Zive vs. ChatGPT vs. Copilot: Which one fits enterprise use?

The challenge of enterprise AI adoption

The rise of generative AI in the workplace is undeniable. Studies show that employees who use AI tools can boost their performance by up to 40% over those who don’t, and business leaders widely expect AI to drive major productivity gains. However, rolling out AI across an entire organization – safely, scalably, and strategically – remains a daunting challenge. Enterprises have to contend with data scattered across silos, strict compliance requirements, and the need to integrate AI into existing workflows. In fact, roughly 80% of company knowledge is unstructured (documents, wikis, chats, etc.), forcing employees to waste time searching for information in disparate apps. A famous McKinsey report found employees spend an average of 1.8 hours each day – nearly 20% of the work week – just searching for and gathering information. This inefficiency underscores why a successful AI solution must connect to corporate knowledge bases and tools to truly augment employees' work.

At the same time, organizations (especially in Europe) face stringent data privacy and security obligations. Regulations like GDPR already mandate careful handling of personal data, and the new EU AI Act is coming into effect with no grace period – as of August 2025, companies deploying AI systems must meet extensive transparency and risk management requirements or face fines up to 7% of global turnover.

Simply put, an enterprise AI initiative must be compliant and trustworthy by design.

Currently, many teams are experimenting with popular AI offerings such as OpenAI’s ChatGPT (now available in an enterprise edition) and Microsoft’s Copilot for Office 365. These tools are powerful within their own spheres – ChatGPT for general Q&A and content generation, and Copilot for assisting within Microsoft apps. But were they built to serve as a foundation for company-wide AI adoption? Or do their limitations make it difficult to use them as an all-encompassing enterprise AI platform?

In this article, we compare ChatGPT Enterprise, Microsoft 365 Copilot, and Zive – a dedicated enterprise AI platform – across key dimensions for enterprise AI. We’ll examine each solution’s strengths and weaknesses in areas like integration capabilities, knowledge management, compliance, and scalability.

ChatGPT Enterprise: Powerful assistant with platform limitations

ChatGPT Enterprise is the business-focused edition of OpenAI’s ChatGPT, offering the raw power of GPT-4 through an intuitive chat interface. Its natural language abilities are arguably best-in-class, capable of drafting content, brainstorming ideas, writing code, and answering general questions with human-like fluency.

From an enterprise perspective, ChatGPT Enterprise does introduce some important features: it provides enterprise-grade security and privacy commitments (OpenAI does not use your chats or data to train models, and the service is SOC 2 compliant with encryption). It also includes an admin console for managing users with Single Sign-On (SSO), domain verification, and usage insights‍. In essence, organizations get a centrally managed version of ChatGPT with unlimited, faster GPT-4 access and extended context length. These are valuable improvements for safe, internal use of the AI. And for certain use cases – e.g. a marketing team generating copy or a researcher summarizing reports – ChatGPT Enterprise can be a great assistant out of the box.

That said, ChatGPT Enterprise remains a standalone tool, not a platform you can easily integrate into all facets of your business operations. It was not designed to plug into your proprietary databases or SaaS tools. Some key limitations include:

• No native integration with enterprise systems: ChatGPT cannot directly connect to your internal knowledge bases, business applications or databases to fetch information. It has no built-in connectors to tools like SharePoint, Confluence, Salesforce, Slack, etc. – meaning it only knows what a user manually tells it in a prompt. Unlike an enterprise search, it won’t automatically recall company-specific knowledge unless you copy-paste that context each time. This lack of data integration is a serious shortcoming for enterprise knowledge management.
• No persistent company memory or knowledge structuring: By default, ChatGPT does not retain context from your organization’s past interactions or documents beyond a single session. It doesn’t build an index of your internal data or "learn" your company’s knowledge in a governed way. Every query stands alone, which limits its usefulness for tasks like cross-document search or cumulative learning. There is no concept of an evolving knowledge base or content lifecycle management in ChatGPT – it’s essentially an on-demand generative AI with short-term memory.
• No ability to automate workflows or create custom AI agents: ChatGPT is an AI assistant you converse with; it doesn’t provide a framework to orchestrate tasks or integrate with process automation. For instance, you cannot use ChatGPT alone to build a sales assistant that executes multi-step tasks, or an HR bot that automates onboarding workflows, without developing a lot of custom code via the OpenAI API. There is no no-code tool or agent builder in ChatGPT Enterprise – it’s not a platform for custom enterprise AI applications, just a very smart chatbot.
• Locked into OpenAI’s ecosystem of models: As the name implies, ChatGPT Enterprise only gives you access to OpenAI’s models (GPT-4, and presumably updated GPT-3.5 variants). You have no flexibility to choose different underlying large language models for different tasks – for example, you can’t use an open-source LLM or a model from another provider within the ChatGPT interface. Many enterprises might prefer certain models for data residency or cost reasons (or future specialty models), but ChatGPT doesn’t accommodate that. It’s one-size-fits-all with OpenAI’s model, which could be a drawback if needs diversify.
• Data residency and compliance concerns: By using ChatGPT, your prompts and outputs still travel to OpenAI’s servers (which are U.S.-based) for processing. European companies have to carefully evaluate this, since sensitive data could be exposed to a jurisdiction under the U.S. CLOUD Act. Notably, Italy’s data protection authority briefly banned ChatGPT in 2023 over GDPR privacy violations and later fined OpenAI €15 million for how it handled personal data in the service‍. This highlights the compliance uncertainty of using a U.S.-hosted AI service in Europe. There is also no option to self-host or ensure EU-only data processing with vanilla ChatGPT. For highly regulated industries or any company subject to strict EU data laws, this lack of control and transparency can be a show-stopper.
• High (and unpredictable) cost at scale: ChatGPT Enterprise’s pricing is not publicly disclosed in detail, but it essentially offers “unlimited” GPT-4 usage for a hefty fee (aimed at large organizations), while heavy use of the OpenAI API for custom integration can rack up substantial costs per token. If an entire company started relying on ChatGPT for daily work, the compute costs could become significant. In contrast, more purpose-built solutions might handle on-premise deployment or more efficient caching of knowledge to control costs. Microsoft’s CEO notably remarked that running GPT-4 level models for every employee’s queries can be very expensive – so cost management is a consideration. In short, ChatGPT is an expensive compute service and scaling it to thousands of employees may not be economically practical without careful governance.

In summary, ChatGPT Enterprise is a powerful general AI assistant that shines for individual productivity and team brainstorming, thanks to GPT-4’s capabilities and an easy interface. It provides a taste of what AI can do and is excellent for experimentation or pilot programs. But as a foundation for enterprise-wide AI deployment, it falls short on integration, customization, and compliance. It’s not (yet) an “AI platform” for the enterprise – it’s a single-tool solution. The bottom line: ChatGPT is great for ad-hoc usage by individuals or small teams, but using it to power AI across departments, on all your internal data, with full IT control, is not feasible in its current form

Microsoft 365 Copilot: Convenient for Microsoft workflows, but a walled garden

Microsoft 365 Copilot is another prominent entrant in the enterprise AI space, with a fundamentally different approach. Instead of a standalone chatbot, Copilot embeds AI assistance directly into the Microsoft Office 365 suite – Word, Excel, PowerPoint, Outlook, Teams, and more. The promise is compelling for companies already using Microsoft’s ecosystem: Copilot can help draft documents in Word, analyze and visualize data in Excel, generate emails in Outlook, create meeting summaries in Teams, and so on, all within the familiar interfaces of those applications‍. For users, it feels like having an intelligent assistant inside Office that you can call upon with a prompt, rather than having to go to an external chat tool.

Where Microsoft 365 Copilot shines is in leveraging context from your files and communications to make suggestions. Because it’s integrated at the application level, Copilot can (with appropriate permissions) draw on your calendar, emails, or the document you’re editing to tailor its responses. For example, in a Word document, you might ask Copilot to draft a summary and it knows about the content of that document. In Teams, you could ask “What were the key decisions in the last meeting?” and it can analyze the meeting transcript to answer. This deep integration into daily workflows means employees don’t have to switch contexts – the AI comes to them, which can reduce friction and increase adoption. Another advantage is the user experience: it’s provided by Microsoft, so it uses your existing Office 365 login and respects the permission structures and security of your Microsoft tenant. There’s an element of trust and consistency for IT since it’s an extension of the tools they already manage.

However, the very strength of Copilot – being tied to Microsoft’s ecosystem – is also its greatest limitation. By design, Microsoft 365 Copilot operates exclusively within Microsoft’s walled garden of products. If your workflows and data reside 100% in Microsoft apps and services, this is fine. But most enterprises use a variety of tools. Here are the key limitations to consider:

• Only supports Microsoft environments: Copilot is not a generic AI interface you can connect to arbitrary data sources. It currently cannot natively interface with third-party or external systems like Google Drive, Slack, Confluence, Salesforce, ServiceNow, or other non-Microsoft platforms. For example, if your engineering knowledge is in Confluence, or your CRM data is in Salesforce, Copilot out-of-the-box will not be able to access that information. It’s constrained to Microsoft 365 data (documents in OneDrive/SharePoint, emails in Exchange, chats in Teams, etc.). Organizations are rarely 100% Microsoft-only; the inability to tap into other silos means Copilot cannot serve as an enterprise-wide knowledge solution. This is echoed by the fact that Copilot has no integration for Slack, Notion, and many other common enterprise apps. Any integration outside of Microsoft would require custom development (if possible at all via Microsoft Graph APIs), which defeats the purpose of a ready-made AI assistant.
• Not a standalone platform: Microsoft 365 Copilot is best understood as a feature addition to Office, not a platform to build on. You cannot easily build new AI-driven applications with Copilot beyond what Microsoft delivers. There is no no-code interface to create custom AI agents or workflows. For instance, you can’t use Copilot to build an AI customer support chatbot for your website, or a custom workflow that spans multiple systems – those are outside Copilot’s scope. It has a predefined set of capabilities aligned to Office apps. In other words, Copilot is not extensible by end-users or even by your developers in any significant way (aside from whatever Microsoft might enable via its Graph/API in the future). Companies looking for an AI platform will find Copilot quite limited in flexibility.
• Uses only OpenAI (GPT-4) via Azure: Behind the scenes, Microsoft 365 Copilot is powered by OpenAI’s GPT-4 (and perhaps other AI services hosted in Azure). As with ChatGPT, you don’t have a choice of model or the ability to route certain queries to a different AI. Microsoft manages the model selection (currently they have a partnership with OpenAI, so it’s mainly GPT-4). There is no support for alternative large language models, whether for cost optimization or special use cases. If an enterprise had reasons to prefer a model hosted in-house or an open-source model for certain data (e.g., to avoid sending highly sensitive data to an external API), that’s not an option with Copilot’s closed approach.
• Licensing costs and requirements: Microsoft 365 Copilot comes at a premium price – it’s offered as a paid add-on to Microsoft 365 enterprise subscriptions, at roughly $30 per user per month in addition to existing licensing. For a large company, that can mean millions of dollars per year to deploy Copilot for everyone. And that’s on top of already paying for the Office 365 suite itself. The per-seat cost makes organizations think carefully about who truly needs it. Copilot’s value might justify the cost for certain knowledge workers, but scaling it to every employee is an expensive proposition (especially compared to an internal platform that could support unlimited users at a fixed cost, as we’ll see with Zive). Furthermore, to even be eligible for Copilot, users must have certain Microsoft 365 license levels (like E3/E5 or Business Standard, etc.) – which most large enterprises do, but it introduces some friction in deployment. You’ll need your IT administrators to handle the licensing procurement and enable the feature for users via the admin center. There’s no simple “sign up online and use it” for Copilot; it’s rolled out through your Microsoft 365 tenant by IT. This means deploying Copilot might involve significant coordination and possibly phasing (Microsoft initially even had a 300-seat minimum, which was later removed, but it shows it’s designed for enterprise-scale deals, not small teams).
• Requires Microsoft-centric security posture: Because Copilot integrates with your Microsoft 365 data, organizations must be comfortable with Microsoft’s cloud handling that data (which most are, if they use 365). However, any data not already in Microsoft’s cloud won’t be accessible. Also, some companies might have regulatory constraints on using cloud AI services that analyze internal content (even Microsoft had to assure customers that Copilot does not use customer data to train models and such). Microsoft provides certain compliance promises, but ultimately you are entrusting your data to Microsoft’s AI framework. If your strategy is multi-cloud or you want an on-premise option, Copilot would not fit.
• Limited scope of impact: While Copilot can definitely improve productivity in the context of Office apps, it does not address processes and knowledge outside of those apps. Many critical business operations happen in other systems (for example, software development in GitHub or Jira, customer support in Zendesk, operations in SAP, etc.). Copilot doesn’t reach those. So it can end up as a helpful add-on for Office tasks, but not a unified solution that employees across all departments and tools will rely on. Its impact is somewhat siloed to the world of documents, emails, and meetings. For an organization seeking to boost productivity across all workflows, Copilot would leave many gaps unfilled.

In summary, Microsoft 365 Copilot is an excellent enhancement if your company lives and breathes Microsoft 365. Employees will enjoy having AI assistance in Word, Excel, Outlook, and Teams – it can speed up document creation, provide formula help in spreadsheets, generate summaries and more. For Microsoft-centric organizations, Copilot could drive adoption of AI by virtue of being seamlessly integrated. However, Copilot is not an AI platform for the entire enterprise – it’s an AI helper for the Microsoft ecosystem. It lacks cross-platform integration, broader knowledge consolidation, and the ability to handle use cases outside of Office. It’s also costly on a per-user basis, which makes scaling to everyone an expensive decision.

The bottom line on Copilot: It’s a strong product for what it does (bringing GPT-4 into Office apps), and it will benefit companies that are all-in on Microsoft 365. But it is “a helpful add-on, not a complete platform” for enterprise AI. Companies will still need other solutions to cover the non-Microsoft parts of their business and to implement AI in a more centralized, governable way.

Zive: a comprehensive AI platform built for the enterprise

Zive is an all-in-one enterprise AI Platform designed from the ground up to enable fast, secure, and scalable AI adoption across an entire organization. In contrast to ChatGPT or Copilot – which started as consumer or single-product AI solutions – Zive’s focus is on being enterprise-ready: integrating with company data, automating business workflows, and providing the necessary governance for widespread use. It’s not a chatbot or a plug-in; Zive is a full platform that acts as a central AI brain for your business. This means it aims to combine the strengths of an intelligent assistant, an enterprise search system, and an automation tool – all under your company’s control. Let’s break down how Zive addresses the enterprise requirements we’ve discussed:

• Integrated enterprise knowledge base: Zive tackles the problem of scattered, siloed information by connecting to 100+ business applications and data sources . It offers ready-made integrations for popular enterprise tools – from CRM systems like Salesforce, ERP like SAP, collaboration wikis like Confluence/Notion, cloud storage like SharePoint/OneDrive, communication platforms like Slack and Teams, email, databases, and many more. In addition, Zive provides APIs and connectors to integrate any custom or legacy systems an organization might have. The result is that Zive can index and understand a company’s entire digital knowledge estate. All those previously siloed documents, emails, tickets, and records become part of a unified knowledge repository that Zive’s AI can draw upon. An employee can ask Zive a question and it could pull relevant information whether that lives in an Excel report, a SharePoint site, a PDF on Google Drive, or a conversation in Slack. This is transformative – instead of spending 2 hours searching multiple systems (remember that 1.8 hours/day stat), employees can get answers in seconds from Zive, with the AI intelligently retrieving and synthesizing knowledge across all tools. In essence, Zive functions as an AI-powered enterprise search and Q&A system that truly spans the company’s data silos. It’s like having a private ChatGPT that has been trained on your organization’s information (with proper permissions, of course). This capability directly addresses the knowledge management pain point that neither ChatGPT nor Copilot can solve natively.
• Enterprise search and contextual Q&A: Building on its integrated data, Zive provides a unified search experience for users. Employees can ask questions in natural language and Zive will search through the indexed company data to generate answers, complete with references to source documents if needed. Think of it as a “Google for business” that knows your company’s content. Unlike a traditional keyword search, Zive uses AI to understand intent and context, so queries like “Find our latest pricing strategy presentation” or “What is the process for onboarding a new vendor?” yield precise results even if the phrasing doesn’t match file names. Moreover, Zive’s AI can actually read the relevant documents and produce a concise answer or summary, not just a list of files. This kind of enterprise search (sometimes called cognitive search) dramatically improves information discovery. And because Zive honors all the access permissions of the source systems, employees only see results they are allowed to see – a critical feature for security. Essentially, Zive becomes the intelligent knowledge hub for the organization, something neither ChatGPT (with no internal data access) nor Copilot (limited to MS data) can claim.
• Custom AI agents and workflow automation (no-code): One of Zive’s standout features is the ability for non-technical users to create AI-powered agents and automations without writing code. Through a visual interface, every team can configure digital assistants tailored to their needs. For example, your marketing team might build an agent that automatically analyzes campaign data from Google Analytics and Salesforce and answers questions like “Which leads are most likely to convert this week?” or generates a summary of campaign performance. Your HR team could create an onboarding Q&A bot that new employees can ask questions like “How do I set up my VPN?” – the bot would pull from HR manuals and IT knowledge bases to give instant answers. Operations could automate an AI agent to handle routine internal service-desk tickets by drawing on documentation. The key is these agents can perform multi-step tasks and access relevant data as configured, essentially acting like specialized mini-chatbots or task bots for each department’s use case. Zive provides templates and an intuitive builder so that creating these AI workflows is as simple as point-and-click. No engineers or external consultants are required to deploy these automations. This democratizes AI development within the enterprise – power users in any department can leverage AI to streamline their processes, without waiting on IT or having to invest in custom software development. Neither ChatGPT Enterprise nor Microsoft Copilot offers such a capability; this is a major differentiator that enables enterprise-wide AI orchestration and automation.
• Multi-modal accessibility: Recognizing that employees operate in various environments, Zive is built to meet users where they are. It is not just a web app; you can use Zive through multiple interfaces for convenience:
  • Dedicated apps or web interface: Users can interact with Zive via a central web app (or desktop app) as an AI assistant or search portal.
  • Within Slack and Teams: Zive integrates into collaboration platforms. For instance, you can query Zive right inside a Slack channel or Teams chat – useful for pulling knowledge into a conversation or getting an instant answer without leaving your chat app.
  • Browser extension: Zive offers an extension that allows you to use it on any webpage or web-based SaaS app. This can provide on-the-fly assistance, such as summarizing a webpage or drafting content while in an online tool.
  • Embeddable widget: For internal tools or intranet sites, Zive can be embedded as an AI helper. Imagine your company’s internal portal having a “Ask Zive” box that employees can use to query anything or get help.
  This flexibility ensures that whether an employee is in email, in a CRM web app, or chatting in Slack, Zive’s assistance is one click away. The goal is to integrate AI into the daily flow of work rather than requiring users to constantly switch context (which was a drawback with standalone ChatGPT). It lowers barriers to adoption – people can rely on Zive naturally throughout their day, which increases the ROI of the system.
• Enterprise-grade governance, security and compliance: As a European-made platform (Zive is based in Germany), compliance and data protection are at the core of its design. All of Zive’s services and data processing are hosted exclusively in the EU on GDPR-compliant infrastructure. Data never needs to leave the EU, and Zive is certified to ISO 27001 for information security management. Importantly, Zive’s architecture is set up so that none of your private company data is sent to third-party LLM providers without control. When Zive uses external models like OpenAI or others, it does so via agreements and systems that ensure GDPR compliance (e.g., through EU datacenters or using encryption/proxying such that the model providers don’t retain any data). In fact, Zive guarantees that your data is never used to train the underlying AI models, eliminating the concern that many have with public AI services. The platform essentially acts as a secure intermediary – your data stays in the Zive system, and only the necessary query context is sent to the model, with all appropriate legal safeguards in place. Moreover, Zive implements role-based access controls and audit logs at a granular level. This means it respects all user permissions from source systems (so employees only get answers from data they are allowed to see), and it keeps an audit trail of AI usage for compliance and monitoring. Administrators can manage who can do what, set retention policies, and ensure the system aligns with company policies. With the EU’s AI Act demanding transparency and risk controls, these governance features are crucial – and Zive is built to comply with the highest European standards for data privacy and AI usage. In short, Zive offers the peace of mind that IT and compliance teams require, far beyond the basic assurances of ChatGPT or the limited scope of Copilot.
• Multi-LLM support and future-proof flexibility: Zive takes an open, best-of-breed approach to AI models. Instead of relying on a single AI model, Zive can leverage multiple large language models and even new models as they emerge. Out of the box, it supports leading models like OpenAI’s GPT-4, Anthropic’s Claude, Google’s PaLM/Gemini, Meta’s Llama (or other open-source models like Mistral), etc. The platform will automatically select the most appropriate model for a given task or query – for example, it might use GPT-4 for a complex analytical question, but use a smaller open-source model for a very domain-specific task if that’s more efficient or if data needs to stay in-house. This “right tool for the job” approach means you get the optimal balance of performance, cost, and privacy for each use case. It also avoids vendor lock-in: you are not tied to one AI provider. If a new model comes out tomorrow that is more powerful or cheaper, Zive can integrate it. This flexibility is important in the fast-evolving AI landscape. Neither ChatGPT Enterprise nor Microsoft Copilot offer such optionality – they are each tied to a specific model/backend. Zive’s multi-LLM strategy ensures your enterprise AI capabilities can evolve over time and always use the best available intelligence. It essentially future-proofs your AI investment.
• Scalable deployment and cost efficiency: Zive is designed for rapid implementation and company-wide scaling. The platform can typically be rolled out in a matter of days or weeks, not months. Since it’s offered as a managed SaaS, the setup mainly involves connecting your data sources (via those pre-built connectors) and configuring initial use cases. Zive’s team or partners usually assist with onboarding to ensure a smooth rollout. The speed of deployment is a notable advantage – for instance, ClimatePartner (a Zive customer) was able to launch Zive to all their employees within weeks. This is in stark contrast to the lengthy process of custom-building an internal solution or even the configuration work sometimes needed for Microsoft’s AI features.
• In terms of cost, Zive offers a transparent and scalable per-user pricing model designed for enterprise rollout, with plans starting at €15 per user/month and expanding based on usage needs and team size. Unlike ChatGPT Enterprise or Microsoft Copilot, which charge significantly higher per-seat fees (often around $30 to /user/month) while offering only a fraction of the integration, automation, and governance capabilities, Zive delivers broader functionality at a fraction of the cost. A business license includes access to Zive’s full platform—multi-LLM orchestration, enterprise search, no-code agent builder, and standard integrations—along with granular permissions, audit logs, and admin controls. For larger organizations, Zive offers volume-based discounts and custom enterprise plans, ensuring predictable spend even at scale. Because Zive consolidates capabilities that would otherwise require multiple tools—AI assistants, enterprise search, internal knowledge bots, workflow automation—it often results in 50–70% lower total cost of ownership compared to other siloed AI tools. More importantly, the pricing structure makes it feasible to equip every knowledge worker with AI—not just a select few—maximizing productivity and organizational impact. With no surprise compute fees or limited app-specific usage, Zive’s pricing is not just competitive—it’s engineered for sustainable, enterprise-wide adoption.

With all these capabilities, Zive stands out as a platform that was truly built to enable “AI everywhere” in the enterprise. It combines the conversational power of advanced LLMs with the context of your business data, adds tools for automation, and wraps it all in enterprise governance. A European IT leader evaluating solutions will appreciate that Zive checks the boxes on compliance (GDPR, EU hosting, security certifications), on integration (connectors to existing systems), and on flexibility (multi-LLM, customizable agents). In other words, Zive is designed to be the AI backbone of an organization – something you can deploy broadly to employees knowing it will securely empower them rather than being a risky experiment.

To put the differences in perspective, let’s look at a side-by-side comparison of ChatGPT Enterprise, Microsoft 365 Copilot, and Zive across key enterprise criteria:

Feature comparison

The table below summarizes how Zive compares to ChatGPT Enterprise and Microsoft Copilot on the features and requirements that matter for enterprise AI:

Final Thoughts

For companies that are simply experimenting with AI on a small scale, a tool like ChatGPT Enterprise or Microsoft Copilot can be a great starting point. These offerings can get teams acquainted with AI and deliver quick wins in isolated domains (e.g., individual productivity or Office document tasks). However, if the goal is to roll out AI across your entire organization – across all departments, workflows, tools, and data – you will need more than a standalone chatbot or a single-app plugin. Neither ChatGPT nor Copilot was designed to serve as the foundation for enterprise-wide AI transformation with full integration and governance.

Zive, on the other hand, provides exactly that foundation. It is the only solution of the three that was purpose-built for company-wide AI adoption, automation, and oversight. With Zive, enterprises get a unified AI platform that can serve every employee, securely harness the organization’s collective knowledge, and adapt to future needs. It offers the extensibility and control required by IT leaders, while also being intuitive and immediately useful for end users across the business.

AI has the potential to revolutionize work, but realizing that potential across a large organization requires a platform that bridges AI and your business. Zive provides that bridge. For IT leaders and C-level executives looking to empower their workforce with AI while maintaining control and compliance, Zive offers a proven path forward – an enterprise AI platform that truly fits enterprise use.

If you’d like to see Zive in action - how it unifies your company’s knowledge, automates workflows, and delivers real value across departments - we recommend checking out our short product tour.

Or, if you'd prefer a more personalized walkthrough, you can schedule a demo with our team to explore how Zive can fit your specific needs.