Topsort T-brain

Give commerce a
shared understanding.

T-brain is the commerce foundation under every Topsort decision: shared models and embeddings instead of a separate partial view per surface.

Talk to salesAI Commerce Intelligence
/Overview

What T-brain
does.

Turn commerce data into better decisions across ranking, prediction and auctions, using models that learn from shopper behavior, product relationships and real outcomes.

01Commerce embeddings

Shared vector representations of products, queries and shoppers.

02Relevance models

Ranking that understands intent instead of matching keywords.

03Low-latency inference

Predictions returned inside the auction, not after the page renders.

04Feedback ingestion

Every click, purchase and outcome improves the next decision.

05Cross-surface reuse

The same understanding powers search, ads, recommendations and offsite.

06Developer access

Models and signals exposed through APIs your own surfaces can call.

Commerce embeddings
/Architecture

One intelligence layer under every surface.

Search, ads, recommendations and offsite read from the same understanding, so decisions stay consistent wherever a shopper meets your catalog.

01

Understand intent

Learns from products, queries, shopper behavior and transactions.

02

Predict outcomes

Uses real commerce signals to predict relevance and likely performance.

03

Decide in real time

Ranks and selects the best outcome with low latency inference.

04

Learn continuously

Every click, purchase and outcome improves the next decision.

/Demand

Better decisions compound when the system keeps learning.

Shared models mean every surface benefits from the same signals, and every outcome makes the next decision sharper for shoppers, advertisers and the retailer.

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Embeddings
Shared representations of products, queries and shoppers
Relevance
Ranking grounded in intent instead of keyword matching
Inference
Predictions returned inside the auction in real time
Feedback
Outcomes fed back into the next decision automatically
Reuse
One understanding across search, ads and recommendations
/Evaluation

What to ask any retail media platform.

Whether you are comparing vendors or weighing an internal build, these are the questions that decide the outcome.

01Does search understand the same shopper your ads do?
02How many separate models describe the same catalog today?
03Where does relevance break down between surfaces?
04How quickly does a new signal reach production ranking?
05Who owns the models behind your commerce decisions?
06Can your team call those models from their own surfaces?
/How it works

From raw commerce data to decisions in real time.

Every step feeds the next, so relevance keeps improving as the network runs.

Step 01

Understand intent

Learns from products, queries, shopper behavior and transactions.

Step 02

Predict outcomes

Uses real commerce signals to predict relevance and likely performance.

Step 03

Decide in real time

Ranks and selects the best outcome with low latency inference.

Step 04

Learn continuously

Every click, purchase and outcome improves the next decision.