Kumo launches KumoRFM-2, the first foundation model to outperform machine learning on enterprise data, scaling to 500 billion rows. Published April 14, 2026 MOUNTAIN VIEW, Calif., April 14, 2026 /PRNewswire/ - Kumo, a leader in predictive AI, today announced its launch of KumoRFM-2, the first foundation model to outperform fully supervised machine learning on enterprise relational data. Built by the team that created PyTorch Geometric (the most widely used library for graph machine learning, with 23,700+ GitHub stars and 1.2M+ monthly PyPI downloads) KumoRFM-2 replaces months of feature engineering and dedicated model builds with a single model that any team can query in plain English. It requires zero training, and scales to 500 billion+ rows of data. The implications are significant: predictions that previously required PhD-level data scientists, 3 to 6 months of feature engineering, and a custom-trained model for every predictive task can now be generated instantly by anyone in the organization. On Stanford RelBenchV1, KumoRFM-2 outperforms its predecessor by 10% and surpasses the strongest supervised machine learning model by 5% across both classification and regression tasks. On the SAP SALT enterprise benchmark, KumoRFM-2 achieves state of the art results by surpassing tabular model ensembles such as AutoGluon as well as recent tabular foundation models by a wide margin. Performance further improves by 13% upon fine-tuning. "Kumo.ai has transformed how we approach lead scoring at Databricks. Since deploying their platform, we've seen conversion rates from leads to opportunities improve from 1.2x to 6x, and we've doubled the volume of high-intent, quality leads entering our pipeline. The impact on our marketing performance has been substantial," said Anoop Muraleedharan, Sr Director Data & Analytics, Databricks. Every current approach to predictive AI on enterprise data faces the same fundamental problem: the most valuable predictive signal lives in the relationships across multiple tables in a data warehouse, but every existing tool, including LLMs, XGBoost, and tabular foundation models, destroys those relationships by flattening multi-table data into a single table before modeling even begins. KumoRFM-2 is the only foundation model that preserves these relationships natively, working directly on the graph of connected tables without flattening. Built on a new Relational Graph Transformer architecture published at ICLR 2026, the model processes data at 5 GB/sec with 20 million lookups per second, and delivers predictions across industries. "Enterprise data - customer records, transactions, product catalogs - holds enormous untapped revenue potential. Until now, using that data to generate business predictions required months of feature engineering and deep data science expertise, putting it out of reach for most teams," said Dr. Vanja Josifovski, Co-Founder and CEO at Kumo. "KumoRFM-2 changes that: it's the only model that actually understands the relationships across your tables instead of destroying them, it scales to hundreds of billions of rows, and it lets any team ask predictive questions in natural language. No feature engineering. No data science expertise required." "For years, AI has been constrained by a fundamental limitation of not being able to reason over structured enterprise data. Database is not a document, it is a graph of relationships," said Dr. Jure Leskovec, Co-Founder and Chief Scientist at Kumo. "KumoRFM-2 is the first model that sees the full graph. We developed Relational Graph Transformers, where the AI model can attend to any datapoint, preserving the complete structure of relational data at arbitrary scale. And by adding a natural language interface, we make it possible for teams across the organization to ask not just what happened, but what will happen next, and why." KumoRFM-2 was developed by a founding team with more than two decades of experience shaping modern machine learning and deploying AI at scale. The leadership team includes Co-Founder and CEO Dr. Vanja Josifovski, former CTO of Airbnb and Pinterest, who has extensive experience scaling AI systems for hundreds of millions of users; Co-Founder and Chief Scientist Dr. Jure Leskovec, a Stanford professor and pioneer of relational deep learning whose work underpins KumoRFM-2's architecture; and Co-Founder and Head of Engineering Dr. Hema Raghavan, former Senior Director of Engineering at LinkedIn, who leads the company's engineering and product execution, bringing cutting-edge research into enterprise-ready systems. KumoRFM-2 key breakthroughs include: * First foundation model to outperform task-specific supervised ML models. Across 41 predictive tasks on four major benchmark suites, KumoRFM-2 is the first few-shot foundation model to surpass task-specific supervised approaches on common benchmark tasks. It outperforms the best single-table foundation model, which operates on a single flat table without relational context, by 18%, LLM-based approaches by more than 10%, and the best supervised relational models by 1.5%, effectively automating the "Data Scientist" role in the feature engineering pipeline. * Zero training required with extraordinary data efficiency. KumoRFM-2 achieves state-of-the-art results through in-context learning alone. No task-specific training, no feature engineering, no model building. The model is remarkably data-efficient, using as little as 0.2% of the labeled data that supervised approaches require (context examples vs. full training sets), making it dramatically faster and more practical than any existing approach. * Scales to 500 billion+ rows. While KumoRFM 1 was limited to small-scale in-memory datasets, KumoRFM-2 scales to billion-scale relational databases. A custom graph engine with database connectors pushes computation directly to the data layer, building a memory-mapped data structure enabling 5 GB/sec and 20 million lookups/sec for low-latency inference and fine-tuning at production scale. KumoRFM-2 connects directly to SQL databases and cloud data warehouses, including Snowflake, Databricks, and Spark. * 89% accuracy on SAP SALT, improving state-of-the-art by 13%. On the SAP SALT benchmark, which reflects real-world ERP data with approximately 5 million records, KumoRFM-2 achieves 0.89 MRR when fine-tuned, surpassing giant tabular model ensembles like AutoGluon (0.77) and the best-performing baseline CARTE (0.79) with a single model. * The only model that works on both single-table and multi-table data. KumoRFM-2 is the only foundation model that operates natively on both single-table and multi-table structured data. Every competing approach requires flattening multi-table data into a single table, destroying the cross-table relationships that are the most valuable predictive signal in enterprise data. * New architecture. This new architecture replaces Graph Neural Networks (which are limited to local neighborhoods and lose information across hops) with a transformer-based approach that preserves the ability to attend across row, column, foreign key, and cross-sample dimensions. This eliminates the information bottleneck of message-passing architectures while scaling to large context sizes. * Natural-language interface and agent-ready design. Users can ask predictive questions across hundreds of use cases and receive predictions in plain language with explanations of the factors that influenced each result. The system translates natural language into Kumo's Predictive Query Language (PQL), a structured intermediate representation that also serves as a composable primitive for AI agents, enabling predictive modeling to be stacked and enriched with retrieved information. * Robustness to noise, missing data, and structural degradation. Ablation studies show KumoRFM-2 maintains high accuracy even under extreme conditions: only a 6% performance drop at high feature-dropout levels (compared to 17% for single-table models), stable accuracy even when 75% of relational edges are removed, and essentially constant performance under heavy injection of noisy columns. By aggregating information across the relational graph, the model effectively "fills in" missing information from neighboring entities and tables. * Pre-trained on synthetic and real-world data with zero leakage. KumoRFM-2 is pre-trained on an expanded combination of synthetic data and real-world relational databases. The model has not seen any of the evaluation datasets during pre-training, guaranteeing no leakage of information. Pre-training progresses in multiple stages, transitioning from simpler tabular settings to more complex relational structures. The company is backed by Sequoia Capital. Kumo's investor and advisor network includes Frank Slootman (Snowflake Board), Sridhar Ramaswamy (CEO, Snowflake), Ben Silbermann (Founder, Pinterest), Matei Zaharia (CTO & Co-Founder, Databricks), Tristan Handy (CEO, dbt Labs), and more than 20 additional leaders from Discord, Amazon, Apple, and leading venture firms. About Kumo Kumo is the creator of KumoRFM, the first foundation model built for structured business data. Pre-trained on billions of relational patterns, KumoRFM delivers zero-shot predictions on enterprise data with no training or feature engineering required. Founded by Dr. Vanja Josifovski (former CTO of Airbnb and Pinterest), Dr. Jure Leskovec (Stanford Professor, pioneer of Relational Deep Learning, former Chief Scientist at Pinterest), and Dr. Hema Raghavan (former AI lead at LinkedIn, Inc. 's 2026 Female Founders 500). Kumo's team created PyTorch Geometric (23,700+ GitHub stars, 21M+ downloads) and has published foundational research at NeurIPS, ICML, and ICLR. Backed by Sequoia Capital. Deployed in production at DoorDash, Snowflake, Databricks, Reddit, Coinbase, and Sainsbury's. To learn more, visit kumo.ai.

Kumo has launched KumoRFM-2, a foundation model that outperforms traditional machine learning on enterprise relational data, scaling to 500 billion rows. The model achieved state-of-the-art results across 41 predictive tasks and four major benchmarks without requiring feature engineering or task-specific training. Built on a new Relational Graph Transformer architecture, KumoRFM-2 preserves relationships across multiple database tables rather than flattening them. It processes data at 5 GB per second with 20 million lookups per second and allows users to query predictions in natural language. On Stanford RelBenchV1, it outperformed its predecessor by 10% and supervised machine learning models by 5%. Founded by the team behind PyTorch Geometric, Kumo is backed by Sequoia Capital and deployed at DoorDash, Snowflake, Databricks, Reddit and Coinbase.

Inc. names kumo Co-Founder, Dr. Hema Raghavan, to its 2026 Female Founders 500 list. Mar 12, 2026, 16:19 ET Inc.'s annual Female Founders list celebrates the nation's most innovative women entrepreneurs, who collectively generated approximately $12.3 billion in 2025 MOUNTAIN VIEW, Calif., March 12, 2026 /PRNewswire/ - Kumo is proud to announce that Dr. Hema Raghavan, Co-Founder and Head of Engineering, has been named to Inc.'s 2026 Female Founders 500, an annual list honoring the most dynamic women business leaders in the United States. The honor recognizes founders whose bold ideas, resilience, and execution are shaping the future of their industries. Each year, Inc. editors evaluate applications through a rigorous, multi-round selection process. Founders are assessed on both quantitative performance metrics, including revenue growth, funding, sales, and audience size, as well as qualitative factors such as innovation, social impact, and brand momentum. Previous honorees have included such game-changing leaders as Billie Jean King, Sallie Krawcheck, Serena Williams, and Emma Grede, all of whom have transformed their industries and broken barriers along the way. This year, the list was judged by fellow women in leadership positions, including the co-founders and CEOs of New American Funding and Care.com, among others. "Being named to this list is deeply meaningful to me, not just as a founder, but as a woman in deep tech and as a role model to my children," said Dr. Hema Raghavan, Co-Founder and Head of Engineering at Kumo. "When I co-founded Kumo, I knew we were working on a hard problem, one that the industry had largely written off as unsolvable. This recognition is a reminder that there is space for women to lead at the frontier of technical innovation, and I hope it encourages others to keep building." Dr. Raghavan co-founded Kumo in 2021 after identifying a fundamental gap in enterprise AI: every company sits on years of relational data, customers, transactions, behaviors, yet extracting predictive insight from it still required months of manual work by specialized teams. Drawing on her decade of experience leading AI at LinkedIn, where she built the systems behind People You May Know and helped scale the platform from 400 million to 700 million users, she set out to change that. The result is KumoRFM, the first foundation model built specifically for structured enterprise data, enabling zero-shot predictions directly from a data warehouse with no task-specific training required. The technical breakthrough was considered counterintuitive. Prevailing wisdom held that structured data lacked the sequential patterns necessary for transformer architectures. Dr. Raghavan and her team demonstrated otherwise, and the results speak for themselves. Kumo's platform now operates in production at over 20 enterprises, including DoorDash, Reddit, Snowflake, and Databricks, generating predictions for more than one billion users globally. Customers have seen 142% more accurate fraud detection, 5.4x conversion rate improvements, and over $100 million in revenue impact, with model development that is 20x faster than traditional approaches. Kumo has raised $37 million in funding led by Sequoia Capital. "Each year, we are increasingly amazed by the extraordinary leaders on our Inc. Female Founders 500 list," says Bonny Ghosh, editorial director at Inc. "The honorees on this year's list include innovators in AI, beauty and wellness trendsetters winning devoted fans, and nonprofit leaders making a real impact in their communities. Together, they're showing all of us what trailblazing female leadership looks like." Several honorees will be featured in Inc. magazine's Spring print issue, on newsstands March 17, 2026. To see the complete list of honorees, please visit: https://www.inc.com/female-founders/2026. About Inc. Inc. is the leading media brand and playbook for the entrepreneurs and business leaders shaping our future. Through its journalism, Inc. aims to inform, educate, and elevate the profile of its community: the risk-takers, the innovators, and the ultra-driven go-getters who are creating the future of business. Inc. is published by Mansueto Ventures LLC, along with fellow leading business publication Fast Company. For more information, visit www.inc.com. About Kumo Kumo gives companies the ability to extract real value from their business data - transactions, customers, inventory - that sits in the data warehouse but is often constrained by outdated AI techniques and complex feature engineering. Kumo's platform uses Relational Graph Transformers to work directly on relational data, enabling teams to train accurate predictive and embedding models up to 20x faster with no feature engineering. Teams can also use the Kumo Foundational Model for instant predictions and real-time decisioning. Kumo was founded by three PhDs who held executive leadership and academic roles at Pinterest, Airbnb, LinkedIn, and Stanford. The company has raised $37 million and is backed by Sequoia Capital. Kumo makes relational data directly usable for high-quality predictive and generative AI, helping businesses of all sizes apply advanced AI more easily. To learn more, visit kumo.ai. SOURCE Kumo.AI