Sana Labs is redefining how enterprises manage knowledge, search for information, and deliver learning at scale through a single AI-powered platform. The Stockholm-based startup has just closed a $34 million Series B funding round that values it at $180 million post-money, underscoring strong investor confidence as its annual recurring revenue climbs dramatically. Led by Menlo Ventures in the United States, with EQT Ventures and a broad group of 25 angel investors and founder-operators participating, the round signals not only capital for growth but also a thumbs-up from the market for a disciplined, enterprise-grade approach to AI-enabled knowledge management and learning. Sana’s traction is notable: ARR has grown sevenfold over the past year, reflecting both demand for AI-assisted information governance and a clear path to monetization across large organizations. The company’s platform is designed to connect to a wide array of workplace apps—Salesforce, email, Notion, GitHub, Slack, Trello, Asana, and others—ingesting and organizing data across these ecosystems to provide a unified, queryable knowledge resource. The combination of knowledge management, enterprise search, and e-learning is positioned as a unique fusion in a space with many discrete products, and Sana asserts that its platform’s ability to weave these three threads together creates a distinctive value proposition for enterprise customers.
Sana Labs’ funding round and market positioning
Sana Labs closed a notable Series B round that raised $34 million, with Menlo Ventures taking the lead and EQT Ventures among the co-investors. The round also included a substantial cohort of 25 angels and founder-operator individuals, which illustrates a broad base of support from people who have built and scaled technology businesses. The round places Sana at a post-money valuation of $180 million, signaling strong market validation and investor confidence in the company’s ability to execute a multi-pronged platform that intersects knowledge management, enterprise search, and learning applications within the workplace. This funding comes at a moment when enterprises are embracing AI to transform how they manage information, make decisions, and train staff at scale. Sana’s ARR growth—seven-fold in the last year—serves as a tangible indicator of market demand and product-market fit, highlighting that organizations are seeking integrated solutions rather than point solutions for knowledge capture, search, and education.
Sana’s unique approach is framed as a platform that unites three traditionally separate domains. While several products compete in the knowledge management, enterprise search, or enterprise learning spaces individually, Sana argues that its platform delivers a holistic experience by combining all three into a single, cohesive system. This integrated approach is designed to address the organizational need to capture information across disparate tools, surface it via intuitive natural-language queries, and leverage that same information to build and deliver learning content that supports onboarding, training, and ongoing professional development. The company’s ability to onboard data from a broad set of workplace apps and to maintain the knowledge base as information evolves is positioned as a differentiator. The funding round thus functions not only as a capital infusion but also as a vote of confidence in the company’s thesis about the future of knowledge work in the AI era.
Industry observers have noted that there is a crowded landscape for knowledge management, enterprise search, and corporate training. In that environment, Sana positions itself as a single platform designed to reduce the friction that typically arises when organizations try to stitch together multiple tools, data sources, and learning materials. By offering a unified interface and a shared data model, Sana aims to enable continuous use rather than episodic deployment, a characteristic that the company says is evident in the daily engagement patterns of its users. The investors’ backing, including Menlo Ventures’ leadership, underscores a belief that Sana’s platform has the potential to scale across diverse industries and geographies, leveraging a framework that can handle the complexity of enterprise data while delivering a user-friendly experience.
Sana’s growth story is also a reflection of the broader trend toward enterprise AI that emphasizes practical, results-oriented applications. The company’s emphasis on knowledge management—ensuring that the information held within an organization’s knowledge workers’ brains and digital tools is accessible and reliable—aligns with a larger push in enterprise software to combine data preparation, retrieval, and learning into a single system. The Series B round signals to the market that investors expect Sana to accelerate product development, expand its customer base, and broaden its reach across industries and regions, all while maintaining a focus on the user experience and on delivering measurable outcomes through its AI-driven capabilities.
In addition to the core platform, Sana’s model leverages established AI foundations, most notably models from OpenAI, with Sana describing a deep partnership around how to tailor and deploy these models within enterprise environments. This collaboration positions Sana to offer scalable AI capabilities that can be refined for domain-specific use cases, enabling customers to deploy AI that understands their particular workflows, terminology, and information architecture. The combination of a robust data-integration layer, a capable AI engine, and a user-centric front end is central to Sana’s value proposition and to the expectations set by the new funding round. The investment thus carries implications for the company’s go-to-market strategy, product roadmap, and international expansion plans, all of which are likely to be accelerated in the wake of the round.
How Sana’s platform aligns knowledge management, search, and e-learning
Sana’s platform is designed to connect with the variety of tools that organizations use daily, bringing together data captured in different apps and systems. The system ingests information from critical workplace sources—Salesforce, email, Notion, GitHub, Slack, Trello, Asana, and similar tools—and automatically organizes it to create a cohesive, searchable knowledge repository. When users need to access information, they can query Sana in natural, human language, effectively translating everyday speech into precise search results. This direct, intuitive interaction with the knowledge base is a core user experience differentiator, as it reduces the cognitive and operational overhead typically associated with information retrieval in large organizations.
Beyond information retrieval, Sana’s data also serves as the backbone for e-learning modules. These modules support onboarding, training, and professional development initiatives, and they can be created or curated by individuals within the organization as well as generated by Sana itself. The learning modules take various formats, including quizzes, polls, and interactive sessions, to engage learners and reinforce knowledge. The platform can also generate interactive Q&A around webinars, producing outcomes that feed back into the knowledge base to improve future reference and learning materials. This creates a feedback loop where content creation, knowledge discovery, and learning reinforce each other, driving continuous improvement in both information accuracy and user engagement.
The triangulation of knowledge management, enterprise search, and e-learning is central to Sana’s go-to-market narrative. In practice, this means that as information is ingested and organized, the platform surfaces it through accessible, natural-language queries. Simultaneously, it repurposes the same information into structured learning experiences that support onboarding, ongoing training, and professional development. This dual use of information—knowledge discovery and education—creates multiple value streams for customers, from improving information accessibility to accelerating employee ramp-up and enabling consistent, scalable learning programs across large teams.
The platform is not a static repository; it’s a dynamic system designed to adapt as an organization’s data and personnel needs evolve. The AI engine continuously ingests updates across connected apps and adjusts the knowledge graph accordingly, maintaining accuracy and relevance as information changes. Users who need to locate or verify information can rely on Sana to interpret queries in everyday language and deliver results that align with the organization’s data, policies, and verified sources. The learning content attached to that information can be updated in tandem, ensuring that training materials stay current with the knowledge base and the workflows that rely on it.
Sana emphasizes that the platform’s back-end is built to scale, with the AI models and customization layers designed to handle a wide range of enterprise contexts. The company notes that while it builds the platform’s own models and interfaces, it leverages OpenAI’s models as a core capability, applying domain-specific fine-tuning and optimization to deliver a tailored user experience. The emphasis on user experience—ensuring that the interface remains intuitive, fast, and responsive—reflects a broader trend in enterprise AI, where usability and reliability are critical for widespread adoption. The platform’s ability to unify information access with learning content means that organizations can not only find what they need quickly but also turn those insights into practical training that improves performance and knowledge retention.
For employees, Sana offers a streamlined path to information that matters. Instead of navigating through a labyrinth of apps and manual workflows, they can search for knowledge in natural language and receive results that are immediately actionable. For managers and learning and development teams, the platform provides a framework to establish and track training programs that are tightly integrated with the company’s knowledge base. The learning modules become not just a supplement to training but an intrinsic part of the knowledge ecosystem, with content that is directly informed by the organization’s real-world data and experiences. This integrated approach is designed to reduce the time and effort required to onboard new hires, to train specialists, and to upskill existing staff, all while maintaining alignment with organizational standards and practices.
Sana’s product philosophy also highlights a practical orientation toward data governance and verifiability. The company recognizes that accuracy and trust are central to enterprise knowledge management, and it embeds a verification framework into the platform. Customers can designate which sources are verified, how information is sourced, and what access permissions apply to unverified content. This governance model is designed to minimize the risk that users encounter conflicting or unreliable information, while still preserving the advantages of AI-assisted information discovery and learning. The approach reflects a careful balance between AI-driven automation and human oversight, with controls that can be customized to fit an organization’s risk tolerance, regulatory environment, and quality standards.
In terms of outcomes, Sana’s platform aims to deliver measurable improvements in knowledge accessibility and learning effectiveness. By consolidating information across multiple tools and turning it into structured, digestible learning content, the platform is positioned to improve onboarding times, accelerate professional development, and reduce the friction associated with information fragmentation. The integration of knowledge management and learning also offers potential efficiency gains for administrators, who can manage content creation and updates within a single system rather than coordinating across disparate tools. The end result is a more connected and capable organization, where employees can find and apply knowledge quickly and continuously develop their skills.
The evolution of Sana: from back-end engine to front-end learning
Sana’s early concept centered on building a robust backend machine learning engine designed to organize and categorize information from a broad spectrum of enterprise data sources. The vision was to create an intelligent system that could understand the structure and content of what employees create and use, enabling efficient storage, retrieval, and future use. However, as the company engaged with prospective customers, it became clear that there was a strong demand for something more tangible and immediate: a front-end experience that allowed people to query information easily and to design learning materials that complemented the knowledge base.
Joel Hellermark, Sana’s CEO and founder, explains that the pivot from a backend-first approach to including a front-end querying experience emerged early in the company’s development. Customer feedback and market signals indicated that users wanted a straightforward way to explore and leverage the stored information, not just to have it passively organized in the background. In response, Sana expanded its product strategy to incorporate a human-friendly interface that could support direct querying as well as the creation of training and learning materials. The dual capability—powerful data organization on the back end and accessible, intuitive querying on the front end—became a defining characteristic of Sana’s platform. The learning component was then integrated to leverage the same data for onboarding, training, and professional development.
The learning features include quizzes, polls, interactive sessions, and other formats that can be tailored to the organization’s needs. Importantly, Sana’s approach positions learning as an outcome of information interaction rather than as a separate, isolated process. When interactive Q&A sessions are conducted around webinars or other events, the outputs can be captured and fed back into the knowledge base to improve future learning content and to update the information store. This creates a continuous improvement loop where information management and learning reinforce each other, enhancing both knowledge accuracy and learning effectiveness over time.
The product’s architecture supports this integrated approach by offering a scalable AI-enabled engine for organizing information and a versatile front-end for human interaction. The engine handles ingestion, classification, linking, and update propagation as data evolves, while the front end provides natural-language search capabilities and a means to design, deliver, and manage learning content. The combined effect is intended to deliver a more holistic, productive experience for employees, helping them find relevant information quickly and turn it into practical knowledge that supports their everyday work and long-term skill development.
Hellermark emphasizes that Sana’s focus on the enterprise differentiates it from consumer-oriented AI and traditional learning platforms. For enterprises, the value proposition lies in enabling employees to locate and apply relevant information across a distributed and often fragmented technology landscape, while simultaneously supporting the creation and distribution of targeted learning content that aligns with organizational goals. The platform’s ability to unify data, search, and learning into a single workflow is the core differentiator, offering a path to improved productivity, faster onboarding, and more effective professional development programs.
Engagement metrics and workforce impact
A standout aspect of Sana’s narrative is its assertion of continuous usage rather than episodic engagement. The company contends that Sana is used on a regular basis by tens of thousands of employees across roughly 100 organizations, with both weekly and daily active usage. This contrasts with typical e-learning platforms that often see more sporadic usage patterns. The implication is that Sana’s cross-functional capabilities—knowledge discovery, immediate access to information, and integrated learning—create a habit-forming experience that sustains ongoing engagement. In practice, this means that employees are not only retrieving information but also leveraging the platform to learn in context, during the course of their work, rather than postponing learning for a dedicated training session.
The scale at which Sana operates is notable. The platform is described as being used by a large and diverse set of employees across more than 20 countries, illustrating the company’s international reach and its ability to handle multilingual, cross-border enterprise needs. The breadth of customers and geographies speaks to Sana’s capability to adapt its platform to different regulatory environments, languages, and workflows, while maintaining a consistent user experience. The company’s emphasis on real-world usage metrics provides potential customers with a compelling picture of how the platform might perform within their own organizations, while also signaling to investors that the product demonstrates durable value in everyday operations.
From a product management perspective, these engagement patterns are informative for understanding Sana’s product-market fit. The combination of knowledge management, search, and learning implies multiple touchpoints with the platform: data ingestion, knowledge retrieval, content creation, and learning delivery. Each touchpoint can contribute to different metrics—adoption rates, time-to-value for onboarding, completion rates for learning modules, quality of information as reflected in verified sources, and the effectiveness of knowledge discovery as measured by improvements in decision-making and productivity. Sana’s ability to demonstrate high-frequency usage across a broad base of users, across a large number of organizations, provides a narrative of product resilience and relevance in complex enterprise environments.
In addition to user engagement, Sana’s technology stack and product architecture enable the platform to scale with customer needs. The AI models, guided by OpenAI’s technology, are designed to support domain-specific adaptation, allowing customers to tailor the system to their industry vocabulary, processes, and governance requirements. The scalability aspect is critical for enterprise buyers who need solutions that can grow with their organizations, support an expanding user base, and handle increasingly large volumes of data without sacrificing performance or reliability. Sana emphasizes that the platform’s front-end experience, which translates natural language queries into precise, actionable results, contributes to sustainable usage by lowering friction and increasing the perceived value of the system in daily tasks.
Investor commentary on engagement emphasizes the practical impact of the platform. A key point raised by a lead investor is the perception that Sana is building a true, end-to-end knowledge management solution from the ground up, designed for the knowledge economy. This investor notes that the distributed nature of modern enterprises makes it essential to enable all employees to access, contribute to, and learn from the organization’s knowledge base. The professional culture of modern companies—where teams are dispersed, collaboration is distributed, and the pace of innovation is rapid—creates a need for tools that can unify information and learning. Sana’s platform, in this view, addresses that need by offering a comprehensive solution that supports both knowledge discovery and content creation in a way that is more agile and participatory than traditional systems.
The technology stack and OpenAI partnership
Sana’s technical backbone centers on a multi-faceted approach to AI-driven knowledge management and learning. The platform’s data ingestion and organization are complemented by an AI engine that can interpret complex queries and surface relevant information across a sprawling data landscape. The company asserts that while its platform provides the customization and orchestration required to support enterprise needs, the underlying AI models come from OpenAI. Sana describes a deep, ongoing partnership with OpenAI, highlighting that the models have been in continuous use since before the product launch. This collaboration is presented as a critical element enabling Sana to deliver the advanced capabilities that differentiate its offering.
The strategic use of OpenAI’s models, including GPT, is framed as a foundation for scalable, domain-specific AI experiences. Sana contends that underlying models from OpenAI will continue to exist and evolve, with opportunities to fine-tune them for specific domains and organizational contexts. For Sana, the focus is not merely on embedding generic AI capabilities but on delivering a refined user experience that resonates with the needs of enterprise knowledge workers. This approach involves layering domain-specific customization, governance controls, and user-centric interfaces on top of powerful language models to produce practical outcomes in information retrieval and learning.
Hellermark emphasizes that the real value lies in the user experience built on top of the foundational AI. The platform’s success, in his view, will come from how well it can adapt OpenAI’s capabilities to the unique needs of an organization—how easily employees can ask questions in natural language and receive accurate, contextually relevant results, and how learning content can be generated, updated, and aligned with the organization’s policies and practices. This emphasis on UX over raw capabilities reflects a broader trend in enterprise AI, where robust models must be integrated into workflows in a way that feels native, reliable, and practical to everyday users.
From a product strategy standpoint, Sana’s approach to leveraging OpenAI models with domain-specific tuning aligns with the industry’s movement toward adaptable AI systems. Enterprises seek AI that understands their industry jargon, workflow patterns, and governance requirements, and that can be tuned to reflect the organization’s standards. Sana’s strategy recognizes this need and positions the platform as a vehicle to deliver both generalized AI benefits and specialized, domain-aware capabilities. The combination of a strong AI core with enterprise-grade customization and governance is designed to meet the demanding requirements of large organizations, including security, data residency, compliance, and operational reliability.
Why Sana chose enterprise learning and the scalability story
Founder and CEO Hellermark describes a deliberate focus on professional development as the core target for Sana’s enterprise learning strategy. He explains that education encompasses a broad spectrum—from early education for younger students to higher education and adult learning. Sana’s decision to concentrate on professional development—a subset of education tailored to the needs of employees and organizations—stems from both practicality and scalability considerations. The rationale is twofold: first, there is a significant, under-served market for enterprise tools that manage and unlock the value of an organization’s internal knowledge; second, enterprise learning is especially scalable because it can be standardized across the workforce, integrated into daily workflows, and deployed across multiple jurisdictions.
Hellermark argues that while the education sector as a whole is a meaningful frontier, solving learning in a K-12 setting presents substantial challenges related to country-by-country variations, regulatory differences, and curriculum diversity. The enterprise route offers a more tractable path to scale: standardized content, governance, and deployment across many teams and departments, with a common platform for knowledge capture and learning. The enterprise model affords the ability to align content with organizational goals and performance metrics, making it easier to measure outcomes, justify ROI, and iterate on product features based on concrete business needs. This scalability is particularly important in today’s environment, where startups and their investors prioritize business models with clear unit economics, a verifiable customer base, and proven technology that can deliver measurable value.
Hellermark also points to the practical advantage of focusing on enterprise users who work with large volumes of information and need to make it accessible to many employees across functions. In scenarios such as hospitals, engineering teams, sales organizations, or product management groups, the ability to unify disparate sources of information and to deliver learning modules that help employees perform more effectively is highly compelling. The capacity to serve professionals—from doctors to engineers, product managers to sales representatives—in more than 20 countries demonstrates the platform’s global reach and its ability to adapt to diverse regulatory and operational contexts. This breadth reinforces Sana’s stance that enterprise learning, supported by robust knowledge management and search functionality, is the most viable path to scale for a company of Sana’s size and ambition.
The enterprise focus also aligns with broader industry signals that emphasize AI-enabled transformation across corporate functions. In a world where teams operate with distributed data sources and where the pace of innovation puts pressure on employees to learn rapidly, a platform that merges information access and learning can yield significant productivity gains. Sana’s emphasis on an integrated approach—supporting information discovery, collaboration, and learning within a single ecosystem—speaks to a practical, results-oriented vision for how AI should augment human work. The company’s messaging around scale, governance, and domain-specific AI underscores the belief that enterprise-grade AI can be both powerful and responsible, delivering tangible benefits without sacrificing control or reliability.
Hellermark’s long-standing belief in the power of education to drive broader societal impact is tempered by a pragmatic understanding that education, in its traditional forms, is difficult to standardize and implement at scale on a global basis. By contrast, the enterprise learning model is presented as scalable, repeatable, and adaptable to a wide range of industries and job functions. The rationale rests on the idea that solving learning within the enterprise—where information is concentrated, workflows are well-defined, and the workforce is connected through digital tools—can deliver transformative results for companies seeking to improve performance, reduce training costs, and accelerate the adoption of new processes and technologies. The strategy is to build a platform that serves as a central knowledge hub while also enabling targeted learning experiences that directly tie to business objectives and performance metrics.
Sana’s approach suggests a broader ambition: to become a vertical platform for enterprise knowledge, search, and learning that can be deployed across multiple sectors and geographies. The focus on professional development aligns with the needs of a modern workforce that requires ongoing upskilling to stay current with technology, regulatory changes, and evolving best practices. The platform’s ability to connect to a variety of tools used within organizations, then translate that information into actionable knowledge and learning content, positions Sana as a comprehensive solution for knowledge work. The combination of this focus with a scalable and flexible AI backbone lays a foundation for future growth, international expansion, and the potential to serve as a backbone for digital transformation initiatives across industries.
Notably, Sana’s leadership envisions continued expansion beyond its initial markets and client base. The enterprise orientation is seen not only as a route to revenue growth but also as a way to create durable value through improved information governance, standardized training, and a more efficient knowledge ecosystem within organizations. The company’s strategy emphasizes practical outcomes and measurable impact, with a product that can continuously evolve to meet the changing needs of enterprise customers. By focusing on the intersection of knowledge management, search, and learning, Sana is positioning itself to offer a comprehensive platform that aligns with the priorities of CIOs, CHROs, and other senior leaders responsible for information governance, workforce development, and digital transformation initiatives.
Data governance, quality, and verifiability in AI-driven knowledge
One of the more nuanced aspects of Sana’s platform is its approach to the quality and verification of information that the system sources and presents. The company acknowledges that AI systems can produce outputs that are not always correct or consistent, and it emphasizes the importance of incorporating a structure for verification into the platform. This structure enables organizations to specify which information sources are reliable and to designate how the system should handle unverified data. Users can be granted access to unverified information if appropriate, or restricted to only verified content, depending on organizational policies and risk tolerance. The ability to rank information, designate verified sources, and control input channels is presented as a governance framework designed to mitigate the risk of inaccuracies while still leveraging AI to streamline knowledge retrieval and learning.
Hellermark notes that the verification framework is a core component of Sana’s knowledge management approach. He explains that while the platform may include models that perform search or information retrieval, there must be a deliberate mechanism that takes into account the need to verify knowledge and to create guided journeys for users. The verification structure is described as a "built-in" feature of the system, not an afterthought, and it is designed to provide a path for users and administrators to define how information is sourced, verified, and presented. This approach recognizes the persistent challenge in AI regarding accuracy and reliability and offers a practical way to address it within enterprise contexts.
That said, the team is candid about the ongoing nature of accuracy issues in AI. The industry’s historical pattern shows that AI reasoning and data processing can produce inaccuracies or inconsistencies, and the question of how to handle such outcomes remains a central discussion in AI governance. Sana notes that, for now, accuracy concerns have not impeded its growth. The company’s governance framework, combined with a flexible interface for data source selection and content verification, is intended to give customers control over the trustworthiness of the information they rely on, while still benefiting from AI-assisted discovery and learning. The challenge of achieving perfect accuracy in AI is acknowledged, but Sana positions its verification structure as a practical compromise that preserves value generation while acknowledging the limitations of current AI capabilities.
Industry observers, including venture partners, have offered positive assessments of Sana’s approach to knowledge management, verification, and learning. One investor who leads the round has stated that Sana is building a genuine knowledge management solution from the ground up, designed for the distributed, information-rich environment of modern companies. The investor highlights that in an era where organizations are more decentralized and must do more with less, enabling all employees to access and contribute to the knowledge base is crucial. This perspective reinforces the view that Sana’s platform addresses a fundamental organizational need—balancing AI-enabled knowledge access with governance and human oversight—to support scalable, data-driven decision-making and learning across the workforce.
The investor’s belief in Sana’s differentiators also centers on the concept of an “organizational knowledge graph”—the idea that content coupled with its provenance, authorship, and verified sources can be connected in meaningful ways to support both information retrieval and content creation. In practical terms, when prospects encounter Sana’s product, they see not only the AI-powered search capabilities but also the platform’s ability to support content authors and learners simultaneously. The experience highlights how the product’s design enables more engagement with the information and increases the perceived value of the platform. The investor argues that this combination—powerful AI with a robust content creation experience and a transparent governance framework—creates a level of engagement and extensibility that is difficult to match with traditional tools.
Market implications, organizational knowledge graphs, and the path forward
The enterprise AI space is evolving rapidly, with many players delivering components or standalone solutions for knowledge management, search, and learning. Sana’s claim to unique value resides in its integrated approach that binds these three areas together around a single data model, a unified user experience, and a governance framework designed for enterprise environments. The platform’s ability to connect to a broad set of common workplace applications—a list that includes Salesforce, email, Notion, GitHub, Slack, Trello, and Asana—positions Sana to be central to how information flows across an organization. The aim is to reduce the friction associated with information retrieval, improve the speed and relevance of search results, and create learning content that is closely aligned with real-world workflows and organizational needs.
From a market perspective, Sana’s positioning underscores a broader trend toward “knowledge economy” tools that emphasize the importance of turning scattered data into actionable intelligence. The platform’s combination of knowledge management, search, and e-learning aligns with enterprise needs for efficiency, accuracy, and continuous upskilling. As companies navigate a landscape of increasing data complexity, a unified approach to managing, discovering, and learning from information can provide a competitive advantage. Sana’s strategy reflects a belief that the future of enterprise software lies in platforms that can harmonize data governance with practical learning outcomes, enabling organizations to derive value from their knowledge assets while maintaining control over content quality and information provenance.
The investment and growth plan associated with Sana’s Series B are intended to accelerate the company’s ability to scale across markets and industries. The leadership team has signaled a desire to expand the product’s reach, deepen its integration capabilities, and advance its domain-specific AI offerings. The platform’s architecture, designed to support a diverse array of data sources and learning formats, should enable Sana to address a broader spectrum of enterprise use cases—from onboarding and compliance training to product development and customer-facing enablement. As Sana continues to scale, its focus on governance, verification, and user experience will be crucial to sustaining trust in AI-enabled enterprise tools and in demonstrating measurable value to customers and investors alike.
The broader implications for the AI-enabled enterprise software market include continued emphasis on integrated platforms that span multiple functions. As AI becomes embedded in day-to-day workflows, organizations will increasingly seek systems that enable both efficient retrieval of information and scalable learning. Sana’s model illustrates how a single platform can deliver both capabilities while maintaining governance controls and domain-specific adaptability. The Series B round not only provides capital for growth but also signals to prospective customers and partners that Sana is pursuing a long-term strategy to become a central layer in enterprise knowledge work.
Customer impact, real-world use, and long-term potential
For customer organizations, Sana’s platform promises tangible operational benefits. By unifying information across the tools employees already use, the platform can shorten time-to-answer for internal queries, reduce the cognitive load associated with switching between applications, and ensure that learning content reflects the most current information. Onboarding new hires can be accelerated as new employees are guided through learning paths that are closely tied to the organization’s actual data and workflows, helping them become productive faster. For existing staff, continuous professional development can be delivered in a just-in-time manner, leveraging information that is already present in the enterprise’s knowledge base, enabling more relevant and timely training.
In practice, the platform’s learning modules can be created by internal teams to reflect company procedures, standards, and best practices, or they can be configured or augmented by Sana to align with industry-specific requirements. The learning content is not an isolated product; it is derived from and anchored to the organization’s knowledge resources, ensuring that the training materials are relevant and up-to-date. The ability to update content in response to changes in policies, processes, or market conditions means that learning programs can remain current, delivering ongoing value rather than becoming quickly outdated.
The potential impact across industries is broad. Knowledge-intensive sectors such as healthcare, engineering, software development, and sales organizations stand to benefit from enhanced knowledge accessibility and more effective learning programs. In these contexts, the ability to surface the right information at the right time and to convert that information into practical training can improve decision-making, enable safer and more compliant practices, and support professional growth. Sana’s platform is positioned to address these needs by delivering a unified experience that integrates information retrieval with targeted learning content, all underpinned by robust governance. As the company expands its international footprint, it will encounter new regulatory environments, languages, and industry norms, each presenting both challenges and opportunities for platform adaptation and value creation.
Conclusion
Sana Labs has positioned itself at the intersection of knowledge management, enterprise search, and e-learning, presenting a unified AI-driven platform that aims to transform how organizations capture, access, and learn from information. The recent Series B funding round, led by Menlo Ventures with participation from EQT Ventures and a broad group of angels, underscores strong market validation and a belief that Sana’s approach can scale across industries and countries. With a post-money valuation of $180 million and ARR growth that has accelerated seven-fold in the past year, Sana is establishing a foundation for sustained growth and broader adoption in the enterprise AI space.
The platform’s architecture—capable of ingesting data from a wide array of workplace apps, organizing it automatically, and allowing users to query information in natural language while also enabling dynamic, production-grade learning content—addresses core enterprise needs. The strategic partnership with OpenAI provides a powerful AI core, while Sana’s emphasis on domain-specific tuning, governance, and an intuitive user experience positions the product to deliver real value in everyday work. Hellermark’s focus on professional development and scalable enterprise learning as the mission for Sana reflects a thoughtful response to market realities: education in the enterprise can be a significant lever for productivity, innovation, and competitive advantage when paired with governance and a strong AI backbone.
In a market that increasingly demands integrated, scalable, and responsible AI solutions, Sana’s approach offers a compelling blueprint for how knowledge work can evolve. By uniting knowledge management, enterprise search, and learning into a single, cohesive platform, Sana seeks to unlock the latent value of an organization’s information, empower employees with on-demand learning tied to real-world data, and provide governance mechanisms that ensure accuracy, trust, and compliance. As the company scales, its success will depend on maintaining a superior user experience, continuing to refine domain-specific AI capabilities, expanding the ecosystem of integrated tools, and delivering measurable outcomes that demonstrate the platform’s transformative potential for knowledge work in the modern enterprise.