Thinkfree Drive: Secure Self-Hosted Cloud Storage with Integrated Online Office
Businesses today are drowning in sensitive data while facing constant security threats, complex regulations, and inefficient data management. While public cloud solutions may seem convenient, they often come with steep costs, reduced flexibility, and privacy concerns. This results in a lack of control and insight required to effectively manage their data. Consequently, many enterprises are considering a shift toward private or hybrid cloud environments to regain control and optimize their IT strategies. A recent Broadcom report revealed that 69% of enterprises are considering repatriating workloads from public cloud to private cloud, with over one-third (35%) already having done so. This is where Thinkfree Drive comes in. By combining a self-hosted cloud storage with a fully integrated online office suite, it gives you complete ownership of your data while enabling your teams to collaborate securely and in real time. Solution Overview Thinkfree Drive addresses key enterprise challenges by delivering a secure, flexible, and fully featured platform. Integrated Cloud Storage and Online Office Suite Thinkfree Drive combines secure, self-hosted cloud storage with a fully integrated online office suite, providing a unified platform for data management and collaboration. Users can store, access, and share files safely while simultaneously creating, editing, and collaborating on documents, spreadsheets, and presentations in real time. Granular File Permissions for Complete Control and Security Provides file-level permissions for six key actions—viewing, editing, downloading, uploading, deleting, and sharing. This gives you complete control, strengthens data security, and streamlines team collaboration. Automatic File Versioning Thinkfree Drive automatically records previous file versions, allowing you to restore data from any point in time with ease. Seamless Sharing & Collaboration You can share files and folders across your organization, empowering teams to work together smoothly and securely. Comprehensive File Format Support Thinkfree Drive provides support for a wide range of file types, including Office documents such as OOXML and ODF, PDF, images, and text files. You can preview all these files instantly, and edit OOXML and ODF documents without the need for additional software. Effortless Installation With a simple, Docker-based installation, you can deploy both cloud storage and an online office suite quickly and simply. How Businesses Can Leverage Thinkfree Drive Thinkfree Drive is designed for organizations that prioritize data protection and collaboration. Here’s how it helps key sectors. Healthcare Healthcare organizations are under constant pressure to protect patient privacy and comply with strict regulations like HIPAA. Within a private cloud, Thinkfree Drive ensures patient records, medical images, and research data are stored with the highest level of security. The Solution Store sensitive medical data in a private cloud with data encryption and detailed audit logs. The Outcome Teams collaborate on research reports and clinical documents securely, allowing staff to focus on patient care rather than data compliance. Financial Services Financial institutions face the critical risk of data breaches and are required to maintain strict security standards. Thinkfree Drive provides a secure environment to manage sensitive customer account information, investment reports, and contracts. The Solution Our granular permission management allows you to strictly control who can access and edit reports in real time. Centralized management and containerized deployment streamline IT operations while maintaining the highest security standards. The Outcome Your teams can work on critical documents with full confidence that every piece of data is protected and every change is meticulously tracked. Government & Public Sector Government agencies must secure confidential policy documents, project reports, and budget data without relying on external cloud providers. Thinkfree Drive eliminates this dependency by keeping all data and operations within your own secure, on-premise environment. The Solution Fine-grained access controls and comprehensive audit logs ensure secure operations and full transparency. The integrated online office tools enable seamless, real-time collaboration across departments. The Outcome You can protect sensitive information and streamline collaboration while ensuring compliance and improving public service efficiency. Boost Productivity and Security with Thinkfree Drive Thinkfree Drive goes beyond simple cloud storage. It combines self-hosted data control, enterprise-grade security, and integrated productivity tools into one solution, helping organizations across industries work smarter while keeping sensitive information safe. If your business is ready to reduce dependency on external cloud platforms and take back control of its data, We are here to help you. Request Demo Today Explore more about Thinkfree Drive Like this post? Share with others! Recent Articles
Data Sovereignty Fuels Microsoft and Google Alternatives
Microsoft 365 and Google Workspace have been widely used for years, firmly establishing themselves in the office software landscape. They offer integrated tools for document editing, file storage, sharing, and team collaboration—all accessible through a web browser. This convenience has made them a go-to choice for many individuals and organizations. But across Europe, that long-standing reliance is starting to shift. Governments and enterprises in countries like Germany and France are actively working to reduce their dependence on these platforms. This shift isn’t just about choosing alternative software—it reflects a deeper, structural concern: data sovereignty. What Is Data Sovereignty and Why Does It Matter? Data sovereignty is the principle that individuals and organizations should retain full control over their data, including where it’s stored, who can access it, and how it’s processed. It’s not just a privacy issue. It’s tied to national security, industrial competitiveness, and operational stability. Key Risks of Relying on Foreign Cloud Platforms Legal ConflictsUnder laws such as the U.S. CLOUD Act and the Patriot Act, American companies like Microsoft and Google are legally required to hand over data stored abroad if requested by U.S. authorities. This creates a legal gray area where data physically stored in Europe can still fall under U.S. jurisdiction, potentially violating GDPR and triggering lawsuits, fines, and customer loss. Operational DisruptionForeign cloud reliance comes with service interruption risks. Legal disputes, geopolitical tensions, or technical outages can result in sudden shutdowns. For public institutions or sensitive industries, this could cause major societal or financial disruptions. High Switching CostsMigrating from one cloud provider to another isn’t simple. It involves format conversion, system rebuilding, and staff retraining. These costs can lock organizations into a single vendor, even if service quality declines or pricing rises. Loss of Competitive EdgeWhen sensitive customer or business data resides on foreign servers, organizations have less freedom to analyze, adapt, or build services around it. In contrast, companies with full control over their data can innovate faster and expand smarter. Google’s Data Sovereignty Response: Efforts and Limitations To address growing concerns over data sovereignty, Google launched its Sovereign Controls for Google Workspace initiative in 2023. This strategy includes processing data strictly within the EU, customer-managed encryption keys, and localized EU-based support teams. In 2024, the company expanded data center investments in France and Germany, and introduced “Assured Controls” to block data transfers outside specific regions. While these moves reflect an acknowledgment of sovereignty demands, they have critical limitations. Google remains a U.S.-based company and is still subject to the CLOUD Act, which gives the U.S. government legal authority to request access to data stored abroad. This raises concerns about whether Google can ever truly meet European data sovereignty requirements. Even with customer-controlled encryption keys, the infrastructure managing those keys operates within Google’s cloud environment — meaning customers don’t have full operational autonomy. Despite claims of GDPR alignment, several EU privacy experts and regulatory bodies argue that Google’s changes offer only partial compliance and do not fully support sovereign data control. (Refer to the Google Workspace Blog, Sovereign Controls GA-2023) Microsoft’s Data Sovereignty Response and Its Challenges Microsoft introduced the EU Data Boundary initiative in 2022, outlining its plan to store and process all European customer data for Microsoft 365, Azure, and Dynamics 365 within EU borders. The initiative, aimed at addressing data sovereignty concerns, is expected to be fully implemented by 2025. The company is also investing in additional EU-based data centers. However, Microsoft also remains subject to U.S. jurisdiction and is bound by laws such as the CLOUD Act and National Security Letters (NSLs). These laws can override localized data handling policies, undermining the core promise of data sovereignty compliance. Moreover, the infrastructure that supports EU data storage and processing may still rely on global Microsoft teams for maintenance and operations. This introduces potential exposure to cross-border risks, even if the data is physically kept in Europe. For many privacy advocates and regulators, Microsoft’s sovereignty strategy is seen as a step forward in optics, but not a comprehensive solution. (Refer to the Microsoft Blog – EU Data Boundary for the Microsoft Cloud) The EU’s Own Cloud Projects: GAIA-X and OpenDesk As private companies and public institutions explore alternatives to reduce dependence on foreign cloud services, European governments are launching structural initiatives to reclaim data sovereignty. Persistent concerns that foreign-operated clouds leave European data vulnerable to external government control have driven this shift toward domestic control over data storage, processing, and protection through projects like GAIA-X and OpenDesk. GAIA-X A cross-border cloud project led by Germany and France, aiming to build a federated data infrastructure with common standards. Companies like Siemens, SAP, and Deutsche Telekom are participating, with a focus on enabling secure, internal data sharing across industries. OpenDesk A French government project to provide self-hosted collaboration tools — including email, calendars, and document editing — using open-source platforms. Some ministries have already begun trial deployments. A Sovereignty-First Alternative: High-Control Collaboration with Office Integration As leading vendors like Google and Microsoft continue to roll out technical measures to address data sovereignty, structural limitations remain. This is precisely why some public institutions and enterprises in Europe are reluctant to place full trust in these platforms — and are actively searching for alternatives. But these alternatives go beyond simply avoiding foreign-owned platforms. The real goal is to establish a collaboration environment where data can be directly controlled on the organization’s own infrastructure, without sacrificing user experience. This has led to growing interest in combining collaboration platforms and office software that offer strong autonomy and security. Are Open-Source Office Tools a Viable Alternative? Open-source office solutions are often viewed as a strong choice for organizations that prioritize data sovereignty. Their self-hosted structure gives organizations full control over data storage, access, and usage without being subject to foreign jurisdiction or third-party cloud policies. Some tools also support real-time editing through web browsers without relying on external cloud services, and can be restricted to internal networks to maximize security. In theory, they offer
Thinkfree Partnership with Nextcloud, world’s most popular privacy-focused Collaboration Platform
We are proud to announce our partnership with Nextcloud, world’s most popular privacy-focused collaboration platform. Headquartered in Germany, Nextcloud develops and provides software for online collaboration and communication, including file storage, chat and video conferencing, email, contacts, and calendar solutions. Its solutions are widely adopted by tens of thousands of private companies, public institutions, and government agencies globally, and are used by tens of millions of users worldwide. Recently, driven by the growing trend in Europe to reduce reliance on U.S. big tech cloud services and to strengthen digital sovereignty, demand for Nextcloud solutions has been rapidly increasing across Europe and other regions such as the U.S., Canada, Asia, and Latin America. As a result, Nextcloud’s demand in the first half of 2025 has grown more than threefold compared to the previous year, solidifying its position as a strong alternative to Microsoft and Google. Through this partnership, Nextcloud users can seamlessly view, edit, and collaborate on office documents using Thinkfree’s online office suite, which is fully integrated with Nextcloud’s collaboration platform and highly compatible with Microsoft Office formats. Thinkfree Office is available as a connector app in the Nextcloud app store, making it easy for users to get started. Deekay Kim, CEO of Thinkfree, said, “Nextcloud is a global leader in collaboration platforms, trusted by customers across Europe and beyond. Through this partnership, we look forward to helping businesses enhance productivity in their digital environments while protecting their digital sovereignty together with Nextcloud.” He added, “Backed by over 15 years of office software development expertise, Thinkfree offers high compatibility with Microsoft Office formats and features a powerful proprietary document engine that enables fast document rendering and processing. This allows Nextcloud users to enjoy a seamless and efficient document editing and collaboration experience.” “We’re proud to welcome Thinkfree Office to our growing ecosystem”, said Frank Karlitschek, founder and CEO of Nextcloud. “Together with Thinkfree Office, we’re expanding choice and flexibility for users who demand control without compromise.” See how Thinkfree Office works 👉 Thinkfree Office for Nextcloud Also Check out from 👉 Nextcloud blog Like this post? Share with others! Recent Articles
AI for Customer Support: Case Studies and Results for SMEs
When it comes to resolving customer issues, what’s more important: speed or accuracy? In truth, both are equally crucial. Customer service directly impacts revenue, reputation, and customer loyalty. Yet many CS teams struggle with overwhelming inquiry volumes, fragmented systems, and limited staffing. These challenges slow down response times and reduce service quality. The core issue is inefficient access to information. Agents spend valuable time switching between tools to find what they need. In high-turnover environments, the lack of experienced staff adds even more friction. Why is AI essential for customer service teams, and how can it help? Enterprise AI Search gives agents instant access to accurate, cross-system data. It improves speed, enhances service quality, and enables teams to focus on solving meaningful problems. In this post, we explore how AI is reshaping customer service through real enterprise use cases. Enterprise Case: How AI for Customer Support Boosted Performance Vodafone: Improving Support Efficiency with AI Search Challenge: Vodafone’s customer service center faced a major bottleneck due to fragmented data across multiple systems. By leveraging AI for customer service, Vodafone aimed to streamline data access and improve overall support efficiency. Customer contract information, pricing plans, and technical support data were all stored separately, making it difficult and time-consuming for agents to retrieve what they needed. This led to longer wait times and reduced operational efficiency. AI Adoption: Vodafone addressed this by implementing IBM Watson Assistant to develop and integrate the chatbot into its agent support system. AI analyzes customer queries in real time and delivers relevant information, such as contract details and pricing options. Directly to agents. By performing unified searches across multiple databases, AI presents the necessary data through a single interface, eliminating the need to switch between systems. Key Result of AI adoption: Customer satisfaction increased by 50% with GenAI-enhanced AI interactions Net Promoter Score (NPS) improved by 20% compared to the previous AI system First-time resolution rate increased from 15% to 60% Shorter customer wait times and improved service quality AI enabled agents to access information quickly and efficiently, reducing call durations and freeing them to focus on tasks that required human judgment and problem-solving. Rather than replacing agents, AI functioned as a real-time assistant that enhanced productivity and decision-making.(Vodafone to boost TOBi with GenAI, The Mobile Network 2024) Lufthansa: Reducing Wait Times through AI Search Challenge: Lufthansa’s customer service center, Lufthansa InTouch, faced mounting operational pressure due to high volumes of inquiries related to booking changes, refund requests, and flight details. Agents had to manually retrieve data from various systems — including reservation records, account details, and flight schedules — leading to delays and inefficiencies. AI Adoption: Lufthansa adopted the AI platform to implement a conversational AI system, which was integrated into the agent support workflow. This AI solution analyzed customer queries in real time and retrieved relevant data, such as booking information, flight schedules, and refund policies, from multiple backend systems (e.g., CRM, reservation platforms). It also suggested optimal next steps, like proposing alternative flights or guiding refund procedures, to support faster decision-making. Key Result of AI Adoption: Reduced customer wait times and improved service quality. AI agents support 16 million customer interactions per year, with peaks up to 375,000 per day. The AI streamlined access to complex, distributed data, helping agents resolve inquiries faster and more accurately. By automating repetitive lookups and offering context-aware guidance, it allowed agents to focus on more nuanced customer interactions such as explaining refund policies or negotiating travel changes. The implementation also led to a significantly lower Average Handling Time (AHT), allowing agents to resolve inquiries more efficiently. It served as a reliable productivity partner rather than a replacement.(16+ Million AI Conversations Yearly With Self-Service AI Agent) How AI Boosted CS Team Performance in SMEs Avetta: Shortening Call Handling Time with AI Search Challenge: Avetta’s customer service team faced operational inefficiencies caused by data silos across multiple systems. Information related to supply chain contractors, compliance documentation, and past customer inquiries was scattered, making it time-consuming for agents to locate and process the data. This led to longer customer wait times and prevented agents from focusing on complex support requests. AI Adoption: To address these issues, Avetta integrated a conversational AI solution to support its customer service team. The AI analyzed customer inquiries and retrieved relevant information — such as contractor certification status, compliance documents, and historical tickets — in real time, displaying it through a unified interface. AI-generated videos were also used to streamline agent onboarding and training processes, helping reduce data handling time. Additionally, the AI provided real-time guidance to agents, offering suggestions such as how to navigate compliance procedures. Key Result of AI adoption: Reduced average call handling time by 16 seconds Increased onboarding efficiency by 20% Increased agent retention by 8% AI eliminated the need for agents to manually navigate fragmented data systems, allowing them to access key information quickly and efficiently. This significantly shortened handling times and enabled agents to devote more attention to complex issues, such as contractor compliance resolution. AI-powered video training further enhanced agents’ ability to process data effectively. By automating repetitive tasks, the AI served as a productivity partner that elevated both decision-making and customer engagement quality. (Synthesia Case Study — How Avetta is using AI video to boost the productivity of 150 support agents 2024) AI Is Vital for SMEs and CS Outsourcers Too We’ve seen how companies of all sizes are using AI to transform their customer service operations. But in many ways, AI can make an even bigger impact for smaller teams, where every second counts and resources are limited. For SMEs and outsourcing providers, faster response times and higher accuracy are not just goals. They are essential to business success. Want practical insights into how SME support teams use AI? 🔗 See how customer support teams use AI in practice 🔗Explore ready-to-use 12 AI prompts for support teams Key Considerations for AI Adoption in SMEs AI has proven to be a powerful enabler for SMEs and customer service outsourcing teams, driving both efficiency and quality in customer service operations. As
Hosted API vs Self-Hosted: Best Deployment Option
Choosing between a self-hosted and hosted API model is more than a technical decision. It’s a strategic call that affects your scalability, security, time-to-market, and total cost of ownership (TCO). In this guide, we’ll walk you through the essential differences, real-world scenarios, and decision frameworks to help you find the best fit for your organization. What Are Hosted APIs and Self-Hosted? Hosted API A service operated and maintained by a third-party provider. You get immediate access via API keys and interact with it over the internet. Since there’s no need for local installation or infrastructure management, hosted APIs enable fast integration and rapid deployment. Think Stripe (for payments), Twilio (for messaging), or OpenAI (for AI models), which let you use powerful features without setting up anything yourself. These APIs often come with automatic updates, global performance optimization, and flexible pricing models that make them ideal for agile development and operational efficiency. Self-hosted You install and run the software on your own infrastructure, giving you full control over deployment, configuration, and data management. Unlike cloud-based services, self-hosted systems allow organizations to maintain complete visibility into how their applications operate. This approach is often preferred by teams that need to meet strict compliance standards or require deep customization at the system level. For example, solutions like Nextcloud and Mattermost offer self-hosted deployment options, enabling document collaboration or team communication entirely within private environments. Hosted API vs Self-Hosted: A Feature Comparison When to Choose a Hosted API Hosted APIs are best for fast-moving teams that want simplicity and minimal maintenance. They’re ideal for SaaS products, MVPs, or teams with limited infrastructure capacity. Choose a hosted API when: Speed and fast iteration are critical Dedicated infrastructure resources are limited Usage is unpredictable and benefits from flexible pricing Security and compliance can be managed by the provider Real-World Case: Why a SaaS Platform Chose Hosted API Integration A growing SaaS collaboration platform needed to offer in-app document editing to support seamless workflows for its users. While real-time collaboration was essential, the team lacked the infrastructure and resources to build and maintain an office suite internally. Running their own deployment would have added operational overhead and distracted from core product development. Instead, they chose to integrate a cloud-based office suite using a hosted API. This approach enabled users to open, edit, and collaborate on documents directly within the platform — no downloads or tool switching required. It improved engagement and collaboration speed, all without increasing infrastructure complexity. When to Choose a Self-Hosted Deployment Self-Hosted deployment is a better fit for organizations that need full control over their systems. It suits environments where compliance, data ownership, and integration flexibility are essential. Choose Self-Hosted Deployment when: Data control and internal security policies are critical Complex workflows or deep system integrations are needed You already operate private infrastructure (on-prem or BYOC) You want to avoid long-term vendor lock-in Real-World Case: Why Software Company Chose Self-Hosted Deployment A global 3D design software company needed to keep all document editing within its own infrastructure to maintain strict control over sensitive data. This was essential because key documents such as BOMs, test reports, and assembly manuals were often edited through third-party tools, which increased the risk of data leakage and made it difficult to comply with internal security standards. To address these risks and enable seamless collaboration, the company adopted a self-hosted deployment of Thinkfree Office. This allowed teams to edit documents securely and efficiently within the platform without relying on external software. 👉 Read the full story TCO Comparison: Hosted vs Self-Hosted This cost comparison reflects the total cost of ownership (TCO) for each model. It goes beyond upfront costs and includes long-term expenses such as infrastructure, maintenance, staffing, downtime, scaling, and compliance. Why TCO Varies TCO isn’t one-size-fits-all. It varies based on: Company size & structure:Startups may prefer hosted models to avoid the burden of infrastructure, while larger enterprises can absorb internal operations more efficiently. Security & compliance needs: Stricter data protection or regulatory requirements may push organizations toward self-hosted deployment, increasing internal costs for compliance and control. Deployment scale: Supporting 10 users is very different from supporting 10,000. Larger-scale deployments typically require more infrastructure and IT management when self-hosted. Usage model: High usage volume can make usage-based pricing less cost-effective over time, making self-hosted deployments more economical at scale. Cost Breakdown Table API Deployment Decision Checklist Ask the following: Do we need full control over data and infrastructure? Are we bound by regulations that restrict third-party services? Do we have internal engineering capacity to manage hosting? Is our usage expected to scale rapidly in volume or complexity? Advantages of Hosted(Cloud) API Cloud-based (Hosted) APIs offer distinct benefits that go beyond quick setup: Elastic scaling: Instantly handle traffic spikes without hardware planning. Automatic updates & security patches: Zero downtime releases handled by the provider. Global edge performance: Leverage CDN and regional data centers for lower latency. Lower upfront capital expenditure: Pay for usage instead of servers. Advantages of Self-Hosted Deployment Self-Hosted deployments provide full control and are ideal for organizations with strict requirements: Complete data ownership: All data remains within your infrastructure. Deep customization: Configure the system to match your internal workflows and policies. No vendor lock-in: Full independence from third-party service constraints. Better alignment with compliance: Meet strict regulatory standards such as HIPAA or GDPR. Real-world examples: Nextcloud (document collaboration), Mattermost (team communication), Collabora Online (office suite) Thinkfree supports both Hosted and Self-Hosted API deployment models Thinkfree supports both Hosted API and Self-Hosted deployment models offering flexibility for document collaboration. SaaS platforms integrating Thinkfree’s Hosted API can enable real-time document editing with minimal setup. It requires no local installation, follows a usage-based billing model, and returns all edited documents securely to the client’s infrastructure. No files are stored on Thinkfree servers, making it a simple yet secure option for fast integration. For organizations that need stricter control over their data and systems, Thinkfree offers a Self-Hosted deployment model. This includes on-premises or BYOC (Bring Your Own Cloud) options, providing full data ownership, system-level customization, and alignment with internal IT policies and
Top Enterprise AI Search Solutions in 2025
Traditional search tools fall short as enterprise data grows across clouds, apps, and repositories. Employees waste hours finding information, decisions stall, and knowledge fragments. Enterprise AI search solutions solve this by blending semantic understanding, real-time indexing, and generative AI to deliver fast, secure, and contextual answers. This guide evaluates 10 leading solutions in 2025, offering IT leaders, CIOs, and knowledge managers a roadmap to choose the right platform while future-proofing their search strategy. If you’re not quite sure why Enterprise AI Search is necessary, this article breaks it down clearly 👉 Why Business Seek Enterprise Ai Search Why Enterprise AI Search Solutions Matters in 2025 According to Gartner’s Survey, 65% of organizational decisions today are more complex than they were just two years ago, requiring more stakeholders, more variables, and faster responses. Gartner emphasizes that in this increasingly dynamic environment, businesses must enhance their ability to make optimal decisions quickly, consistently, and with minimal risk. To meet these demands, organizations are turning to AI and data analytics to accelerate insight and action. Enterprise AI search platforms support this shift by connecting fragmented information across tools, enabling hybrid teams to work more efficiently, and improving operational resilience. When evaluating these platforms, key capabilities to look for include: Semantic understanding and contextual relevance Generative AI for summaries and direct answers Real-time indexing with permission-aware access controls Seamless integration with enterprise systems Deployment flexibility (cloud, on-premises, or hybrid) Enterprise-grade security, compliance, and logging Total cost of ownership and measurable ROI Top 10 Enterprise AI Search Solutions 1. Glean Effective for: Organizations with 1,000+ users seeking fast deployment and unified enterprise search Integrated Tools: Over 100 workplace tools, including Slack, Salesforce, Google Workspace, Microsoft 365, Jira, ServiceNow, Dropbox, GitHub, Notion, Confluence, and others via prebuilt connectors or APIs. Industry Focus: General enterprise use across hybrid workforces and departments AI Highlights: Real-time indexing with personalized results via a knowledge graph. Generative AI provides document summaries and email responses, while strict access controls support enterprise security. Key Features Glean connects over 100 business tools through prebuilt connectors and a simple admin interface, allowing integration via OAuth or API keys mostly no-code, though custom integrations may require light API configuration. Its knowledge graph ensures results are tailored by identity and behavior, and real-time indexing keeps content up to date. Strength Known for fast deployment and strong personalization. SSO and SCIM support enable enterprise-grade identity management, and minimal IT overhead makes onboarding quick. Trade-offs Advanced query rewriting for highly specialized taxonomies is limited. Pricing may be a concern for teams with fewer than 500 users. 2. Refinder AI Effective for: Individual users, small teams, startups, SMEs, and organizations seeking fast onboarding and simple operations. Integrated Tools: Google Drive, Gmail, Notion, Figma, Jira, Confluence, and more can be easily connected via a click-based interface. Most apps can be integrated without any coding. Industry Focus: Professionals and organizations that need unified access to internal data across tools, including real estate, customer service, legal services, research, freelancing, and companies that handle document-heavy workflows. AI Highlights: RAG-based AI search with flexible deployment options — hosted or on-prem. Designed with security, ease of onboarding, and operational simplicity in mind. Key Features Refinder offers unified search powered by retrieval-augmented generation (RAG). It connects to work tools via click-based, no-code integrations, and provides secure permission management through an admin dashboard. Although designed for enterprise use, it is accessible to small teams and individuals, who can easily connect their own tools to explore the solution in real time. Strength Teams can complete setup within just a few hours. With security built into every layer, the solution is well suited for teams that manage sensitive or regulated information. The intuitive user experience makes it easy to adopt across different industries. For startups, tailored benefits are available through a dedicated startup support programs including free access for a limited period, expert consultations, and other customized support. Trade-offs Currently limited in scalability for large enterprises with thousands of users, as it is optimized for small to mid-sized teams, though Refinder is actively preparing for enterprise-grade scalability. 3. Guru Effective for: Mid-sized teams (50–500 users) looking for lightweight knowledge sharing and contextual assistance Integrated Tools: Slack, Microsoft Teams, Salesforce, Zendesk, Google Drive, Confluence, Jira, ServiceNow, and others Industry Focus: Mid-sized teams managing shared knowledge across departments AI Highlights: Contextual search with browser and chat integration. AI verifies and surfaces knowledge where teams work. Key Features Guru serves as an intelligent knowledge layer embedded in your daily communication tools like Slack and Microsoft Teams. Instead of requiring users to search external knowledge bases, Guru proactively delivers relevant answers directly within the flow of conversations. Its AI suggests content based on context, while built-in verification workflows help teams ensure the accuracy and freshness of shared knowledge. Strength Fast rollout, intuitive UX, and productivity-focused search integrations. Teams can deploy within a week. Trade-offs Limited support for structured metadata or large enterprise scaling. Custom data modeling not supported. 4. Lucidwork Fusion Effective for: Regulated organizations requiring scalable, multilingual, and secure search infrastructure Integrated Tools: Salesforce, ServiceNow, SharePoint, SAP, Google Drive, Box, and others Industry Focus: Healthcare, finance, and other regulated sectors AI Highlights: Built on Apache Solr with support for 50+ languages. Customizable ingestion pipelines and robust security controls. Key Features Fusion leverages Apache Solr for high-volume, multilingual search, supporting complex datasets across cloud and on-premises environments. Customizable ingestion pipelines allow tailored data processing, while granular access controls ensure compliance with regulations like GDPR or HIPAA (source: Lucidworks official website). AI-driven semantic search improves result relevance, and real-time indexing keeps data current. The platform supports hybrid deployments, offering flexibility for regulated industries. Strength Robust compliance features and multilingual support make it fit for global, regulated enterprises. Highly customizable pipelines allow precise control over search behavior. Scalability suits high-volume environments. Trade-offs Requires dev resources and long implementation time (6–8 weeks). Higher TCO for smaller teams. Smaller teams may find the setup complexity and cost prohibitive. 5. Elastic Search Effective for: Developer-heavy teams building custom internal or product-integrated search applications Integrated Tools: Slack, Jira, Confluence, Salesforce, ServiceNow, Google Drive, Microsoft 365, GitHub, Dropbox, Zendesk, SharePoint, and others Industry Focus: Technical teams embedding scalable internal
12 Essential AI Prompts for E-commerce Customer Support Teams
Customer support has become increasingly complex and challenging due to the expansion of real-time inquiry channels such as email, live chat, and messenger apps and the global reach of modern businesses. Customers now expect instant, accurate, and personalized support, with inquiries coming in 24/7 and in multiple languages. Fortunately, AI innovations like ChatGPT and modern assistant tools are paving the way for smarter, more scalable support. These tools have the potential to significantly reduce the workload on support teams by automating responses and improving service quality.See related article However, just using AI tools doesn’t mean you’ll see results. Without the right approach, even the most powerful tools may fall short. In particular, the accuracy and effectiveness of AI responses largely depend on how prompts are written. Well-crafted prompts tailored to real-world business scenarios can make all the difference in delivering exceptional customer support. In this article, we will explore practical, ready-to-use AI prompts designed to help e-commerce teams improve their customer service. What is an AI Prompt and How It Works? An AI prompt is the input or instruction given to a generative AI model like ChatGPT to guide its response. In simpler terms, it’s the way you “ask” the AI to do something. This can range from a question or a sentence to a detailed scenario or task description. For example, if you type:“Write a friendly reply to a customer asking about delayed shipping,”the AI will generate a response based on that instruction, drawing from patterns it has learned from vast amounts of training data. The way a prompt is written significantly affects the output. A clear, specific prompt leads to more accurate, relevant, and useful responses. Vague or overly broad prompts can result in generic or off-target answers. In the context of customer support, prompts can be tailored to match real-life service situations — such as handling complaints, explaining return policies, or providing product recommendations. When prompts are tailored to real business scenarios, AI becomes a dependable partner for improving service quality and operational efficiency. AI Prompts for E-commerce Customer Support 1. Handling Shipping Delays Customers often worry when their orders are late. Use this prompt to generate a helpful and empathetic reply: Prompt:“The customer is expressing frustration about a shipping delay. Write a polite and empathetic response that includes the shipping status and expected delivery date. Customer message: ‘[Customer message]’” 2. Managing Return and Exchange Requests Returns and exchanges are very common in online shopping. Here’s how to guide your AI to handle them effectively: Prompt:“The customer is requesting a return or exchange. Write a helpful message that explains the process clearly and includes any important policies. Customer message: ‘[Customer message]’” 3. Resolving Payment Issues Payment errors can frustrate customers and cause order abandonment. Use this prompt to offer support calmly and clearly: Prompt:“The customer encountered a payment issue. Provide a clear and friendly explanation along with step-by-step troubleshooting. Customer message: ‘[Customer message]’” 4. Order Cancellation Sometimes customers need to cancel an order. This prompt helps your AI guide them through the process: Prompt:“The customer wants to cancel their recent order. Write a response that explains the cancellation policy. Customer message: ‘[Customer message]’” 5. Answering Product Questions When customers ask about product details, accuracy matters. Use this prompt to deliver concise and helpful responses: Prompt:“The customer is asking about product features. Write a clear, friendly, and informative reply. Customer message: ‘[Customer message]’” 6. Responding to Reviews AI can help you respond to both good and bad reviews quickly and professionally. Prompt:“The customer left a review. If it’s positive, thank them and invite them back. If it’s negative, acknowledge their concerns and promise improvement. Customer review: ‘[Customer review]’” 7. Promotions and Discounts E-commerce shoppers love deals. Here’s a prompt for when they ask about discounts or coupons: Prompt:“The customer is asking about discounts. Respond with current promotions and how to apply coupons. Customer message: ‘[Customer message]’” 8. Back-in-Stock Notifications When popular items sell out, shoppers often ask if they’ll return. This prompt helps you respond and even suggest alternatives: Prompt:“The customer asked if a sold-out item will be restocked. Provide availability information and recommend similar products if needed. Customer message: ‘[Customer message]’” 9. Back-in-Stock Notifications Sometimes the AI needs additional details to resolve an issue. Use this prompt to ask the customer for clarification politely: Prompt:“The customer asked a question, but more information is needed to provide a complete answer. Write a polite response asking for the necessary details. Customer message: ‘[Customer message]’” 10. Escalating to a Human Agent Some issues require personal attention from a human agent. Use this prompt to transfer the case smoothly: Prompt:“The customer has a complex or sensitive issue. Write a respectful message explaining that a human agent will follow up shortly. Customer message: ‘[Customer message]’” 11. Handling Multilingual Inquiries Global customers often reach out in different languages. Use this prompt to respond appropriately in their language: Prompt:“The customer wrote in [language]. Write a polite and accurate response in the same language about the status of their order. Customer message: ‘[Customer message]’” 12. Confirming Customer Identity or Order Before sharing sensitive order information, identity confirmation is often needed. Use this prompt to request it professionally: Prompt:“The customer is asking for order details, but we need to verify their identity. Write a professional message asking for their order number or email address. Customer message: ‘[Customer message]’” Final Thoughts Great customer support doesn’t have to be time-consuming. With the right AI prompts, you can deliver consistent, friendly, and fast responses. Start by customizing the prompts above based on your tone, policies, and customer needs. Integrate them into your chatbot, helpdesk, or AI assistant to boost efficiency and improve customer satisfaction. But here’s a bigger question:What if your support team could instantly access every piece of relevant information from your internal systems, without switching tabs or asking other departments? Meet Refinder AI: Enterprise AI Search That Understands Your Business Refinder AI is an AI search and assistant solution for any size of business that connects with
Key Differences Between Generative AI and Enterprise AI
AI tools like ChatGPT have quickly become part of everyday work routines. From drafting documents to summarizing meetings, using AI now feels ordinary rather than revolutionary.As AI becomes more common in the workplace, a persistent challenge remains: many professionals are still unclear about what “AI” actually means in a business setting. It is common to assume that all AI works in the same way or can be used for the same purposes. In reality, technologies such as Generative AI and Enterprise AI Search are built on entirely different foundations. They are designed to solve different type of issues, rely on different data sources, and fit into workflows in very different ways. This article is not about choosing one over the other. Its goal is to provide a clear and practical comparison to help you understand what each type of AI is, what it can and cannot do, and how to evaluate them based on your organization’s needs. What Is Generative AI vs Enterprise AI Search? Generative AI refers to AI systems that create new content — text, code, or images — based on a user prompt. These systems are typically powered by large-scale models like Transformers or GANs, trained on massive public datasets. Enterprise AI Search, on the other hand, focuses on finding and organizing information across internal business systems. It uses AI to understand queries in context and retrieve relevant results from tools like Notion, Slack, or internal wikis. Most are built on Retrieval-Augmented Generation (RAG) architectures, enabling real-time search and synthesis based on internal documents and communication data. Key Differences Between Generative AI and Enterprise AI Search CategoryGenerative AIEnterprise AI SearchPurposeContent creation (text, code, images)Information retrieval and decision supportData SourcesPublic web data + some live searchReal-time data from internal systemsResponse FlowPrompt → Direct output from modelPrompt → Retrieve documents → Generate responseTech StackTransformer, GANNLP, ANN, RAG, Markov Decision ProcessCustomizationLimited (API-based)High (on-prem or private cloud deployments)Security & CompliancePotential data exposureMeets enterprise requirements (e.g., GDPR, ISO)ExamplesChatGPT, Claude, Copilot, Stable DiffusionGlean, Coveo, Elastic, Microsoft AI Search, Refinder Purpose and Use Focus Generative AI is designed for rapid content generation. Tools like ChatGPT (OpenAI), Claude (Anthropic), and GitHub Copilot use pretrained data patterns to generate new text, images, or code based on user inputs. Enterprise AI Search is built to optimize decision-making and internal knowledge management. It connects disparate data sources and enables context-aware, accurate retrieval. Notable examples include Glean (focused on productivity), Coveo (customer experience optimization), Elastic (open-source search), Microsoft Azure AI Search (tightly integrated with Microsoft 365), and Refinder (designed for startups and smaller organizations). How They Use Data Generative AI relies on static training data — books, web content, encyclopedias — collected before deployment. Some tools now include real-time web browsing (e.g., Perplexity, ChatGPT Browse), but most responses still stem from pre-trained knowledge. Enterprise AI Search works with real-time data. It connects directly to systems like CRM, ERP, Slack, and file storage platforms. When a user submits a query, the system retrieves and synthesizes data on the spot. This ensures current, traceable answers with clear sources. How They Work Generative AI predicts the most likely word sequence based on patterns learned during training. For example, for the prompt “The capital of France is,” a model might assign probabilities — Paris (90%), London (5%), Berlin (3%) — and output the most likely result: “Paris.” This approach mimics human language prediction but doesn’t verify facts in real time, which can lead to misinformation or “hallucinations.” Enterprise AI Search combines retrieval and generation. A user submits a query, the system searches internal data, and the model generates a response based on the retrieved context. Using RAG, it ensures that answers are grounded in actual documents, reducing the risk of misinformation. Technology Under the Hood Generative AI uses technologies like Transformers (for language modeling) and GANs (for image generation). These tools are mostly cloud-based, easy to access via API, and widely adopted. However, customization is limited. Companies can’t easily control access levels or restrict data scope based on internal policies. Enterprise AI Search combines NLP, ANN (approximate nearest neighbor search), RAG, and probabilistic reasoning (e.g., Markov Decision Processes). These systems can be deployed on-premise or in private cloud environments and allow granular customization — such as user-level access control, audit logging, or custom search boundaries. Security and Compliance Security is a key concern in enterprise environments. Generative AI typically runs on public cloud infrastructure, meaning organizations have limited control over data storage and access policies. This makes it less suitable for industries with strict compliance needs. Enterprise AI Search offers tighter control. Solutions can be hosted within a company’s infrastructure and include features like encryption, user access control, and detailed audit trails. These capabilities make them more suitable for regulated industries such as finance or healthcare. Understanding Work Context Generative AI doesn’t understand business context. It can process text, but it doesn’t know who created a document, what project it belongs to, or why it exists. This lack of context can result in generic or irrelevant answers. Enterprise AI Search, however, can analyze metadata such as authorship, timestamps, version history, and project affiliations. It can answer questions like “Who created this file?” or “Which project is this document part of?” — making it valuable for tasks like handovers, project onboarding, and compliance reviews. Comparing Use Cases: Generative AI vs. Enterprise AI Search Here’s a question that often gets overlooked: how exactly do these two types of AI differ in how they are used? They may seem similar at a glance, but in practice, they serve very different functions. The differences become especially clear when you look at where each one is actually applied. Generative AI Marketing copywriting Code generation Meeting summaries, brainstorming Text formatting and translation Enterprise AI Search Internal document retrieval Regulatory document tracking Project-specific knowledge support Customer inquiry tracking and response automation Which AI Is Right for Your Business? With so many AI options available, choosing the right solution has a direct impact on your organization’s productivity and performance. Generative AI and Enterprise AI Search differ significantly in architecture, functionality, security, and customization. Whether your team needs
Is ChatGPT Enough? Why Businesses Seek Enterprise AI Search
“Draft an email.” “Summarize a meeting.” “Organize notes.” We live in an era where professionals type commands at their desks rather than ask colleagues. Generative AI like ChatGPT, Perplexity, and Grok have shifted communication from people to machines, replacing Google searches and even offering near-expert-level responses. That’s not all. With just a simple prompt, Generative AI can extract insights, tailor content to different audiences, and draft strategic plans in seconds. You might think, “If it can do all this, can one person handle a hundred tasks?” And are you actually handling the work of a hundred people now? Despite Using ChatGPT, We Still Spend 2.5 Hours a Day Searching According to IDC, office workers spend 2.5 hours a day searching for information, which adds up to nearly 30% of their workday and 650 hours a year. The reason is clear. we use too many productivity tools at work, scattering our data across multiple platforms like Notion, Google Drive, Slack, Figma, and Dropbox. The more tools we have, the more fragmented our data becomes, making searches increasingly complex. So, why can’t ChatGPT solve this? The Weaknesses of Generative AI: Questions ChatGPT Misses “What was our team’s scope during the new project meeting?”“How much of our marketing budget remains this quarter?”“Has Legal reviewed the new advertising contract?” ChatGPT can’t really answer these questions. Why? Because Generative AI like ChatGPT doesn’t know your company’s data, and it doesn’t have permission to access it either. Instead, it’ll probably just throw out some generic advice or make something up. And it’ll sound very confident while doing it. Generative AI’s Structural Limitations No Access to Internal Data Generative AI tools like ChatGPT are trained on publicly available web data. It cannot access your company’s internal tools like ERP, CRM, Google Drive, Slack threads, meeting notes, or contracts, meaning it can’t provide accurate answers based on real data. No Understanding of Work Context Generative AI lacks awareness of why a document was created, who authored it, what project it supports, and how it fits into broader workflows. Without this understanding, it cannot link related documents, track project progress, or reflect the nuances of internal business processes, creating significant limitations. Security and Privacy Vulnerabilities Most generative AI services, including ChatGPT, run on cloud-based systems where users enter prompts and receive answers in real time. This setup is convenient, but not without risk. In March 2023, a bug caused ChatGPT to show other users’ chat titles and billing details. In 2024, Italy fined OpenAI €15 million for collecting personal data without clear consent. These cases show that generative AI must handle user data with stronger privacy protections. Hallucinations Risks in Business A hallucination in generative AI refers to a response that is entirely or partially made up, yet presented as if it were true. These outputs often sound confident and credible, which makes them especially dangerous in business settings. For example, a support chatbot giving incorrect information may harm a company’s reputation and reduce customer trust. Faulty outputs can also mislead decision-makers, leading to financial loss or strategic mistakes. Hallucinations: Real-World Cases OpenAI CaseIn 2024, OpenAI introduced its latest models, o3 and o4-mini, aiming to enhance reasoning capabilities. However, research found that these models exhibited a higher rate of hallucinations compared to their predecessors. (Source: OpenAI’s new reasoning AI models hallucinate more) Google Bard CaseIn 2023, Google’s Bard AI incorrectly claimed the James Webb Space Telescope (JWST) captured the first image of a planet outside our solar system — actually captured by the Very Large Telescope (VLT) in 2004.This error caused a 7.7% drop in Alphabet’s stock price, wiping out $10 billion in market cap. (Source: Best Ways to Prevent Generative AI Hallucinations in 2025) Even with better prompting, hallucinations remain an unresolved challenge in 2025. Why Do Hallucinations Occur? Hallucinations in Generative AI occur because they predict the most statistically probable sequence of words based on the patterns they learned during training. When the Generative AI like Chat GPT encounters gaps in its knowledge or ambiguous prompts, it fills in the missing information with plausible-sounding, but often inaccurate, content. This happens because Generative AI does not verify facts against an external source during response generation. They are a byproduct of how probabilistic language models. Enterprise AI Search Controls Hallucination with RAG Controlling hallucinations through RAG is a key reason why businesses choose Enterprise AI Search over generative AI. This capability clearly distinguishes the two. RAG (Retrieval-Augmented Generation) is a method that improves AI reliability by combining search before generation. Instead of relying solely on learned patterns or internet search, it retrieves relevant information from internal company data before generating a response. This approach reduces the risk of hallucinations and helps Enterprise AI Search deliver accurate, context-aware answers based on real facts, instead of pretending to know. Why Businesses Need Enterprise AI Search Verified Data Sources In business, trust depends on evidence. We constantly ask what the source is and whether there is proof. Generative AI relies on public web content, which may include information that is outdated, biased, or even manipulated. Without knowing who wrote it or how accurate it is, referencing such data can pose real risks for businesses. Enterprise AI Search avoids this by focusing only on company-owned data. It connects directly to internal systems such as ERP, CRM, Google Drive, Notion, and Slack threads to retrieve and verify real-time documents and communications. Because it uses data already managed and trusted by the company, it delivers reliable answers aligned with real business operations. The Purposes: Generative AI vs. Enterprise AI Search Generative AI: Designed for CREATING content. Enterprise AI Search: Built to retrieve, integrate, and ACCELERATE DECISION-MAKING based on verified company data. As Gartner (2023) defines it, Enterprise AI Search enhances organizational productivity and operational efficiency, not by generating random content, but by helping employees find the right information faster. This shows that businesses need AI tools purpose-built for their real operational needs. Context-Aware AI Assistance in the Workplace Enterprise AI understands the flow of a project by tracing the history behind documents, communications, and records across systems like ERP, CRM, email, drives, and collaboration tools. It
What Netflix’s AI Search Means for Your Business
Netflix is making headlines again not for a new hit show, but for rethinking how users find content. During its first-quarter results conference call, CEO Greg Peters announced that Netflix is developing a new search experience powered by OpenAI. The goal? To make content discovery smarter, faster, and more intuitive for users. This shift isn’t just about streaming. It reflects a broader change in how people expect to interact with digital systems and it’s a signal that enterprise search is due for a similar transformation. From Keywords to Conversations: Expectations Are Changing These days, people search the way they speak. They don’t want to remember exact keywords or dig through complex filters. Whether it’s choosing a movie or looking for an internal policy document, they want relevant results. Netflix’s move toward conversational AI search is a response to this behavior shift. And the same expectation is rapidly emerging in the workplace. Let’s say an employee types a question like “Where can I find the Q3 sales report?” or “What’s our company policy on remote work in Europe?” into a search bar. Traditional keyword-based systems often struggle to understand these queries unless you type the exact keyword, they rarely return useful results. But AI-powered search does not. It understands the user’s intent behind the query and delivers results that truly align with what the user is looking for. Whether for entertainment or productivity, users expect digital systems to understand what they mean, not just what they type. The Cost of Poor Search in the Workplace Spending minutes or hours a day hunting for files might not seem like a big deal until you multiply it across entire teams. Inefficient search leads to wasted time, slower decisions, and lost opportunities. Let’s take a common example. A customer support agent needs to locate past interactions on a specific complaint. If the data is scattered across email, chat logs, and ticketing platforms — and the search tool can’t interpret natural queries — they lose time and context. AI-based enterprise search can change that. By understanding context and intent, it surfaces relevant results from multiple systems instantly. Smart Recommendations, Smarter Workflows Netflix’s AI adoption isn’t just helping users search but it’s helping them discover. It anticipates intent and recommends content they didn’t even know they wanted. In a similar way, AI-powered enterprise search can go beyond answering queries. It understands intent, delivers highly relevant information and documents, and can uncover internal data that users didn’t even know existed. This turns search from a passive tool into an active driver of productivity and insight. AI Search as a Strategic Investment Netflix is betting on AI search not just for user convenience, but to stay ahead in a competitive market and the same idea applies to enterprises. Better internal search boosts enterprise productivity, reduces information silos, and improves decision-making, all of which are crucial for growth and agility. This isn’t about keeping up with technology trends. It’s about enabling your workforce to work smarter and eliminate inefficiency. Conclusion Netflix’s shift toward generative AI-powered search is more than a feature update. It’s part of a larger movement toward intuitive, intelligent digital experiences. As user expectations evolve, so must the systems we rely on to do our work. Enterprise AI Search isn’t just a nice-to-have anymore. It’s becoming a business necessity. If your team is still losing time digging through emails, folders, or outdated portals, it’s time for a better way. Want to see how AI search can help your team work faster and find what matters?Let’s explore what’s possible with Refinder AI. 👉 Start your 30-day free trial Like this post? Share with others! Recent Articles