Qatalog has secured $26 million in venture funding to develop an AI knowledge management system. This significant backing highlights the transformative potential of Qatalog’s AI answers for businesses.
In this blog post, we’ll dive into what sets Qatalog apart from other knowledge management tools and explore the features that can transform your business operations.
1. Qatalog
Qatalog is a leading AI-based knowledge management system that redefines how businesses search across internal knowledge and interact with data. Once you connect your apps, you can ask questions and follow up just like you would when using ChatGPT. It searches across your company data to deliver accurate, source-backed answers.
Unlike other tools for AI knowledge management that copy your data to provide answers, Qatalog doesn’t. It pulls information directly from the source in real time without transferring or storing it on third-party websites. This approach ensures more accurate results while minimizing the risk of data breaches – a crucial advantage for teams working with live databases or adhering to strict privacy and compliance regulations.
Qatalog supports structured and unstructured data discovery. It can synthesize insights form documents stored in business intranet software and databases like Salesforce, BigQuery, and Snowflake.
The best way to experience how Qatalog consolidates internal knowledge is to try it yourself.
What makes Qatalog the best AI knowledge management tool?
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Search across unstructured structured data: Search through various file types across platforms like Google Workspace, and Microsoft Office for unstructured data, and databases like Salesforce, Snowflake, BigQuery, and Looker for structured data.
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No data indexing: Searches happen in real time without copying, uploading, or storing data.
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Plug-and-play integrations: Seamlessly connects with multiple tools and platforms.
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Enterprise-grade compliance: Offers end-to-end encryption, ISO 27001, SOC2-Type II, HiTrust, and GDPR compliance.
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Permissions control: Provides granular control over what integrations can be searched, ensuring permissions-based access.
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User-friendly interface: Optimized for question-based queries, making data retrieval simple and intuitive.
Pricing
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14-day free trial with live integrations.
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Pro plan with 20+ integrations at $15 per user per month.
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Custom enterprise plans with dedicated support.
2. Glean
Glean is a knowledge management tool focused on search and information retrieval across various integrated tools and data sources. After setup, Glean indexes your company’s data, which can take a few days, depending on the volume.
Glean customers can self-host the platform to prevent third-party access or use Glean’s managed cloud. Glean promises to follow strict policies to restrict access to your data, only allowing it for the necessary debugging purposes.
Learn more about what it means when knowledge retrieval tools index data.
Key features
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Indexed enterprise search: Glean creates a copy of your data for faster response times.
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Encryption: Data is encrypted during transit.
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Data permissions and governance: Built-in controls to manage access.
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Contextual results: Results are tailored based on user behavior, language, and relationships.
Glean pricing
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Glean’s pricing isn’t public – book a demo for details.
Check out Glean alternatives.
3. GetGuru
Guru is an AI-powered platform to centralize and manage company knowledge by integrating various tools and workflows. Guru provides enterprise AI search, intranet, and a wiki in one platform.
The platform requires either Knowledge Sync or direct use as your knowledge base, wiki, or intranet to deliver accurate AI answers. Guru performs optimally when all three products are used together. The AI search function alone may be limiting and require advanced search techniques, such as quotes or binary searches, to locate specific information. Compare Glean vs Guru vs Qatalog.
Key features
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Chrome extension: Works across cloud-based tools.
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Knowledge Sync: Customers can migrate their knowledge into Guru.
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Permissions control: Manageable access easily.
Pricing
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Free trial available.
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All-in-one plan at $18/month.
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Custom plan for enterprises.
Compare the alternatives to GetGuru.
4. ChatGPT for Enterprise
ChatGPT for Enterprise is a versatile AI tool designed for conversational interfaces and general knowledge retrieval. Although it wasn’t specifically built for workplace search or knowledge retrieval, many companies use it to process internal information. However, it doesn’t have direct integrations with enterprise apps. To customize it for various business needs users create workflows through custom plugins or APIs. Check out a secure and more integrated alternative to ChatGPT for business.
Customer prompts and company data are not used to train OpenAI models. However, some organizations may still have privacy concerns related to uploading business data to OpenAI’s systems.
Key features
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Compliance: SOC2, GDPR, and HIPAA compliant.
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Custom plugins: Required to access company-specific data.
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GPT-4 access: No usage caps.
Pricing
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Contact sales for pricing details.
Gmail search not working? Qatalog is also available for personal use.
5. Coveo
Coveo is an AI-powered federated search and recommendation platform that delivers fast, personalized results across various business environments. It excels in website and e-commerce search, improving customer self-service and helping reps resolve issues faster.
Coveo also integrates with workplace apps, enhancing employee access to knowledge at different touchpoints of their journey. However, it requires a complex setup and indexing process to support knowledge management strategies.
Key features
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AI-driven search: Provides tailored results based on user behavior.
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Unified data: Aggregates data from various sources with secure access.
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Machine learning-powered recommendations: Offers relevant suggestions based on the search context.
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Integrations: Works well with commerce, service, and web platforms.
Pricing
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Coveo offers tiered pricing based on usage and features.
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Exact pricing details available upon request.
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Free trial available.
6. Microsoft Copilot
Microsoft Copilot is an AI tool designed to optimize productivity within Microsoft 365 applications like Word, Excel, PowerPoint, and Teams. While it offers valuable features for automating tasks and enhancing workflows within the Microsoft ecosystem, it may not be the best fit for organizations specifically seeking a dedicated knowledge management platform like Qatalog.
Key features
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Natural language processing: Leverages AI to automate repetitive tasks and provide suggestions.
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Seamless integrations: Works natively within Microsoft 365 applications but requires significant investment in the Microsoft 365 ecosystem to maximize its capabilities.
- Real-time collaboration: Facilitates knowledge sharing by surfacing contextual information during discussions or project updates.
Pricing
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Pricing depends on selected features and licensing tiers within Microsoft 365.
Related: Is ChatGPT better than Microsoft Copilot?
How is AI used in knowledge management?
AI transforms knowledge management by automating and optimizing how users capture and retrieve data and information. Here are several AI knowledge management use cases.
1. Intelligent search and retrieval
AI-based knowledge management (KM) tools leverage natural language processing and machine learning to better understand user queries and deliver relevant results. For example, Qatalog can search through both structured and unstructured data across diverse sources (e.g., documents, databases, emails) and retrieve accurate information quickly.
It integrates with various systems (Google Suite, Salesforce, BigQuery, etc.) to make this process seamless.
2. Knowledge discovery
AI uncovers insights by identifying patterns and relationships within vast datasets. Advanced language models enhance knowledge discovery by surfacing new connections or overlooked information. The shift from basic text retrieval to intricate semantic understanding improves decision-making and problem-solving.
3. AI agents and assistants
AI-powered agents and virtual assistants help employees by answering questions, guiding them to relevant resources, or even performing simple tasks like scheduling meetings or retrieving data. This reduces the burden on human support teams, improves efficiency, and boosts customer experience.
4. Enhanced data governance and compliance
AI knowledge management systems that avoid indexing company data directly facilitate stronger data governance and significantly reduce data security risks involved in storing or transferring data. This is especially relevant in highly regulated industries.
5. Collaboration and workflow automation
AI can streamline collaboration by suggesting relevant documents or resources in real time during projects. AI KM software can also automate workflows related to knowledge tasks, like routing documents for review or approval.
6. Sentiment and contextual analysis
In addition to organizing data, AI can analyze employee or customer feedback, emails, or support tickets, helping organizations understand sentiments and trends. This is crucial for companies dealing with customer knowledge bases.
Generative AI helps organizations manage vast amounts of information effectively and makes it easier for teams to find and use the knowledge they need.
Learn more about search technologies.
How are AI knowledge management tools better?
Traditional knowledge management tools offer essential functionalities for manually organizing and sharing information, while AI-powered knowledge management platforms significantly enhance these capabilities by introducing automation, real-time, contextual search, and seamless integrations to keep knowledge relevant.
Organizations looking to leverage their knowledge assets more effectively and accelerate their processes may benefit from adopting AI KM solutions.
Here’s a comparison table of knowledge management tools and AI-powered tools.
Aspect |
Traditional knowledge management tools |
AI-powered knowledge management tools |
Core functionality |
Document management, collaboration, keyword-based search, manual categorization. |
Natural language processing, semantic search, conversational information retrieval, personalized recommendations. |
Knowledge accessibility |
Require manual search for knowledge. |
Embeds knowledge into workflows for real-time, contextual access. |
Automation & efficiency |
Require significant human effort for tasks. |
Key topics automatically extracted from documents and organized. Reduced need for manual intervention. |
User experience |
Static interfaces, manual navigation and search through document repositories by users. |
AI assistants and chatbots that interact with users in natural language |
Integration capabilities |
Standard business app integration, limited extensibility. |
Seamless integrations with your current tech stack, robust APIs for customized solutions. |
Data insights |
Basic usage analytics, minimal actionable insights (i.e., document downloads, user activity logs). |
Machine learning for usage analysis, predictive insights, trending topics. For example, AI can identify outdated information prompting a review and update. |
Content creation |
Users create, upload, and maintain content repositories. Provide limited content assistance (i.e. templates). |
AI-assisted content creation (summaries, document drafts, translations), smart content curation. |
Scalability & adaptability |
Fixed structures, challenging to adapt to changing organizational needs and difficult to scale. |
Dynamic adaptation, quick adjustments for new data, and organizational changes (i.e., new departments, terminologies, and knowledge areas) without manual reconfiguration. |
Security & compliance |
Basic access controls, and manual compliance management. |
AI-driven security (anomaly detection), automated compliance monitoring, and proactive content flagging. |
Knowledge verification |
Manual review for accuracy. |
AI automatically verifies and updates knowledge for real-time accuracy. |
FAQ
What steps should organizations take to ensure their data is ready for AI integration?
Knowledge management vendors typically index company data to prepare it for AI integration and effective use. This involves copying, mapping, and storing data in a repository for easy retrieval. These systems require continuous maintenance and often a dedicated team to manage the data flow. However, this approach can result in inaccuracies, increased maintenance costs, and security risks from storing data copies on third-party servers.
In contrast, Qatalog eliminates the need for data indexing. It directly converts unstructured data into actionable AI-powered insights, simplifying the process while reducing maintenance and security concerns. This enables teams to focus on leveraging their data effectively without the complexities of conventional indexing.
Find out more about the challenges of indexing data for AI-powered search.
How does an AI-based knowledge management system integrate with other tools?
AI-powered knowledge management platforms like Qatalog offering RAG as a service are designed for seamless integration with the existing systems and tools that organizations already rely on. Qatalog integrates with other apps using a combination of APIs and pre-built connectors. It consolidates data from multiple sources such as document storage systems, communication platforms like Google Workspace, Microsoft 365, and Slack, as well as tools like Salesforce and cloud-based databases like Snowflake and BigQuery.
By centralizing this information in one unified interface, Qatalog ensures teams can access everything they need without disrupting existing workflows or requiring significant system changes.
Does Gen AI replace knowledge work?
Generative AI transforms knowledge work enterprise search software by automating repetitive or data-heavy tasks like summarizing documents, generating reports, and answering queries. It serves as a powerful tool that enhances productivity and is unlikely to replace knowledge work. By getting rid of the grunt work, AI allows knowledge workers to focus on complex tasks, creative thinking, and making decisions that require specialized expertise. Looking ahead, it’s more about collaboration between AI and humans, combining the strengths of both to improve efficiency and decision-making.