Chronos Systems’ Post

Chronos Workflow Enters a New Dimension: Introducing Google AI-Powered Intelligent Document Processing We're proud to announce that we've empowered the CWP process and document management system with the power of Google Cloud Document AI This is one of the most advanced developments in the CWP process and document management system to date: the integration of artificial intelligence is realized with the help of market-leading Google Cloud Document AI technology. With this step, we are not only introducing a new feature, but also revolutionizing the processing of company documents and related workflows. How Does AI Powered Data Extraction Work? The process is simple and efficient. When a user uploads a document to the CWP system—whether it's a scanned image (pl. JPG, PNG) or a digital document (pl. PDF)—Google's AI engine gets to work automatically or on request: 1. Scan and Text Recognition (OCR): If the document is image-based, the system performs state-of-the-art optical character recognition (OCR), converting the content into digital text. 2. Intelligent Data Labeling: AI analyzes the text and context of a document, then automatically (or based on pre-trained models) identifies and "labels" key data. Such can be the name of the customer, the invoice number, the payment deadline, the items and the total amount on an invoice. 3. Automatic Data Upload: The CWP system recognizes these AI-generated tags and automatically inserts their corresponding data into the appropriate data fields defined in the system. Key Benefits For Your Business: * Dramatic Efficiency Gains: Eliminate manual data entry and speed up document processing by up to 80-90%. * Cost and time savings: With automated processes, you can save valuable working hours that colleagues can use for strategically important tasks. * Superior accuracy: AI-powered data extraction reduces errors due to human error, ensuring reliable and consistent data quality. * Scalability: The system can easily cope with the growing volume of documents without the need to proportionally increase administrative resources. Level up with CWP and harness the power of AI to optimize your business processes! Learn more about the new feature and request a personalized demo from our experts. #CWP #AI #WorkflowAI #DocumentmanagementAI #Automation #BPAAI #Digitization #GoogleAI #GoogleCloudAI

Apace’s AI-enabled media data layer fills the enterprise content gap: Most enterprises nail document governance—but stumble with video, audio, and creative media. That gap is exactly where Apace DataManager + Cloud Channels shine. What Apace brings to completing enterprise's content management is taming media data from ingest, aggregation, automated cataloging to archive and secure/authorized user consumption via: Offering Single unstructured data space for all storage silos at edge and cloud targeting media and non-media data content Policy automation for media: apply retention, legal holds, distribution rules at ingest—not after the fact. AI-ready metadata: edge pre-processing with automated facial, object recognition, scene detection and audio transcription Hybrid freedom: Managing data anywhere at edge and cloud with special services for media and in specific video data such as proxy generation and AI auto-indexing. What the Content Cloud contributes: Unified governance & security across all content types (docs + media) with audit, classification, and zero-trust controls. Workflow & e-sign to operationalize reviews, approvals, and attestations. AI/automation agents to extract insights, route tasks, and answer questions over governed repositories. Global residency + customer-managed keys for regulated workloads. The outcome: A single policy plane for your richest content—meeting recordings, training footage, creative assets, field/surveillance video—turned into compliant, searchable institutional memory that actually accelerates work. If you’re building AI programs on messy, multi-site media, pairing Apace’s media data plane with a governed content platform isn’t just additive—it’s transformative. #AI #DataGovernance #EnterpriseContent #MediaWorkflows #MAM #Compliance #AIOps #DataLifecycle #EdgeComputing #UnstructuredData

Agentic AI is the next structural shift. We're moving from AI that responds to prompts to AI systems that can autonomously: 1. Plan: Break down a complex goal into multi-step tasks. 2. Act: Execute those tasks using multiple tools (APIs, databases, web). 3. Self-Correct: Learn from mistakes and adapt the plan in real-time. Example in Action: Imagine an AI agent for a marketing team that doesn't just write a social post, but coordinates the campaign, schedules across platforms, tracks metrics, and adjusts the ad spend all on its own. This isn't just automation; it's delegation at scale. Are you ready to redesign your workflows around autonomous agents? Call to Action: What is the first complex, multi-step process in your organization you'd hand over to an AI Agent? Share your ideas below! Google Google Cloud CNTXT Abacus #AgenticAI #FutureofWork #AIAgents #ArtificialIntelligence #Automation

🌟 A Little AI Journey I’m Excited About… 🌟 A few weeks ago, I started exploring Vertex AI. Honestly, I wasn’t sure what to expect another cloud ML tool? But as I dug in, I realized it’s not just a tool; it’s a whole ecosystem transforming how companies build AI. 💭 Imagine this: A retail company wants to predict what products will fly off the shelves next month. Traditionally, it would take weeks of data wrangling, training models, deploying them… a lot of moving parts. Now, with Vertex AI pipelines, they can automate the entire workflow: Clean the data ✅ Train and tune models automatically ✅ Deploy models with monitoring and retraining ✅ All in one unified, scalable platform. And it’s not just retail: 🏦Banks are creating smarter chatbots and fraud detection systems. 👨⚕️Healthcare firms are leveraging AI to assist in early diagnosis. 🏭 Manufacturing is predicting equipment failures before they happen. 🚀 What amazes me most is how quickly industries are adopting Vertex AI. The speed, efficiency, and integration it offers are game-changing. 🎯 Why I’m Sharing This: I’m currently learning Vertex AI and preparing for my PMLE certification. Every step I take here feels like unlocking a new superpower not just understanding AI models, but also how enterprises can truly leverage them at scale. So here’s my thought: 💬 Have you ever imagined AI that thinks ahead for your business, instead of you chasing insights? That’s exactly what platforms like Vertex AI are making possible. #VertexAI #MachineLearning #AI #GoogleCloud #MLOps #PMLE #ContinuousLearning #Innovation #DataScience #VertexAI

Building scalable AI agents: Design patterns with Agent Engine on Google Cloud: AI Agents are now a reality, moving beyond chatbots to understand intent, collaborate, and execute complex workflows. This leads to increased efficiency, lower costs, and improved customer and employee experiences. This is a key opportunity for System Integrator (SI) Partners to deliver Google Cloud’s advanced AI to more customers. This post details how to build, scale, and manage enterprise-grade agentic systems using Google Cloud AI products to enable SI Partners to offer these transformative solutions to enterprise clients. Enterprise challenges The limitations of traditional, rule-based automation are becoming increasingly apparent in the face of today’s complex business challenges. Its inherent rigidity often leads to protracted approval processes, outdated risk models, and a critical lack of agility, thereby impeding the ability to seize new opportunities and respond effectively to operational demands. Modern enterprises are further compounded by fragmented IT landscapes, characterized by legacy systems and siloed data, which collectively hinder seamless integration and scalable growth. Furthermore, static systems are ill-equipped to adapt instantaneously to market volatility or unforeseen "black swan" events. They also fall short in delivering the personalization and operational optimization required to manage escalating complexity—such as in cybersecurity and resource allocation—at scale. In this dynamic environment, AI agents offer the necessary paradigm shift to overcome these persistent limitations. How SI Partners are solving business challenges with AI agents Let's discuss how SIs are working with Google Cloud to solve some of the discussed business challenges; Deloitte: A major retail client sought to enhance inventory accuracy and streamline reconciliation across its diverse store locations. The client needed various users—Merchants, Supply Chain, Marketing, and Inventory Controls—to interact with inventory data through natural language prompts. This interaction would enable them to check inventory levels, detect anomalies, research reconciliation data, and execute automated actions. Deloitte leveraged Google Cloud AI Agents and Gemini Enterprise  to create a solution that generates insights, identifies discrepancies, and offers actionable recommendations based on inventory data. This solution utilizes Agentic AI to integrate disparate data sources and deliver real-time recommendations, ultimately aiming to foster trust and confidence in the underlying inventory data. Quantiphi: To improve customer experience and optimize sales operations, a… https://lnkd.in/dndypaQv 🔗 Google IA #AI #GoogleCloud #Automation #DigitalTransformation #EnterpriseAI

Microsoft Copilot is evolving into a Multi-Model AI Hub Big update from Microsoft — Copilot is no longer limited to OpenAI models. Starting now, enterprises can tap into Anthropic’s Claude models alongside OpenAI’s GPT. 🔎 What this means in practice: When you open Word, Excel, Outlook, Teams, or PowerPoint, Copilot will let you choose which AI model powers your task. For creative writing, brainstorming, or natural conversation, you might pick OpenAI GPT-4 / GPT-4 Turbo. For deep reasoning, structured problem-solving, coding support, or safer enterprise workflows, you might switch to Anthropic Claude Opus 4.1 or Sonnet 4. Behind the scenes, Microsoft routes your request through Azure, ensuring data stays secure, compliant, and enterprise-ready. The response flows right back into your familiar Microsoft 365 apps. ⚡ Why this is a big shift: 1. Choice & Flexibility → No longer locked into a single AI provider. Organizations can match the right model to the right job. 2. Enterprise Safety → Anthropic emphasizes guardrails and structured reasoning, which complements OpenAI’s creativity. 3. Strategic Diversification → Microsoft reduces dependency on one partner, while expanding the Copilot ecosystem. 4. Future-Proofing AI Workflows → As more models get added, Copilot becomes a “one-stop AI interface” for business users. 📊 Example Use Cases: Draft a complex policy report in Word → powered by Claude Opus 4.1. Summarize long email chains in Outlook → handled by Claude Sonnet 4. Build creative marketing copy in PowerPoint → generated with GPT-4 Turbo. Automate data formatting in Excel → whichever model fits best, chosen dynamically. 🔮 The Bigger Picture This move signals a gradual “unbundling” of Microsoft from OpenAI, positioning Copilot as a neutral AI orchestrator that enterprises can trust for choice, safety, and scale. 👉 The enterprise AI future isn’t about one model winning — it’s about giving users the ability to choose the right tool for the right task. OpenAI Microsoft Anthropic #Microsoft #Copilot #Anthropic #Claude #OpenAI #Azure #FutureOfWork #EnterpriseAI #AIIntegration

How deeply do you think AI can understand the complex IT industry? Context is king when it comes to AI So as we build the first AI platform that's purpose-built for tech industry we need to teach it this rich and complex context. This week we are doubling down on giving Brand Stori a very deep understanding of IT Industry So it understands every little nuance of situations: - System integrator vs Mid-tier situation - cloud deal vs data & AI situation - client content vs partner content - content to support a deal at existing account vs net new It should also understand - What matters to enterprise buyers? - Why now? - Who are you competing with? - Why you vs the incumbent? It's like a knowledge graph of how a top performer thinks. This is the only way we can finally get content that understands the industry, the situation and the way a brand needs to position.. Next step is to layer the complex relationship of services ( like cloud, data & AI, Digital etc.) with brand-specific credibility ( Analyst rating, success story, scale, certification)..

🚪 Google just opened a new front door for AI at work Gemini Enterprise isn’t another assistant or chat tool, it’s Google’s move to make every workflow AI native. Until now, you used Gemini in pockets, Docs, Gmail, Slides. Now it becomes the entry point for your company’s data, apps, and people to work with AI in one secure space. 💡 What’s different • Unified access: Chat with all your company’s documents, data, and systems from one place • Built-in agent builder: Create and deploy custom or pre-built AI agents without writing code • Context aware: Grounded in your company’s data and your personal role, so outputs are relevant, not generic • Full-stack integration: Powered by Google’s entire AI stack, from Gemini 2.5 Pro to TPU Ironwood infrastructure 🏢 Why it matters This moves enterprise AI from “tools for a few” to “infrastructure for all.” It’s how Google is turning every employee into an AI-connected node, not just giving them chat access, but giving them system access through conversation. 🔥 The takeaway Gemini Enterprise is Google’s bid to own the enterprise AI interface layer. If your company runs on Google Workspace or Cloud, this isn’t optional, it’s your new operating layer. 💡 Google just made AI the new operating system for work. How ready is your org to plug in? #AI #EnterpriseAI #GoogleCloud #GeminiEnterprise #DigitalTransformation

Google just announced Gemini Enterprise as "the new front door for AI in the workplace" and honestly, this feels like déjà vu. After deploying AI systems across 60+ companies this year, I've seen this pattern before. Every major vendor promises their platform will be the singular solution that puts an AI agent on every desk. Here's what they won't tell you: the companies succeeding with AI aren't waiting for these all-in-one platforms. They're building with proven tools like Claude API, OpenAI, and Make.com right now. One manufacturing client saved 40% on customer service costs using a simple Claude integration we built in three weeks. Another retail company automated their entire inventory forecasting pipeline with existing tools while their competitors waited for the "perfect" enterprise solution. The uncomfortable truth is that most enterprise AI platforms become expensive science projects. The companies actually shipping value are using modular approaches with GDPR-compliant implementations that work today, not tomorrow's marketing promises. When Google says "agentic platform," I hear vendor lock-in and integration headaches. Real question: are you building with tools that exist now, or waiting for the next enterprise platform promise? The data from our deployments shows speed to value beats feature completeness every time. #AI #Enterprise #Automation https://lnkd.in/eE7ZznpC