BehaviorGPT-Commerce 1
Purchase and session sequences modeled as the language of consumption.
- Parameters
- 150M
The foundation model for behavior
Turning actions into real-time predictions.
Pre-trained foundation model for making predictions on behavioral data
No training required. Fine-tuning is optional.
import unbox
client = unbox.UnboxAI(api_key="your_prod_key")
# the customer's live transaction from the auth stream
def authorize(event):
# 1. Load the cardholder's transaction history
user = client.history.load(user_id=event.card_hash)
# 2. Score how likely this transaction is fraud
result = client.score(history=user, action=event)
# 3. Act on the Unbox intelligence score
if result.probability > 0.95:
return "DECLINED: BEHAVIORAL_ANOMALY"
return "APPROVED"import unbox
client = unbox.UnboxAI(api_key="your_prod_key")
# project a customer's value from their behavior
def lifetime_value(user_id):
# 1. Load the customer's full behavior history
user = client.history.load(user_id=user_id)
# 2. Predict expected value over the next 12 months
result = client.predict(history=user, target="clv_12m")
# 3. Act on the projected lifetime value
if result.value > 5000:
return "SEGMENT: HIGH_VALUE"
return "SEGMENT: STANDARD"
import unbox
client = unbox.UnboxAI(api_key="your_prod_key")
# assess default risk for a credit application
def underwrite(application):
# 1. Load the applicant's behavior history
user = client.history.load(user_id=application.user_id)
# 2. Score the probability of default
result = client.score(history=user, action=application)
# 3. Act on the Unbox risk score
if result.probability > 0.30:
return "DECISION: DECLINE"
return "DECISION: APPROVE"
import unbox
client = unbox.UnboxAI(api_key="your_prod_key")
# rank the next-best items for a live shopper
def recommend(session):
# 1. Load the shopper's behavior history
user = client.history.load(user_id=session.user_id)
# 2. Rank the catalog by predicted intent
result = client.rank(history=user, candidates=session.catalog)
# 3. Return the top items to surface
return result.top(k=10)
import unbox
client = unbox.UnboxAI(api_key="your_prod_key")
# re-rank search results by behavioral intent
def search(query, session):
# 1. Load the user's behavior history
user = client.history.load(user_id=session.user_id)
# 2. Retrieve and rank results for the query
result = client.search(history=user, query=query)
# 3. Return the ranked results
return result.top(k=20)
One recipe, each generation trained on a new domain
BehaviorGPT-Workforce 1
Employee action sequences, predicting workforce dynamics from behavior rather than surveys.
- Parameters
- 5M
BehaviorGPT-Commerce 2
The second commerce generation, learning taste from what people do rather than from pixels.
- Parameters
- 0.5B
BehaviorGPT-V4
One backbone unifying behavior across retail and payments, transferring zero-shot across domains.
- Parameters
- 12.5B
FAQs
What is BehaviorGPT?
BehaviorGPT is a foundation model for human behavior. It learns directly from sequences of actions such as purchases, searches, and clicks, with no hand-engineered features. One pretrained model transfers across catalogs, companies, and tasks.
How is this different from a recommendation engine?
A recommender scores items against a user profile or matches similar users. BehaviorGPT models the sequence itself, so earlier actions change how the next one is read. That context lets it identify intent instead of scoring each action in isolation.
Can BehaviorGPT be used for fraud detection?
Yes. The same action can be routine or suspicious depending on what preceded it, and BehaviorGPT models that sequence directly. It scores risk in real time as a session unfolds, and the model that powers recommendations adapts to fraud and risk without retraining from scratch.
What data does it need?
Chronological event streams: transactions, searches, sessions, and interactions. No labels or engineered features are required.
Is it available as an API or product yet?
Yes, for enterprise partners today. Broader access is still in early research release. Join the waitlist and we will be in touch as capacity opens.
When will I get access after joining the waitlist?
We onboard participants gradually and will email you as access opens for your use case. There is no fixed date yet.
Is there a technical paper or documentation?
Yes. Our papers and documentation are on the research page.