Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Every basis point of ad targeting accuracy translates directly to revenue. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict click-through, conversion, and attribution. 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 ad tech companies choose Kumo
Here's how Kumo transforms ad tech with relational AI.
5.4x
Better conversion prediction
KumoRFM learns from the full advertiser-publisher-user-impression-conversion graph. It captures cross-device journeys, frequency effects, and creative-audience interactions that flat click models miss.
30%
Lower cost per acquisition
Pre-trained on thousands of relational schemas, KumoRFM understands behavioral patterns across impression, click, and conversion data. Better targeting means fewer wasted impressions and more efficient spend.
Minutes
To deploy new targeting models
CTR prediction, bid optimization, audience segmentation, attribution modeling, campaign forecasting. One platform replaces the custom pipeline you'd otherwise build for each model.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
From click-through rate prediction to viewability forecasting, KumoRFM powers every ad tech prediction from the same connected data. no per-model pipelines, no feature engineering.
Predict CTR with dramatically higher accuracy by learning from the full graph of user behavior, ad creative attributes, placement context, and historical conversion patterns — not hand-engineered feature tables.
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Optimize real-time bidding by modeling the relationships between audience segments, inventory quality, competitive dynamics, and conversion likelihood in a single relational graph.
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Build richer audience segments by learning from the full relational structure of user behavior, content affinity, purchase signals, and cross-device activity — not just demographic buckets.
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Move beyond last-click attribution by modeling the full graph of touchpoints, channels, and conversion paths — capturing the true multi-touch relationships that drive conversions.
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Predict which creative elements will resonate with specific audience segments by learning from the relational patterns between creative attributes, user preferences, and engagement outcomes.
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Optimize impression frequency per user by modeling the relationship between exposure count, engagement decay, and conversion probability across channels and time windows.
Build higher-converting lookalike audiences by leveraging relational signals — shared behaviors, content affinities, and network connections — instead of surface-level demographic matching.
Allocate budgets across campaigns, channels, and audiences by predicting marginal returns from the full graph of historical performance, audience overlap, and competitive dynamics.
Predict ad viewability before bidding by modeling the relationships between placement context, page structure, user scroll behavior, and device characteristics in real time.
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The ad data advantage
Even with a world-class data science team, feature engineering fundamentally caps your accuracy. The moment you flatten relational tables into feature vectors, you discard the nuanced relationships between impressions, clicks, conversions, audiences, campaigns, creatives, and publishers. This isn't a team quality problem — it's a structural limitation of traditional ML that no amount of hiring solves.
KumoRFM is pre-trained on thousands of relational schemas. It has already learned what conversion, click-through, and attribution patterns look like across hundreds of data structures — the same advantage GPT has over custom NLP. Your team cannot replicate this breadth no matter how much time you give them. Foundation model scale changes the game.
KumoRFM doesn't replace your data science team — it 10x's them. They go from shipping 3–5 models per year to 50+ per quarter. Tedious feature engineering disappears; the interesting work — defining predictions, interpreting results, driving business impact — remains.
One platform powers CTR prediction, bid optimization, attribution, audience segmentation, and every other ad tech prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
5–15%
CTR improvement
Over traditional prediction models
20x
Faster to production
From months to hours
3–5x
ROAS improvement
Return on ad spend uplift
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
