RapidPLMSync

Rapidflow Inc. offers a range of expertise and services to help your organization streamline and optimize your Oracle Enterprise Business Suite (EBS) with our Agile-EBS Connector, RapidPLMSync.
RapidPLMSync is designed to integrate various data such as parts, part attributes, documents, bills of materials, and approved manufacturers list with EBS, making it easy for your organization to manage and automate these processes.
Rapidflow Agile PLM - ERP Connector

Product Capabilities

  • Visibility: Get visibility in your Oracle PLM and Oracle EBS (ERP) dataset with powerful Dashboards and Reports
  • Detect Imbalance: Slice and dice reports to identify imbalances between two systems like Item imbalance, ECO imbalance, Manufacturer Imbalance, Manufacturer Part imbalance
  • Data Quality: Discover bad data quality with missing Agile PLM and EBS Engineering objects and its attributes
  • KPIs: Automate Reports with KPIs to improve data flow from Agile PLM to Oracle EBS and follow best practice
  • Data Flow: Bi-directional data flow capability that is from Agile PLM to Oracle EBS and vice-versa
  • Supported Objects: Supports all Agile PLM and Oracle EBS engineering objects like Item, BOM, Change Orders, Manufacturer, Manufacturer Parts, Sites and its attributes
  • Automation: Complete automation for engineering data flow from Oracle Agile PLM to Oracle EBS
  • Error Management: Error handling capability. If any object transfer fails between systems, automated report quickly detect them and shows them in dashboard

Why Rapidflow?

  • Rapidflow is a global professional services company and a leading Oracle Partner, with over 13 years of expertise and capabilities in Oracle products and technologies. The company has specialized skills across multiple industry domains and a global team of more than 250 consultants spread across office locations in the US, India, and the Middle East.
  • Rapidflow offers a range of services including End-to-End Implementation, System Integration, and Application Management Services (AMS) for Oracle Fusion Cloud, Oracle E-Business Suite, NetSuite, and RPA (Robotic Process Automation). The company’s unique methodology, Rapid Discovery & Design (RD²) combines with Oracle Unified Method (OUM) to deliver efficient and effective solutions to the  clients.
Why Rapidflow
  • Rapidflow’s team of experts with deep domain and technical knowledge, coupled with their experience in delivering large-scale, complex projects, makes it a trusted partner for Oracle-based solutions. We understand client’s unique business requirements and provide customized solutions that align with the client’s business objectives, sets it apart in the industry. Rapidflow’s focus on delivering quality solutions, on-time and within budget, ensures a rapid return on investment for their clients.
  • Rapidflow is a leading consulting company in the area of Oracle Supply Chain, Product Lifecycle Management, Master Data Management and Business Intelligence. Our focus is on delivering quality solutions through its Rapidflow Implementation Methodology, with real-world experience and unmatched applications expertise, Rapidflow ensures not only implementation success but also guarantees a rapid return on investment for its clients. The company’s team-driven approach helps its clients achieve their corporate goals and maximize operational and financial performance. Rapidflow provides its customers with accelerated business flows and Oracle-based productivity solutions that help organizations improve their efficiency, visibility, and security of their business processes, and make data-driven decisions.

Featured Insights

AI in Motion: Smarter Routes, Leaner Costs

From Planning to Predicting Transportation planning isn’t new. It has long enabled enterprises to plan, execute, and optimize shipments. But even the most advanced systems still relied on human planners comparing options, weighing preferences, and manually adjusting routes. That’s where Oracle’s Order Route Optimization AI steps in. It doesn’t replace any planning rather it enhances it. By learning from historical data and user preferences, AI reduces the noise, predicts optimal routes, and hands planners a streamlined, cost-conscious plan. In other words, what once required efforts of manual tinkering now comes pre-optimized with intelligence built in. The Shift: From Overspend to Smart Spend Before AI: Even when optimized routes were planned, sub-optimal decisions slipped through like sending trucks half-full, choosing costlier carriers, or missing opportunities for consolidation. Across hundreds of shipments, these “small misses” quietly ballooned into major cost leakage. With AI: The system pinpoints routes that balance cost and reliability. It “remembers” which carriers consistently overcharge, which lanes fail to deliver, and avoids repeating expensive mistakes. Every shipment planned smarter → every dollar saved scales across the network. A Practical Use Case: Smarter Shipments in Action A distribution hub manages three product lines: tablets, smartphones, and laptops, they being shipped to Location1 through Locations. Before AI: Every product shipped directly from the hub to each location, leading to higher costs, duplicated trips, and inefficiency. After AI: AI consolidates and optimizes routes thus combining shipments smartly (e.g., Tablets → Smartphones → Laptops in one run, and Tablets + Laptops on another), reducing fuel, tolls, and planning time. Same deliveries, fewer trucks, optimized miles. The result? Lower transportation spends, faster deliveries, and planners freed from micromanaging shipment routes. The Takeaway With embedded AI, Oracle has turned transportation planning from a cost center into a cost optimizer. The roads are the same, but the spending is leaner, the journeys smarter, and the savings undeniable.

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Talk to Your Policy: How Conversational Agents Transform Insurance Queries

Health insurance questions never come at the right time. They usually appear in moments of urgency, right before a hospital admission, while filling out claim forms, or when an unexpected medical bill lands in your inbox. Picture this: A new parent wonders, “Will my baby be covered under my policy from birth?” An employee working late at night asks, “Does my plan cover emergency room visits?” Another employee preparing for surgery asks, “What’s the pre-approval process for cashless treatment?” The answers exist but they’re locked away in dense policy documents, buried across HR portals, or waiting in an HR team’s overflowing inbox. By the time clarity arrives, the employee has already wasted valuable time and experienced unnecessary stress. Even reaching out to HR or the customer care team isn’t always the fastest solution. Employees may need to wait sometimes hours, sometimes days before getting the right answer. Meanwhile, HR or the representative must dig through policy details themselves, balancing speed with the responsibility of providing accurate information. Now imagine if things worked differently. What if employees could simply ask their questions in plain language and instantly receive clear, accurate, policy-backed answers? Better yet, what if they could continue the conversation naturally, asking follow-up questions without starting over each time? This is exactly what conversational agents for health insurance make possible. They transform long, complex policy documents into a simple dialogue—always available, always contextual, and always ready to help. No searching. No delays. No policy confusion. Just clarity, delivered through an ongoing conversation with your policy assistant. This is the power of natural language-driven, AI-powered conversational agents transforming how employees access policy information. Speak the Question, Get the Answer Imagine a smarter approach where employees can simply ask natural questions like: “What’s the maximum maternity coverage under my insurance?” “Does my policy cover pre-existing conditions?” “Which hospitals are in my cashless network?” “What is the claim process for outpatient treatment?” With UiPath Conversational Agents, these aren’t just queries, they’re conversations. The agent maintains context, retrieves exact clauses from policy documents, and gives clear, compliant answers. The Solution: Conversational Agents with UiPath With UiPath’s Conversational Agents, policy communication moves from manual searching to instant answers. Unlike static FAQs or chatbots, conversational agents understand context, ask clarifying questions, and trigger workflows when needed. For example: If the query is about coverage, the agent fetches details from the stored policy document. If the query is about claim submission, it can trigger an autonomous agent to open the reimbursement form in the HR portal. If an exception arises, it is routed to HR or the insurance desk via Action Center. TestCase:- Why UiPath Conversational Agents for Policy Queries? More Than FAQs: Not keyword search, context-aware understanding of your insurance documents. Faster Responses: Get answers in seconds, not days. Built for Complexity: Handles lengthy, unstructured policy documents effortlessly. Human + Bot Collaboration: Agent answers, bots trigger workflows, HR steps in only for exceptions. Easy Integration: Connects to your HR systems, policy portals, and document repositories. Scales with You: Whether it’s 100 or 10,000 employees asking, the system grows with your needs. The Results: Measurable Impact Reduced HR workload by almost 60% on repetitive policy queries. Employees get instant, accurate answers directly from their policy docs. Faster claim initiation → smoother employee experience. Ready to Transform Policy Communication? Whether you’re a mid-sized firm or a global enterprise, UiPath Conversational Agents let employees simply ask in natural language and the system takes care of the rest. Empower your HR and insurance teams to focus on decisions, not document lookups. Because policy clarity should be as simple as asking a question.

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Identity and Access Management (IAM)

Boosting Productivity with Lead-Time Insights AI

In supply planning, productivity doesn’t just mean working harder, it means working smarter. Yet planners often find themselves buried in spreadsheets, reconciling supplier promises. Hours are lost trying to figure out why orders are late, which suppliers can be trusted, and where to focus. The result? Slow decisions, missed opportunities, and planners left exhausted. Oracle Fusion Cloud is changing that with Lead-Time Insights AI turning the invisible into visible, and the messy into manageable. Precision Lens: Where Numbers Tell the Story The first step is clarity. With the Supplier Variance Table, the fog lifts. Average days late. Variance percentages. Historical performance trends. Cold, hard numbers reveal who’s delivering as promised and who’s quietly drifting off course. There’s no hiding behind vague excuses or anecdotes just the truth in black and white. This precision allows planners to prioritize with confidence: focus on the few suppliers causing the most disruption instead of spreading energy thin. The Detective Mode: Every Order, Every Detail But productivity isn’t just about knowing the big picture, it’s also about finding the fine cracks before they spread. With the Order Details View, each shipment becomes a case file: Ordered here. Received there. Variance marked in bold. Delays stop being abstract trends and become individual stories of movement and misstep. This order-by-order transparency uncovers the hidden causes of variance: a bottleneck at customs, a carrier delay, or a supplier batching orders inefficiently. Suddenly, planners aren’t firefighting. They’re problem-solving. The Productivity Angle: Doing More with Less Effort Lead-Time Insights AI frees planners from hours of manual detective work. Instead of chasing data, they act on insights. The productivity lift comes in multiple ways: Faster decisions: Prioritize the top variance drivers instantly. Smarter meetings: Walk into supplier calls with facts, not guesswork. Focused interventions: Fix root causes instead of patching symptoms. The result: Planners spend less time crunching numbers and more time driving value. Industry Verticals Where Productivity Gains Multiply Pharmaceuticals Drug supply chains are regulated and time-sensitive. Lead-Time Insights allows planners to spot recurring supplier delays at the batch level enabling faster corrective actions and ensuring lifesaving products reach patients without delay. Industrial Manufacturing In industries where downtime costs millions per hour, productivity depends on proactive planning. Supplier Variance Tables highlight which parts of the network are unreliable, letting planners focus energy where it matters most preventing costly downtime. Consumer Packaged Goods (CPG) Fast-moving products leave little margin for inefficiency. By investigating shipment-level details, planners identify chronic bottlenecks (like repeated carrier delays), address them, and keep the supply chain flowing smoothly. Mini Case: Pharma Company Doubles Planner Efficiency A mid-sized pharmaceutical manufacturer struggled with recurring supplier delays, often uncovered only when production schedules slipped. Planners spent hours chasing shipment details across emails and spreadsheets. After deploying Oracle Lead-Time Insights AI, the team relied on the Supplier Variance Table and Order Details View to pinpoint the worst offenders. In just one quarter: Planner investigation time dropped signigicantly. Supplier performance review meetings became data-driven, cutting prep time in half. Corrective actions were logged and tracked order-by-order, reducing repeat delays. With Lead-Time Insights AI, productivity is no longer about adding more hands-on deck. It’s about giving every planner the power of visibility, precision, and detective-level clarity, so the entire supply chain works faster, smoother, and sharper.

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Smarter Descriptions, Better Experiences: AI in Product Lifecycle Management

In the digital marketplace, first impressions happen in seconds often through words. A vague, inconsistent, or incomplete product description can mean the difference between a customer clicking buy now or moving on. For product managers, however, creating consistent, feature-rich, and SEO-friendly descriptions across thousands of SKUs is a time-consuming challenge. Manual processes are error-prone, repetitive, and often disconnected from what customers actually want to read. Oracle Fusion Cloud changes the game with AI-driven Item Description Generation turning dry product data into engaging, customer-friendly narratives at scale. From Silent Codes to Clear Narratives Generative AI doesn’t just “fill in the blanks.” It reimagines how product information is expressed: Transforms attributes into sentences → Specs like “20L, Black, Industrial Ink” become clear sentences. Keeps accuracy intact → No creativity at the cost of compliance. Ensures catalog consistency → Standard language across every SKU. Bridges business and people → Technical data becomes approachable text. In other words: data becomes dialogue. How It Works Input: Item master attributes (codes, dimensions, supplier details, use cases). AI Processing: Oracle’s embedded Generative AI models tuned for SCM turn this data into fluent, human-readable text. Output: Draft descriptions in natural language — ready to review, approve, and publish across catalogs. Before vs After Example #1 Before (Code-Only): INK-BLK-20L After (AI-Generated): Industrial Black Ink, 20-liter container. High-density formula for large-scale printing and manufacturing. Supplied by XYZ with a shelf life of 18 months. #2 AI Assist button that generates the Product description AI Assist button that re-generates/reframes the generated description The difference? One is a code. The other is a story. The Customer Experience Edge With AI-crafted descriptions, customers can: Find products faster thanks to clearer keywords. Understand benefits instantly without digging through specs. Trust the brand voice across channels and SKUs. For companies, this translates into fewer abandoned carts, faster buying decisions, and higher satisfaction scores. Why This Matters in Supply Chain Beyond customers, clear descriptions improve the entire value chain: Procurement Efficiency → No confusion over duplicate or vague items. Inventory Visibility → Clean catalogs reduce redundancy and errors. Commerce & Engagement → Richer descriptions build trust and boost conversion. Supplier Communication → Faster, clearer collaboration across regions and partners. The Consistency Advantage: Every Channel, One Voice Manual descriptions often vary by channel or region. AI ensures that every product story stays aligned with brand tone and terminology whether it’s an e-commerce portal, distributor catalog, or mobile app. This consistency eliminates confusion, builds trust, and strengthens customer loyalty. Industry Verticals Where This Matters Most Retail & E-commerce Thousands of fast-moving SKUs need crisp, compelling descriptions to win digital shelf space and boost conversion rates. Industrial Equipmen Complex technical specifications reframed into benefit-driven language help customers (and even sales reps) quickly grasp why a product matters. Healthcare & Medical Devices Precise yet clear descriptions ensure compliance with regulations while making product usage understandable to both professionals and end-users. Mini Case: Electronics Retailer Boosts Online Sales A leading consumer electronics retailer faced a challenge: their website hosted over 25,000 SKUs, but product descriptions were inconsistent — some too technical, others incomplete. Customers struggled to compare models and often abandoned their carts. With Oracle’s AI-powered Item Description Generation, they re-wrote their catalog in just weeks. Each product now carried descriptions that highlighted key features + customer benefits in plain language. The results were immediate: Search-driven discovery improved, reducing bounce rates. Conversion rates on high-value items rose. Customer support queries about “what this product does” dropped significantly. AI didn’t just write words; it created better customer journeys. With Oracle’s AI-driven Product Description Generation, organizations move beyond static product data. They deliver experiences that inform, inspire, and convert at scale.

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Cutting Costs with Smarter Lead Time Insights

Planning is only as good as the assumptions behind it. For decades, businesses have relied on static supplier lead times — numbers set once in the system, rarely updated, and often far from reality. The result? Either excess stock gathering dust in warehouses or constant firefighting with expedite orders when things don’t arrive on time. Both are expensive. Oracle Fusion Cloud: Changing the Story Oracle Fusion Cloud changes the story with Lead-Time Insights AI – an intelligent companion that sees beyond assumptions, listens to your data, and whispers the truth about where time is lost and where it can be regained. Instead of drowning in spreadsheets, planners are greeted by a Treemap Overview. Each supplier and item is a block: the bigger the block, the bigger the impact; the warmer the color, the greater the variance. It’s more than data. It’s a landscape of time itself showing planners not just where problems exist but where opportunities lie. The Cost Angle: Where Savings Appear When AI surfaces real supplier performance, planners can reduce the “extra buffers” that inflate costs. If suppliers consistently deliver faster, safety stocks can be trimmed down, lowering inventory carrying costs and freeing up working capital. If suppliers consistently deliver slower, procurement can act proactively instead of resorting to costly last-minute expedite orders. Over time, the enterprise cuts down both waste and working capital locks, while maintaining service levels. Industry Verticals Where It Matters Most Retail & Consumer Goods Fashion trends fade quickly. Seasonal products have a short shelf life. With Lead-Time Insights, retailers avoid overstocking fast-fashion items by aligning lead times with actual supplier performance. This means fewer markdowns, less clearance stock, and healthier margins, all while ensuring stores are stocked at the right time. Automotive Automotive supply chains are famously complex, with tier-2 and tier-3 suppliers feeding critical parts into the production line. A missed delivery can stop production cold. By using AI-driven lead time accuracy, manufacturers can hold less buffer stock while still ensuring continuity. The result: reduced inventory costs across thousands of parts, without jeopardizing production schedules. High-Tech Electronics Semiconductors and high-value electronic components come with high carrying costs. Traditionally, companies held weeks of safety stock to offset uncertain supplier lead times. With Oracle Lead-Time Insights, planners identify which suppliers consistently meet or beat commitments. This allows them to reduce buffer stock and free up millions in working capital crucial in a cash-intensive sector like high-tech. Mini Case: Electronics Manufacturer Unlocks Hidden Savings A leading consumer electronics company producing smartphones faced ballooning inventory costs. The system assumed a 20-day lead time for semiconductor suppliers, but AI analysis revealed three key suppliers consistently delivered in 14–15 days. Armed with this insight, planners safely reduced safety stock across multiple product lines, cutting inventory by 15%. The financial impact was significant: millions of dollars in freed working capital, without compromising product availability during peak launch season. What once looked like a “necessary cost of doing business” turned into an efficiency opportunity unlocked by Oracle’s Lead Time Insights AI.

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Give your Supplier Search a Productivity Boost

Think about a business negotiation in everyday life. Let’s say you are planning a wedding and need a photographer. You already know a couple of photographers who have worked with your friends, but you wonder if there are better options? Someone with a different style, better availability or a more competitive package. Searching online, comparing portfolios, and reaching out can take hours. Now imagine if, with one click, you were instantly shown a shortlist of vetted, reliable photographers who matched your preferences. That’s time saved, better options discovered and confidence in your choice. This is exactly the kind of productivity boost Oracle is delivering to procurement teams with its new Discover New Suppliers feature, powered by Generative AI, inside the Fusion Cloud. Negotiations in a Smarter Era In business, negotiations are at the heart of procurement. Companies often start with their trusted suppliers but there is always the need to explore new partnerships to expand opportunities, drive better deals, and stay competitive. Oracle Fusion Cloud Procurement has long provided a robust platform for managing negotiations. The new embedded AI feature enhances this journey. Instead of replacing existing capabilities, it builds on them, making the process more productive and intelligent. How This Works? Here’s how it works in practice. The journey starts in Oracle’s Purchasing module, where users open Manage Negotiations, select the negotiation they’re working on, and view the list of existing suppliers. But what if you want to expand the list? Along with the manual search, Oracle now offers an added feature too, a built-in Discover New Suppliers button. Click it and AI gets to work. Within seconds, you see a curated list of potential suppliers that match your needs. For example, if you are looking for technology partners, you might see Dell pop up as a recommended supplier. From there, you can jump directly to Dell’s official website to explore products, services, and opportunities, without ever leaving Oracle’s platform. It’s like having a smart assistant who knows the supplier landscape and brings the right options straight to you. Instead of spending time finding suppliers, procurement teams can spend their energy on what truly matters – strategizing, evaluating, and building strong supplier relationships. Procurement teams have always had the tools to manage supplier negotiations effectively. What Generative AI does here is make a good process even better by removing the repetitive, low-value tasks and letting teams focus on high-impact decisions. Driving Smarter Procurement with built-in Oracle AI and Rapidflow Negotiations are about choices, timing, and relationships. By embedding Generative AI directly into the procurement process, Oracle empowers businesses to discover more, decide faster, and negotiate smarter. With this feature, procurement teams don’t just save time, they gain productivity, confidence, and a competitive edge. At Rapidflow, we help organizations unlock the full potential of Oracle’s AI innovations. Whether it’s boosting productivity in procurement, streamlining financial processes, or enabling smarter Planning decisions, our expertise ensures businesses can maximize the benefits of Oracle AI.

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Training the Brain

Training the Brain: How We Fine-Tune AI Models for Our Needs

Have you ever thought about building your own large language model (LLM) for a custom task something magical that understands your world perfectly? Many of us have had that spark of inspiration at some point. The idea of creating an AI that speaks your language, follows your workflows, and responds just the way you need it to it’s exciting. But then comes reality: Building an LLM from scratch is complex, time-consuming, and resource intensive. For most teams, the dream fades quickly. That’s where fine-tuning changes everything. Instead of starting from zero, what if you could take a powerful, pre-trained model and teach it your domain, your data, your goals? Fine-tuning makes that possible. It’s like customizing the brain of a super-intelligent assistant so it understands you. Introduction: Artificial Intelligence is powerful, but to truly make it work for us our domain, our language, and our users we need more than just out-of-the-box solutions. That’s where fine-tuning comes in. Think of it as teaching an AI model not just general knowledge, but your company’s language, systems, and goals. In this article, we’ll walk through what fine-tuning means, why it matters, how we use it, and where it fits into the bigger picture of applied AI. What is Fine-Tuning Fine-tuning is the process of taking a pre-trained AI model (like GPT, T5, or BERT) and retraining it on a smaller, task-specific dataset. This helps the model specialize in understanding specific domains, jargon, and patterns relevant to a business or use case. It’s like hiring a smart new team member they already know a lot, but you still need to train them to follow your processes and use your vocabulary. Importance of Fine-Tuning Fine-Tuning Workflow Let’s understand with a sample example: Let’s say while working on a natural language task — for example, converting plain English into a SQL query using a large language model (LLM). Now imagine the prompt is: “List the completed orders in the past month.” A general-purpose LLM might return a syntactically correct SQL query because it understands SQL structure and grammar. However, it won’t necessarily return a semantically correct or executable query. Because the model doesn’t know the schema of your database it doesn’t know: What are the table names (Is it orders or sales_orders?) What “completed” means (Is it a status column? What values represent completion?) Which column tracks dates (Is it created_date, order_date, or something else?) In this case the sample output we may get is, “SELECT * FROM orders WHERE status = ‘completed’ AND order_date >= DATE_SUB (CURDATE (), INTERVAL 1 MONTH);”. In this case, the model interpreted “status” as a column and “completed” as a value, but it was unclear whether the table name was “orders” or “sales_orders.” This highlights the ambiguity in selecting table names, column names, attributes, and values. The structure is correct, but this query fails. Why? Because the model doesn’t know your data. The correct table is actually called “sales_orders” The status column uses ‘Closed’ instead of ‘completed’ The date column is “created_on”, not “order_date” This is where fine-tuning comes in and where different techniques help you train the model to speak your language, learn your schema, your vocabulary, and your logic to generate not just correct code, but context-aware, business-ready solutions. Now let me walk through you with the few fine-tuning techniques how actually helps us in fine-tuning tasks, 1. LoRA (Low-Rank Adaption) What if you want to fine-tune a really big model like one with billions of parameters but you don’t have a data center? QLoRA is your tool. LoRA as the name suggests, is a Low Rank Adaption technique; it introduces small trainable low- rank matrices while keeping the base model frozen. LoRA is like slipping a few sticky notes into a giant textbook. Instead of rewriting the whole model, you insert small trainable layers LoRA matrices that quietly learn your patterns. When you train LoRA with your examples: It learns that “completed” = ‘Closed’ It understands that “orders” refer to sales_orders It memorizes that “past month” = filter using created_on These small changes plug into the original model and subtly shift how it behaves just enough to get things right for your domain. This approach is limited by memory constraints, as handling a large number of parameters can require substantial GPU resources. To mitigate this, 4-bit or 8-bit quantization can be used. 2. QLoRA (Quantized Low-rank Adaption) What if you want to fine-tune a really big model like one with billions of parameters but you don’t have a data center? QLoRA is your tool. It works just like LoRA but adds quantization shrinking the model’s memory footprint to 4 bits / 8-bits while preserving its brainpower. When you fine-tune using QLoRA: You can train on massive prompt variations like “closed”, “done”, “fulfilled” all mapped to ‘Closed’ You can fit schema awareness into low-resource environments (even Google Colab) You get smarter outputs without spending on huge GPUs. 3. Adapter based Fine-tuning Adapters are like browser extensions for your AI model. They sit inside the model like tiny assistants, learning only your business logic while the rest of the model stays untouched. In training: Adapters learn your internal table names and columns They translate “completed” into ‘Closed’ even if the term changes across departments They help the model stick to your organization’s terminology You can even have different adapters for different clients, departments, or schemas and swap them in without retraining the full model. Your base LLM remains powerful and general, but whenever it needs to do your tasks, it plugs in an adapter like switching from “general-purpose” to “expert mode.” Rapidflow in Action: Whether you’re looking to build powerful AI Agents using Oracle AI Agent Studio enabling you to create intelligent agents that respond to any kind of knowledge base you provide, even without being a pro programmer for your business or personal use, or you want Genai seamlessly integrated into your Oracle on-premises applications, we’ve got you covered. Or perhaps you need an embedded chatbot

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Just Ask: How Natural Language Is Changing Invoice Automation

It’s the end of the month, and the finance manager makes a simple request: “Can someone pull all the invoices from VendorX for this quarter?” What sounds like a straightforward task quickly becomes a manual grind. The team dives into shared folders, email inboxes, and scattered file drives, shifting through PDFs, scans, and attachments all in different formats. Each file must be opened, read, and checked for relevant details. Hours are spent copying data into spreadsheets, checking for errors, and trying to meet payment deadlines under pressure. Now imagine a different approach: the manager types that same request into an intelligent automation system “Find all invoices from VendorX for Q2 and extract invoice numbers, due dates, and amounts.” Within seconds, the system uses AI to scan every folder, understand each document, identify the relevant invoices, and extract the exact data needed. No digging, no sorting, no manual entry. This is the power of natural language-driven, AI-powered invoice automation and it’s transforming how finance teams operate. Speak the Work into Action Imagine a smarter, more accurate approach where your finance team can give natural language instructions like: “Find all invoices from VendorX in the July folder over $5,000” “Extract due dates and amounts from this month’s scanned invoices” “Check for duplicates across folders for August invoices” With Rapidflow and UiPath Agentic Automation, these commands aren’t just possible, they’re how the system works. Our solution combines natural language understanding, AI document processing, and intelligent automation to scan folders, find relevant invoices, and extract data, all based on plain-English instructions. The Solution: Agentic Automation with UiPath With UiPath’s Agentic Automation, your invoice process transforms from manual chaos to streamlined precision. Unlike basic automation, agentic bots don’t just follow script: They think, collaborate, and adapt. They handle complex documents, route exceptions, and work together to complete end-to-end workflows without human intervention (unless needed). Test Case: Why UiPath Agentic Automation for Your Business? More Than Just Bots: UiPath goes beyond basic task automation, its agentic approach enables bots to think, collaborate, and adapt just like a human instruction. Faster Workflows: Automate end-to-end invoice processes to move at the speed of business. Built for Complexity: Handles unstructured data, exceptions, and diverse formats effortlessly. Human-Bot-AI Collaboration: AI understands your instructions; Bots handle the repetitive work while your team focuses on strategy and decision-making. Easy to Integrate: Works with your existing ERP, accounting, and document management systems, no rip-and-replace needed. Scales With You: Whether you’re processing hundreds or thousands of invoices, the system grows with your business needs. The Results: Measurable Impact Ready to Transform Your Invoice Process? Whether you’re a startup buried in paperwork or a large enterprise seeking smarter scale, Rapidflow’s agentic automation lets you simply speak your needs in natural language and watch the system handle the rest. Empower your finance team to work faster, smarter, and more strategically because automation should understand you, not the other way around. Say goodbye to spreadsheets and hello to effortless control.

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Stop Phishing at the Source: AI for Enterprise Email Protection

Imagine this: You’re wrapping up a long day. An email hits your inbox with subject line: “Urgent: Payment Confirmation Needed.” It’s from a familiar-looking sender, formatted professionally, and the tone carries just the right sense of urgency. You forward it to finance. Another task, done. But this time, it wasn’t just another task, it was the beginning of a phishing attack. One click. One forward. And now your business faces a chain reaction: data breach, financial exposure, compliance risk, and reputational harm. Where Human Error Meets High Stakes Human error is inevitable especially in moments of fatigue, pressure, or distraction. Even the most diligent employees can misjudge a situation. And that’s exactly what cyber attackers exploit. Email, once a simple communication tool, has become a primary entry point for cyber threats. It’s not just a message in your inbox it’s a potential doorway into your business. Despite firewalls, spam filters, and employee training, sophisticated phishing emails continue to bypass defenses. Why? Because attackers are no longer just relying on technology, they’re leveraging psychology. Their messages are more personal, more urgent, and more convincing than ever before. In this context, phishing is not just a technical threat it’s a critical human error risk with high-severity consequences that ripple across your organization: And perhaps most concerning is the damage often isn’t detected until it’s too late. The cost of a single phishing mistake? It can range from thousands to millions, depending on the scale and sensitivity of the exposure. AI-Powered Email Analysis: Reducing Error, Protecting Business To minimize this risk, we’ve developed an AI-powered Phishing and Spam Detection solution built on UiPath’s agentic automation platform. It’s not just another filter. It’s an intelligent safeguard that supports human judgment and catches what people can miss. Automated Action Based on Risk Classification Based on the email type, intelligent workflows can be triggered to reduce response time and risk exposure: Test Case: Send an email to IT Team  This flexibility ensures that organizations can customize responses based on their internal security policies and risk tolerance. Why Choose This Solution? This solution directly addresses one of the most common and costly forms of human error in business today: trusting a malicious email. Employees make fast decisions under pressure Attackers rely on that speed, not lack of intelligence Even one mistake can trigger catastrophic loss With this solution, you introduce an AI-based control layer that: With this solution, you introduce an AI-based control layer that: Catches errors before they become incidents Reduces dependency on employee judgment under stress Enhances organizational resilience against advanced threats it’s not about removing the human, it’s about supporting the human with automation that’s always alert, always objective, and always fast. Better Than Just Prevention: It’s Proactive Protection This isn’t just spam control. It’s a critical error-prevention mechanism. By embedding intelligence into your email workflows, you: Lower risk of human error Minimize chances of financial or reputational damage Ensure faster, smarter responses to threats And most importantly, you give your teams the confidence to work without fear knowing AI is backing them up. Explore how Rapidflow’s AI-powered Email Threat Detection fits into your Error Reduction strategy. Let’s make smarter decisions together, with automation.

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