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DYNAMIC PRICING

Price what is happening now.

Chalk serves pricing models fresh demand, supply, and behavioral features at inference-time. Increase revenue and optimize demand with real signals at the moment price is decided.

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Why Chalk

Reduce your pipeline complexity

Chalk resolves features with data from streams, databases, and APIs at query time, serving them in single-digit milliseconds.

Ship new features faster

Define features in Python or SQL, and deploy on your own cadence. Turo cut new feature delivery from 3 weeks to 1 week.

Serve fresher data at inference

Compute features when the model runs - and make real-time signals available for pricing and the decisions that depend on what is happening now.


Reduce your pipeline complexity

Chalk resolves features with data from streams, databases, and APIs at query time, serving them in single-digit milliseconds.

Ship new features faster

Define features in Python or SQL, and deploy on your own cadence. Turo cut new feature delivery from 3 weeks to 1 week.

Serve fresher data at inference

Compute features when the model runs - and make real-time signals available for pricing and the decisions that depend on what is happening now.


Build models that drive revenue

The first model Medely deployed on Chalk, a charge-rate recommender, added $800K in annual net revenue.

Improve model performance

Iterate with feedback models from development to production in days, not quarters. Apartment List cut model deployment from weeks to 1-2 days, creating more time for pricing experiments.

Run experiments faster

Shorten the path from hypothesis to production. Turo cut feature development and experiment cycles by 67%, giving the team more opportunities to test and improve pricing.

Marketplace Pricing

Supply and demand shift by neighborhood and by hour.

Turo computes host pricing recommendations from live marketplace conditions across 300,000+ vehicles in five countries - and ships new pricing features in a week instead of three.

Rate recommendations

Features resolve in real time at inference

Medely recommends charge rates for healthcare shifts using current demand, fill rates, and market conditions. Features that arrived on a 24-hour batch lag now resolve in real time at inference. The result: $800K in projected annual net revenue from the first model.

Price flexibility and ranking

Models deliver personalized results

Apartment List powers price flexibility and apartment ranking models with sub-10ms dynamic recommendation queries. The models deliver personalized results while the renter is still searching, on features retrieved in under 5ms.

Real-time risk features in single-digit millisecond latency

from chalk import online

@online
def get_username(email: User.email) -> User.username:
    username = email.split("@")[0]
    if "gmail.com" in email:
        username = username.split("+")[0].replace(".", "")
    return username.lower()

@features
class Transaction:
    id: int
    amount: float
    user_id: "User.id"

Fetch device info, IP geolocation, velocity checks, and KYC scores at inference time. No batch lag, no stale features.

Read the Docs

How Medely's first model on Chalk added $800K in projected annual net revenue

Medely matches healthcare professionals with open shifts, and the charge rate on every shift is a pricing decision. Before Chalk, the features behind those decisions arrived on a 24-hour batch lag. Now they resolve in real time at inference, and experiment cycles have dropped from roughly two months toward two weeks.

READ STORY

The first product we ever deployed with Chalk paid for our team, probably more, in net revenue. Adopting Chalk is the biggest singular win I have had as an ML engineer at this company.

Eric SimonStaff Machine Learning Engineer

Bring us your pricing decisions that need fresh data

Every pricing team has a model waiting on batch data. Bring us yours. We'll show it scoring on live demand, supply, and behavior, running in your stack.

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