Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Dynamic pricing is leaving money on the table. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict cancellation risk, pricing, and personalization. KumoRFM learns directly from the relationships in your data and is pre-trained on tens of thousands of datasets, delivering higher accuracy than any internally-built model, in hours, not months.
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Why travel companies choose Kumo
Here's how Kumo transforms travel and hospitality with relational AI.
28%
More accurate pricing predictions
KumoRFM learns from the full booking graph: guests, properties, seasons, events, competitor signals, and cancellation patterns. It captures pricing dynamics that spreadsheet-based revenue management misses.
10-50%
Better personalization accuracy
Pre-trained on thousands of relational datasets, KumoRFM understands guest preferences, loyalty patterns, and travel behaviors across the full guest journey, not just last-stay summaries.
Hours
To deploy new prediction models
Dynamic pricing, booking prediction, guest personalization, loyalty optimization, demand forecasting, and cancellation risk. Deploy them all from one platform without property-specific pipelines.
Loved by data scientists, ML engineers & CXOs at
Use cases
Every prediction your organization needs. from a single platform that learns directly from your connected travel data.
Optimize room rates, flight prices, and package deals in real time by learning from booking patterns, competitor signals, demand graphs, and seasonal relationship networks.
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Predict which travelers are most likely to book by understanding their relationships with destinations, past trips, loyalty tiers, and search behavior graphs.
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Deliver tailored experiences by learning from guest preference graphs — connecting past stays, dining choices, activity bookings, and feedback into a unified profile.
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Maximize loyalty program ROI by predicting redemption patterns, tier progression, and churn risk from member activity graphs and reward interaction networks.
Predict occupancy, flight load, and destination demand by learning from seasonal patterns, event calendars, booking lead-time graphs, and macroeconomic signals.
Identify high-risk bookings before cancellation by analyzing booking behavior graphs, payment patterns, and traveler history networks — enabling proactive overbooking strategies.
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Predict which guests will convert on upgrades, add-ons, and premium services by learning from purchase history graphs, traveler segments, and contextual signals.
Optimize fleet allocation, route scheduling, and capacity decisions by learning from origin-destination demand graphs, seasonal flow networks, and traveler connection patterns.
Predict guest satisfaction and review outcomes before checkout by analyzing service interaction graphs, complaint patterns, and experience quality signals across the guest journey.
The travel data advantage
Feature engineering destroys signal. Even with a great data science team, flattening relational tables into feature vectors discards the nuanced relationships that actually predict traveler behavior. Your travel data is inherently relational — guests connect to bookings, bookings to properties, properties to flights, flights to loyalty programs, loyalty programs to reviews, reviews to pricing history. That structure is the signal. This is a structural limitation of the approach, not a reflection of team quality.
You only have your data. KumoRFM is pre-trained on thousands of relational schemas. It already knows what churn, engagement, and conversion patterns look like across hundreds of different data structures. Your team — no matter how talented — can't replicate the pattern recognition that comes from learning across that many schemas.
Your existing team will love it. KumoRFM 10x's your data science team. Feature engineering disappears entirely. Companies go from 3-5 models per year to 50+ per quarter. The interesting work — defining business problems, interpreting results, driving strategy — remains.
One platform powers dynamic pricing, demand forecasting, guest personalization, cancellation prediction, and every other travel prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
10–25%
RevPAR improvement
Over traditional pricing models
20x
Faster to production
From months to hours
$2.4M
Average annual savings
Reduced ML infrastructure costs
KumoRFM isn't limited to your data alone. Pre-trained on billions of relational patterns across diverse datasets and fine-tuned to your schema, it sees what no in-house model can. As per the SAP SALT benchmark.
LLM
GPT4 + AutoML
PhD Data Scientist
Feature eng. + XGBoost
KumoRFM
Relational Foundation Model
40%
lift in prediction accuracy
“Beating internal XGBoost model on key metrics with far less data/features — on Kumo pre-trained. We replaced six months of pipeline work with a single afternoon.”
Matt Loskamp
GTM Data Science Leader, Enterprise Financial Customer
From startups to enterprises, leading organizations rely on Kumo to deliver predictive insights at scale.
Peer-reviewed
Kumo is built on 40+ peer-reviewed papers at NeurIPS, ICML, and KDD. The methodology is public and reproducible.
K. Huang, M. Fey, J. Leskovec et al.
A foundation model for relational data - pre-trained across schemas, it delivers accurate predictions out of the box and improves with fine-tuning on your specific data.
Read paperM. Fey, W. Hu, K. Huang, J. Leskovec et al.
Introduces learning predictive models directly on relational databases, eliminating the feature engineering pipeline that has historically bottlenecked enterprise ML.
Read paperJ. Robinson, R. Miao, K. Huang et al.
An open benchmark for evaluating relational prediction methods across 11 databases and 30 tasks. Kumo consistently outperforms traditional ML baselines.
Read paper
