Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500

Personalization & Recommendations

Personalization, Recommendations & Search Ranking

Move beyond collaborative filtering. Kumo learns from the full relational graph — purchases, views, returns, reviews, and session context — to deliver recommendations that reflect genuine individual preference.

Product RecsContent PersonalizationSearch RankingNext Best OfferEmail OptimizationSend-TimeNotificationsCold-Start

Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.

By submitting, you accept the Terms and Privacy Policy.

Prediction types

Recs to send-time

3.2x

Accuracy lift

vs. collaborative filtering

<1 hr

To production

Per use case

Cold-start issues

Works on new items

Loved by data scientists, ML engineers & CXOs at

8 personalization use cases

Every touchpoint. Personalized.

From product pages to push notifications — one relational foundation model powers every personalization surface.

Product recommendations

Learn from the full relational graph - purchases, views, returns, reviews - producing recommendations that reflect genuine preference.

Explore

Content personalization

Personalize content feeds, articles, and media by learning from interaction patterns across users and content items.

Explore

Search ranking

Re-rank search results using relational context - what similar users clicked, purchased, and returned.

Explore

Next best offer

Predict the most relevant offer for each customer based on purchase history, preferences, and behavior context.

Explore

Email personalization

Optimize email content, timing, and product selection per recipient using relational engagement signals.

Explore

New collection launch recs

Recommend newly launched items to users based on purchase history and relational context - even with zero interaction data on the new products.

Explore

Notification reranking

Rerank push notifications and in-app messages by predicted engagement - which message, for which user, at which moment.

Explore

Send-time optimization

Predict the optimal send time for each user based on historical open and conversion patterns across channels.

Explore

Traditional recs vs. Kumo

See how relational learning compares to collaborative filtering and content-based approaches.

Traditional

With Kumo

Data used

User-item interaction matrix only

Full relational graph across all tables

Cold-start items

No interactions = no recs

Graph context from similar items & attributes

Feature engineering

Weeks per model

Zero — automated from relational data

Real-time context

Batch-only, stale features

Session, recency, and graph signals

Cross-domain recs

Separate models per surface

One model across all surfaces

Setup time

3–6 months

Under 1 hour

Data used

Traditional

User-item interaction matrix only

With Kumo

Full relational graph across all tables

Cold-start items

Traditional

No interactions = no recs

With Kumo

Graph context from similar items & attributes

Feature engineering

Traditional

Weeks per model

With Kumo

Zero — automated from relational data

Real-time context

Traditional

Batch-only, stale features

With Kumo

Session, recency, and graph signals

Cross-domain recs

Traditional

Separate models per surface

With Kumo

One model across all surfaces

Setup time

Traditional

3–6 months

With Kumo

Under 1 hour

How It Works

Simply connect your data, start asking predictions, and get results.Want more control? Fine-tune the model for your specific use case.

STEP 1

Connect your data

Integrates directly with your warehouse, no additional pipeline setup.

STEP 2

Ask a predictive question

Ask questions in plain English and let Kumo do the modeling for you.

STEP 3

Act on predictions

Get clear predictions and push them instantly into your workflows.

STEP 1

Connect your data

Integrates directly with your warehouse, no additional pipeline setup.

STEP 2

Ask a predictive question

Ask questions in plain English and let Kumo do the modeling for you.

STEP 3

Act on predictions

Get clear predictions and push them instantly into your workflows.

churn_prediction.pql

PREDICT COUNT(transactions.*, 0, 90, days) = 0
FOR EACH customers.customer_id
WHERE COUNT(transactions.*, -60, 0, days) > 0

3 lines. No feature engineering. No pipeline code.

For developers

Predict in a few lines of SQL

Kumo's Predictive Query Language (PQL) replaces months of feature engineering, model training, and pipeline work with a few lines of SQL-like syntax. Describe what you want to predict — Kumo handles the rest.

PQL docs|Python SDK