendor relationship. After two unsuccessful attempts with other firms, they turned to Prolifics to create a hybrid Microsoft .NET “software factory.” The mission was simple but critical: accelerate product releases, reduce delivery risk, and design a development model that could flex and scale with business demand.
Prolifics delivered by blending onshore and offshore expertise into one seamless team. Acting as AssetVue’s dedicated engineering arm, Prolifics introduced agile practices, governed sprint cadences, and embedded program management and business analysis to align technology with business priorities. This approach enabled faster, high-quality product development and paved the way for significant milestones, including the successful launch of AssetVue Version 3.0, a major upgrade in functionality and usability.
Eight years later, the partnership continues to thrive. Prolifics is more than a service provider, it is a trusted extension of AssetVue’s team. With continuous innovation, reliable release cycles, and scalable delivery, AssetVue remains competitive in the IT asset management market while confidently planning for long-term growth.
Westbury, New York, September 2, 2025 – Salesforce’s MuleSoft has launched a suite of AI-focused advancements designed to help enterprises enter the “agentic” age, a future defined by AI agents operating with autonomy, coordination, and governance.
At the heart of this innovation are two groundbreaking frameworks:
Model Context Protocol (MCP): An open-source standard that enables seamless, governed, bidirectional communication between AI agents and enterprise systems. With MuleSoft MCP Support, organizations can transform any existing application or API into an “agent-ready” asset in just a few clicks. Features such as the MuleSoft MCP Connector, Flex Gateway for MCP, and API integration through Agentforce Topic Center bring robust control and interoperability to AI operations. This advancement represents comprehensive MuleSoft Model Context Protocol (MCP) Support, ensuring enterprises can future-proof integrations with governed, secure AI adoption.
Agent2Agent (A2A): A protocol for secure, scalable communication between AI agents themselves. MuleSoft’s A2A Support makes multi-agent workflows discoverable, coordinated, and governed. This includes the A2A Connector and Flex Gateway for A2A, ensuring agents can collaborate dynamically to execute complex tasks. With MuleSoft’s new support for MCP and A2A protocols, enterprises gain the flexibility to unify agent-to-system and agent-to-agent communication without compromising governance.
Why It Matters
As organizations deploy more AI agents and the number of applications and models balloons, technology infrastructures often become tangled and siloed. A staggering 95% of IT leaders rank integration as the primary obstacle to realizing AI’s full potential.
MuleSoft’s new agent orchestration capabilities directly tackle these challenges by providing:
Operational Autonomy – AI agents can execute tasks, access systems, and interact with one another securely and intelligibly.
Governance and Visibility – Businesses retain control over AI behavior, auditability, and compliance across workflows.
Efficiency and Scale – Teams can move beyond reactive automation and toward proactive, trusted AI-driven operations.
Real-World Impact: A Workflow in Action
Consider a real-world example: an observability agent detects a critical server failure in a platform like Datadog (agent-to-system). It alerts a database triage agent (agent-to-agent), which then consults a knowledge base for the solution. Once identified, the remediation agent reboots the relevant cloud service and provides an update via Slack (agent-to-agent to agent-to-system).
This workflow allows IT teams to offload routine issue handling, freeing them to focus on strategic innovation and customer experience enhancements.
Developer Productivity Gets a Boost
MuleSoft is not just focusing on agent action, it’s improving how developers build these integrations. The Anypoint Code Builder IDE now includes generative AI features that:
Ramp up integration development with natural language prompts.
Automatically generate API specs aligned with standards.
Offer DataWeave transformations, powered by AI.
Auto-generate API documentation upon publication via Anypoint Exchange.
These capabilities accelerate project timelines and make it easier to scale AI infrastructure.
Why Prolifics Is Your Ideal Partner for Agentic Transformation
Ready to translate innovation into impact? Prolifics offers the expertise, industry experience, and deep integration skills to help your organization harness the full potential of MuleSoft’s AI agent orchestration:
Strategic Planning & Assessment: We analyze your IT landscape to uncover high-impact use cases for AI agents, aligned with your operational goals and risk profile.
Architecture & Implementation: Whether you require MuleSoft Model Context Protocol (MCP) Support), A2A coordination, or generative AI tooling, Prolifics delivers turnkey design and deployment services.
Governance & Compliance: Secure agent orchestration is core to everything we do—from ensuring access control and audit trails to enforcing enterprise-grade governance across every interaction.
Acceleration Workshops: Through immersive hands-on sessions, we empower your teams to build, govern, and optimize real-world AI agent workflows quickly and confidently.
Contact Prolifics today to explore how MuleSoft’s new support for MCP and A2A protocols can transform your operations, and how we can be your trusted partner throughout that journey.
The promise of industrial automation has always been to improve safety, quality, and productivity. Yet many manufacturers still face labor shortages, skill gaps, and safety concerns that automation alone hasn’t solved. That’s where humanoid factory workers come in. Unlike traditional industrial robotics, rigid, fixed machines designed for repetitive single tasks, humanoid robots are built to move, act, and even “think” like people.
According to Fortune Business Insights, the global Humanoid Robots in Manufacturing and robotics market reached $43.8 billion in 2023 and will nearly double by 2032. Even more striking, Grand View Research reports the humanoid robot market could grow at a staggering 63.5% CAGR through 2030. These figures underline a simple truth: humanoids are not science fiction. They are the next chapter of smart manufacturing.
Rather than replacing human workers, human-robot collaboration (HRC) is the goal. AI humanoid robots in manufacturing are designed to complement people by taking on repetitive or dangerous tasks while employees focus on higher-value, creative work. Gartner and Forrester both highlight how this balance is key to future productivity and resilience in manufacturing.
Where Humanoids Fit in Industry 4.0 and Beyond
Smart factory technology is powered by interconnected systems: IoT sensors, MES/ERP platforms, RPA on the business side, and AI analytics that guide decision-making. Until now, automation has struggled with tasks designed for humans, climbing stairs, handling delicate parts, or working in tight spaces.
Humanoid Robots at Work extend Adaptive Manufacturing into these areas. They can connect to existing factory systems, report production data in real time, identify bottlenecks, and even suggest improvements. This allows businesses to bridge the last mile between traditional robotic automation and human-designed workspaces. For companies exploring how to implement robots in manufacturing, humanoids represent the missing link between automation and human efficiency.
Technology Building Blocks Driving Transformation
AI & Machine Learning
Generative AI in industrial robotics enables robots to learn new tasks quickly, adapt to changing environments, and improve efficiency. Research shows examples of humanoid robots in manufacturing demonstrate that they can now learn new assembly tasks with 85% fewer demonstrations than older models.
Sensing & Vision
Equipped with 3D vision and force feedback, humanoids improve quality inspections, handle delicate parts, and operate safely in human-robot collaboration (HRC) environments.
Mobility & Dexterity
Modern designs allow robots to walk on uneven surfaces, climb ladders, use human tools, and complete complex tasks once thought impossible for machines. These advancements highlight the benefits of humanoid factory workers—not only improving safety but also handling tasks beyond traditional automation.
Business Value & ROI: Day 1 vs. Day 365
How humanoid robots improve factory efficiency becomes clear from day one.
Day 1: Reduced worker exposure to hazardous tasks, streamlined uptime, and faster repetitive processes.
Day 365: Continuous operation, 30–50% productivity gains, fewer defects, and shorter ROI cycles.
According to McKinsey, ROI periods for humanoid deployment have dropped from 5.3 years in 2019 to just 2.8 years in 2023. Oxford Economics adds that companies using AI-powered humanoid robots report labor cost reductions of 22–28% within the first year.
Sample KPI Tracking Framework
KPI
Baseline
After 1 Year
OEE (Overall Equipment Effectiveness)
65%
80%+
FPY (First Pass Yield)
90%
95%+
Scrap Rate
5%
2%
Injury Rate
Industry average
35–50% reduction
Payback Period
3–5 years
18–24 months
Safety & Compliance Enhancements
Humanoid Robots in Manufacturing directly improve workplace safety by handling heavy lifting, repetitive motions, and hazardous material exposure. OSHA data shows that factories deploying humanoid robotics cut exposure-related incidents by more than 50%. The U.S. Bureau of Labor Statistics also found that automation-heavy plants saw injury rates drop by 35%.
For manufacturers, this not only protects workers but also supports compliance with stringent safety and labor regulations. It also showcases the impact of AI on manufacturing workforce safety and sustainability.
Implementation Roadmap
Assessment & Use-Case Prioritization – Identify tasks suited for humanoids: repetitive, hazardous, or precision-based.
Pilot Design – Establish KPIs and capture performance data.
Systems Integration – Connect robots with MES/ERP/PLC systems, ensure cybersecurity and data management.
Change Management & Training – Upskill workers, define roles in human-robot collaboration (HRC).
Scale-Up Strategy – Move from cell pilots to line and plant-wide deployments.
For companies considering how to implement robots in manufacturing, this roadmap provides a practical framework.
Potential Risks and Solutions
Upfront Costs: While prices have come down to the $100k–$150k range per unit, many small and mid-sized businesses still see this as a significant investment. To reduce financial barriers, options such as leasing models, subscription-based Robot-as-a-Service (RaaS), and phased deployment can spread out costs and make adoption more feasible.
Integration with Legacy Systems: Older IT and operational systems can create roadblocks when connecting new automation technologies. Leveraging middleware platforms, APIs, and standardized reference architectures helps bridge compatibility gaps, ensuring smooth connectivity without costly overhauls.
Programming Complexity: Traditional robot programming requires specialized expertise, which can limit scalability. Emerging solutions like low-code or no-code task teaching, paired with Generative AI in industrial robotics, reduce reliance on scarce technical talent and speed up deployment.
Workforce Acceptance: Employees may resist robotics adoption due to concerns about job security or role changes. Organizations can address this by clearly communicating the purpose of automation, emphasizing how it enhances rather than replaces human work, investing in upskilling opportunities, and redefining roles to highlight collaboration between people and machines.
These reflect some of the challenges of deploying humanoid robots in real-world environments.
How Prolifics Can Help
At Prolifics, we partner with manufacturers to navigate every stage of their AI manufacturing automation journey, ensuring technology investments translate into measurable business value. Our approach spans strategy, integration, innovation, and workforce readiness:
Strategic Assessments: We begin with in-depth evaluations of operations, supply chains, and production environments to identify the areas where humanoid robotics and warehouse automation robotics deliver the highest ROI. This ensures automation efforts are targeted, impactful, and aligned to business priorities.
Systems Integration: Manufacturing success depends on seamless connectivity. We integrate robotics solutions across MES (Manufacturing Execution Systems), ERP, IoT, and cloud platforms, building a unified ecosystem that eliminates silos and enables real-time visibility and control across operations.
AI and Data Solutions: Beyond robotics, our AI capabilities amplify manufacturing efficiency. We deploy advanced analytics for demand forecasting, apply Generative AI in industrial robotics to speed programming and reconfiguration, and use digital twins to simulate and predict asset performance, helping reduce downtime and improve throughput.
Change Management Programs: Technology is only part of the journey. We design programs that prepare workers for the future of manufacturing jobs with AI, focusing on clear communication, upskilling, and redefining roles to encourage collaboration between humans and intelligent machines.
By blending deep industry expertise with proven technology accelerators, Prolifics enables manufacturers to accelerate adoption, reduce costs, and achieve sustainable ROI from humanoid robotics and automation, while building a foundation for long-term competitiveness and innovation.
Key Takeaways: Embracing the Humanoid Future
The rise of humanoid robots in manufacturing 2026 and beyond is reshaping industries into safer, smarter, and more productive environments. By embracing AI-powered humanoid robots as partners in the smart factory landscape, businesses can achieve resilience, efficiency, and innovation that outpace competitors.
As leaders consider the Future of manufacturing jobs, they must also explore Industry 4.0 vs Industry 5.0 robotic strategies to ensure long-term adaptability. The future isn’t about humans or robots; it’s about both working side by side.
In the age of digital transformation, enterprises are realizing that data is no longer just an asset, it’s the foundation of innovation, resilience, and sustainability. According to IDC, over 80% of CEOs view data as a core business differentiator, yet legacy systems, siloed data, and outdated infrastructures continue to hinder progress. This is where cloud-driven data modernization becomes a game-changer.
Data modernization services are the process of updating and transforming an organization’s data systems, infrastructure, and practices into modern, cloud-based formats. It enhances accessibility, security, and usability while unlocking greater business value. Modernization typically involves migrating data from legacy systems to modern data platforms, integrating advanced technologies, and implementing strategies that improve data-driven decision-making, business intelligence, and overall outcomes.
Enterprises that embrace this shift are not just improving operations, they’re reimagining entire business models and preparing for enterprise transformation with data modernization.
Cloud Services in Data Modernization
The cloud has evolved into the backbone of digital transformation. From storage and scalability to real-time analytics and AI integration, cloud services are central to cloud-based data modernization. Services such as data lakes, cloud-native warehouses, serverless computing, and AI-driven platforms give enterprises the agility to harness insights quickly and securely.
By shifting to cloud data modernization solutions, organizations can:
Eliminate data silos and unify insights across departments.
Enable hybrid and multi-cloud models for flexibility.
Reduce infrastructure costs while scaling innovation.
Ensure disaster recovery and data security with built-in compliance.
Understanding Enterprise Data Modernization: Why Do Enterprises Need It?
Enterprises need modernization for three critical reasons:
Agility and Competitiveness – Businesses must adapt to dynamic markets and consumer demands faster than ever. Legacy systems cannot deliver real-time intelligence.
Innovation Enablement – Cloud-based data modernization allows for AI, machine learning, and advanced analytics integration.
Cost Efficiency and Sustainability – Modern data platforms optimize storage, reduce redundancies, and enable eco-friendly infrastructure utilization.
In essence, modernization transforms data from a static archive into a dynamic utility, empowering smarter, faster decision-making while fueling business transformation with cloud.
The future of data modernization lies in accelerating intelligence and resilience, with cloud as the foundation.
Future Trends in Cloud Modernization Strategy
As enterprises look ahead, key trends include:
AI-first Modernization: Cloud platforms embedding AI for predictive analytics.
Serverless and Edge Integration: Processing data closer to the source.
Data Democratization: Making insights accessible across the enterprise.
Green Cloud: Providers focusing on energy efficiency and carbon-neutral operations.
Quantum-Ready Cloud: Preparing enterprises for next-gen computing.
These trends highlight the future of data modernization, making cloud-first strategies a necessity, not an option, for enterprise transformation with data modernization.
Industries Embracing Business Transformation with Cloud
Healthcare – Real-time patient analytics, predictive diagnostics, and secure compliance.
Finance – Fraud detection, customer insights, and automated compliance.
Retail – Personalized customer experiences powered by modern data platforms.
Utilities & Energy – Smart grids, demand forecasting, sustainable energy optimization.
Industries across the spectrum are discovering that cloud-driven data modernization enhances business intelligence, resilience, and sustainability.
Integration of Cloud Technology and Business Intelligence
The integration of cloud platforms with BI tools creates a powerful synergy. Modern BI tools leverage cloud-based data modernization to:
Enable real-time dashboards and reporting.
Improve decision-making accuracy.
Support predictive and prescriptive analytics.
Scale insights across global operations.
This integration transforms organizations into insight-driven enterprises where decision-making is proactive rather than reactive.
Accelerating Your Data Modernization Journey
At Prolifics, we recognize that modernization is not just about technology, it’s about building sustainable, future-ready enterprises.
Here’s how we accelerate your journey:
Tailored cloud modernization strategy and adoption roadmaps.
Seamless migrations with data modernization services that ensure security and scale.
Hybrid and multi-cloud expertise aligned to compliance and business needs.
AI and analytics integration for predictive intelligence.
Governance by design for compliance and trust.
Focus on sustainability and eco-friendly cloud usage.
With deep partnerships across providers and decades of expertise, we ensure business transformation with cloud isn’t just an IT upgrade but a reinvention of how your enterprise thinks, operates, and competes.
Conclusion
The future of data modernization is here, and it’s shaping the next era of digital transformation. With cloud as the backbone, enterprises can eliminate inefficiencies, embrace AI, and achieve enterprise data modernization that drives agility, sustainability, and innovation.
The time to modernize is now. Don’t let legacy systems hold back progress. Partner with experts to accelerate your journey, adopt cloud data modernization solutions, and secure enterprise transformation with data modernization that will future-proof your business.
Ready to modernize? Contact Prolifics today and turn your data into your most powerful asset.
Modernize how your teams learn. We design and deliver learning, custom courses, microlearning, simulations, video, and gamified experiences that plug into your LMS for consistency and measurable outcomes. The result: faster upskilling, lower costs, continuous learning.
Tackle today’s training gaps: low engagement, compliance risk, rising spend, weak ROI, and skill shortages. Our approach blends user-centric design, microlearning, blended delivery, and gamification, with AI-driven personalization and analytics. Learners get relevant paths; leaders get proof of impact.
What we deliver: • Custom eLearning and content modernization • Rapid and mobile-first development • Scenario-based simulations • Translation, localization, and knowledge support • AI analytics to optimize journeys
Why Prolifics: • 91% repeat engagement • 97% defect-removal efficiency • 40+ years, 20+ industry awards • Trusted by 150+ Fortune 1000 clients
Proven outcomes across IT, finance, healthcare, and life sciences: higher completion and productivity, reduced audit penalties, accelerated digital transformation, and stronger training ROI. From pilot to enterprise rollout, our structured delivery ensures seamless implementation, scalable adoption, and consistent learner experience.
Partner with Prolifics to turn learning into performance. Let’s build content that advances skills, protects compliance, and fuels growth. Start your eLearning journey today.
Grab your cup of coffee, tea or your beverage of choice and join your JAM/Panther team for our Coffee Break event! Have a front row seat to a demonstration of our latest Panther release 5.60.
When: Wednesday, Oct 29th, 10:00 am – 10:30 am EDT
Enterprises relying on IBM i modernization (formerly AS/400 modernization) systems know it’s a powerhouse for mission-critical operations—trusted for reliability, performance, and cost efficiency. Yet modernizing legacy RPG-based applications remains a major hurdle: technical debt, scarce RPG expertise, and the risk of disrupting critical workflows transform a high-stakes challenge. To bridge this gap, IBM is introducing IBM watsonx Code Assistant for i, a generative AI powered coding assistant purpose-built to accelerate modernization with context-aware assistance directly in the developer’s IDE. IBM watsonx Code Assistant for i is at the center of a new wave of IBM i modernization, helping enterprises tackle RPG code challenges with AI-powered efficiency.
Purpose-Built for RPG & IBM i Workloads
Built on IBM’s flagship Granite code model, fine-tuned specifically for RPG and IBM i modernization applications, the assistant empowers developers—novices and veterans alike—with features to explain, document, and eventually generate or transform RPG code. Starting with context-aware code explanations, future updates will enable unit test generation, code transformations, and more, all integrated into Visual Studio Code environments.
This contextual intelligence addresses a glaring enterprise pain point: shrinking pools of RPG-trained developers. The RPG Code Assistant acts as a virtual mentor, helping newcomers understand complex codebases and veterans to accelerate enhancements—reducing ramp-up time and preserving institutional knowledge.
From Private Preview to Public Launch
Initially unveiled in early 2025 at IBM Think and later showcased at COMMON POWERUp, watsonx for IBM i is currently in private preview, with a public beta rolling out to early adopters soon. The first release will focus on code explanation features, with generation and test support arriving later in the year.
Clients will have cloud, on-prem, or hybrid deployment flexibility, aligning with governance models and data requirements. With AS/400 modernization becoming a pressing need, this flexibility ensures enterprises modernize at their own pace while maintaining compliance.
Why It Matters—Now
Accelerated Modernization – Developers can quickly comprehend and document legacy code, speeding up modernization projects and reducing delays.
Minimized Risk – Built-in intelligence and context-aware guidance help mitigate common errors and reduce disruption to critical business applications.
Talent Enablement – Bridging skill gaps ensures continuity even as seasoned RPG developers retire.
Future-Ready – With IBM AI coding tool transformation and test generation on the roadmap, enterprises can look forward to tangible productivity gains.
Prolifics: Your Trusted AI Modernization Partner
At Prolifics, we specialize in blending emerging AI capabilities into your core legacy modernization journey, safely and effectively. Here’s how we can help you activate IBM watsonx Code Assistant for i:
Assessment & Strategy — We evaluate your current IBM i modernization AI architecture and pinpoint which applications would most benefit from RPG modernization.
Pilot & Deployment – From private beta to production readiness, we provide hands-on deployment, integration into your existing IDEs and CI/CD pipelines, and enablement for your teams.
Governance & Best Practices — Ensuring IP safety, code quality, and compliance remains core to our approach.
ROI Realization – With context-aware code explanation and upcoming transformation features, you can expect faster project cycles, fewer errors, and stronger developer onboarding.
Take the Next Step Today
Ready to transform your IBM i modernization landscape with AI? Join the waitlist for IBM watsonx Code Assistant for i, and partner with Prolifics to unlock next-gen developer productivity and modernization speed safely and strategically.
In today’s fast-evolving AI landscape, delivering systems that are safe, accurate, and aligned with your brand values is no longer optional, it’s mission-critical. Yet traditional approaches to AI oversight, manual review, static classifiers, and rigid monitoring workflows, are often inefficient, costly, and opaque. Enter Databricks’ breakthrough innovation: the Prompt-Guided Reward Model (PGRM), a flexible, scalable, and interpretable solution that reimagines how organizations evaluate and govern AI behavior. With Databricks PGRM, businesses now have a tool that combines scalability with adaptability, setting a new standard in oversight.
The Challenge: Balancing Flexibility, Scale, and Transparency in AI Oversight
Think about this: Leveraging a Large Language Model (LLM) as a “judge” lets you adapt evaluation rubrics on the fly, but LLMs are slow, expensive, and notoriously poor at estimating their own confidence. On the other hand, reward models (RMs) offer fast, scalable, and calibrated scoring, but are rigid, inflexible, and require retraining to adjust criteria.
That’s a major operational dilemma:
Need adaptability? LLM judges give you that—but at a steep cost.
Need efficiency and confidence? Reward models deliver, but only when the requirements are static.
This is where Databricks PGRM shines, it brings together the flexibility of LLM judges with the efficiency and calibration of reward models. This innovation reflects a broader shift toward AI-Powered Data Governance, where oversight adapts in real time without sacrificing accuracy.
PGRM: The Hybrid Champion for AI Quality Control
PGRM is a revolutionary new approach that unlocks three game-changing capabilities:
Instructability at Scale Just like an LLM judge, PGRM can follow arbitrary natural language prompts. Want to measure “factual correctness,” “brand voice adherence,” or “safety compliance”? Just change the prompt. No retraining needed.
Efficiency and Calibration of Reward Models As a classifier, PGRM runs fast and at scale, with no expensive text generation per evaluation. It also provides confidence scores, helping you triage uncertain cases and focus human review where it matters most.
Unified Governance & Continuous Improvement PGRM harmonizes evaluation, monitoring, and reward modeling with a single flexible prompt, so you can surface top-performing responses, fine-tune models using reinforcement learning, and reduce manual effort without sacrificing oversight.
This aligns with the Databricks AI Governance Framework, which emphasizes responsible oversight, transparency, and performance at enterprise scale.
Proven Success: Benchmarks That Speak Volumes
Judge-like accuracy: Achieves an average of 83.3%, nearly matching GPT 4o (83.6%) on evaluation tasks like answer correctness and context faithfulness.
Reward modeling leadership: On the new RewardBench2 benchmark, it ranks #2 as a sequential classifier and #4 overall, with a score of 80.0, outperforming GPT 4o (64.9) and Claude 4 Opus (76.5).
That makes the Prompt-Guided Reward Model the first system to deliver frontier-level performance as both an instructable judge and a highly calibrated reward model, without compromising on efficiency.
Real-World Gains: What Adopters Can Unlock
Unified AI Governance with One Prompt No more juggling disjointed monitoring tools. With Databricks PGRM, a single prompt controls judging, scoring, fine-tuning, and oversight, making AI evaluation more streamlined, transparent, and adaptable.
Smarter Use of Expertise PGRM’s calibrated confidence helps identify which decisions are borderline or “low confidence,” directing domain experts to review only what matters most. This supports LLM oversight practices by combining automation with human-in-the-loop governance.
On-Demand Flexibility Without Retraining Business needs evolve. With PGRM, you simply adjust the prompt. Want to tighten safety compliance today, usher in brand tone guidelines tomorrow? Prompt it, PGRM instantly adapts. No costly model retraining needed.
Reward Said—and Resolved Use the Prompt-Guided Reward Model to automate the selection of best responses, feed them back for model fine-tuning via RLHF, and build continuous improvement loops. Better answers, fewer manual reviews, on autopilot.
In short: Databricks PGRM delivers what neither judges nor reward models could offer alone. This reflects Generative AI governance in action, combining Databricks innovation, AI-Powered Data Governance, and strong Databricks AI Governance for the future of AI governance.
Final Pitch: Why Your AI Should Embrace PGRM Now
In today’s world, building responsible, aligned, and high-performing AI is not a one-time effort, it’s an ongoing journey. Databricks PGRM supercharges that journey with:
Adaptability Instantly pivot your evaluation criteria via prompt tweaks, without model training delays.
Confidence & Efficiency Score thousands of responses at scale, complete with calibrated confidence to guide smart reviews.
Continuous Improvement Identify top answers, replay them into RL pipelines, and incrementally elevate your AI’s performance.
Integrated Oversight Collapse siloed tools into one unified, prompt-powered model—simpler, clearer, more powerful control.
Forward-looking organizations are also exploring the future of AI governance, where technologies like LLM oversight, Responsible AI tools, and Generative AI governance play critical roles in reducing risk while amplifying innovation.
Ready to Transform Your AI’s Quality Culture?
PGRM is not just a model, it’s a new paradigm for AI alignment, governance, and continuous improvement. Whether you’re enforcing safety protocols, maintaining factual accuracy, or preserving brand voice, PGRM offers a leaner, smarter path forward. By adopting AI-Powered Data Governance strategies alongside Databricks innovation, enterprises can confidently scale oversight with measurable impact.
The future of AI governance is here. Judging with confidence doesn’t just feel better, it performs better. And with Responsible AI tools like the Prompt-Guided Reward Model, Databricks AI Governance, and ongoing Databricks innovation, your organization can lead the charge.
