Quant Researcher

From raw signal to production strategy without leaving your notebook.

Zerve's agent understands your data universe, builds reproducible research pipelines, and operationalizes strategies — so you can focus on finding alpha, not managing infrastructure.

Get started free Talk to sales

Data DiscoveryInstitutional MemoryDAG Notebooks

Data Discovery

Research that replicates every time.

DAG-based notebooks capture every step, every parameter, and every output. Run the same experiment six months later and get the same result.

BigQueryConnected

37 tables · 4 schemas

✦Agent mapped 37 tables. Detected 5 join paths and 3 high-signal tables for churn prediction.

37Tables

4Schemas

5Join paths

97%Avg quality

analytics.customers

2.4M rows24 cols

98%

analytics.orders

8.1M rows18 cols

99%

ml.churn_features

2.4M rows32 cols

95%

sessions.events

41M rows11 cols

97%

Join paths detected

customersorderson customer_id

orderseventson session_id

Institutional Memory

Backtest today. Deploy tomorrow.

Research and production run in the same environment. Load any model, dataset, or signal with a single import and go live instantly — no hand-off to engineering required.

AI Agent

In context

Zerve Agent

I can see your Q2 churn analysis — same feature set, 48K enterprise accounts, AUC-ROC 0.891. Apply the same methodology to Q3?

Yes. Exclude accounts that already churned in Q2 from the baseline.

Zerve Agent

Adjusted. 46,892 accounts in scope. Reusing Q2 feature engineering — login frequency, ticket count, MRR delta.

Q3 churn rate: 7.1%, down from 8.3%. Support ticket signal strengthened.

What changed between quarters?

Zerve Agent

SLA compliance improved 18% in Q3. Accounts with 3+ tickets resolved within 4h show 2.1× lower churn. That's new signal since Q2.

Ask anything about your data...

DAG Notebooks

Run a thousand experiments, not just ten.

Zerve's spread() function distributes parameter sweeps, walk-forward windows, and universe variations across parallel compute instantly. Gather results when they're done.

Customer Churn Analysis

Run All

load_data1.5s · λ

1import zerve, pandas as pd

2df = zerve.query("SELECT * FROM analytics.customers")

3print(f"Loaded {len(df):,} rows — {df.churned.mean():.1%} churn rate")

segment_by_tier1.8s · λ

1segments = df.groupby("pricing_tier")

2 .agg(churn_rate=("churned","mean"), avg_mrr=("mrr","mean"))

3print(segments.head())

build_churn_model3.5s · λ

1from sklearn.ensemble import GradientBoostingClassifier

2model = GradientBoostingClassifier(n_estimators=200, max_depth=4)

3model.fit(X_train, y_train)

4print(f"AUC-ROC: {roc_auc_score(y_test, model.predict_proba(X_test)[:,1]):.3f}")

deploy_model4.2s · λ

1endpoint = zerve.deploy(model=model, name="churn-prediction-v2",

2 instance="standard-2cpu", autoscale=True)

3print(f"Live at {endpoint.url}")

Ready to build

One environment. Every step from data to deployment.

Start building free Talk to sales