Accelerate Data Science, Intelligently.

Avenlo combines a familiar Jupyter-style notebook with an AI agent that can inspect files, write code, run cells, and explain the work as it goes.

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
df = pd.read_csv("customers.csv")
monthly_churn = df.groupby(["month", "plan_type"])["churn"].mean().reset_index()
monthly_churn .pivot( index="month", columns="plan_type", values="churn") * 100
Monthly churn risk by cohort

Predicted churn rate (Enterprise vs Self-serve)

-48%
risk_scores = model.predict_proba(features)[:, 1]
top_drivers = explain_top_drivers( model, features, n=5)
onboarding_risk_ratio = 2.1
12s

AI summary

Self-serve churn trends down from 23% to 12% across the period, and customers without onboarding completion show about 2.1x higher predicted risk.

  • Prioritize onboarding completion before the first renewal cycle for at-risk self-serve accounts.
  • Track weekly active usage and account age as the top monitoring signals.
Transport: Connected | Session: Active

Real-world workflow

Describe your task. Get the notebook.

Avenlo turns a technical request into a complete Jupyter-style notebook: code, runs, plots, diagnostics, and written conclusions.

Chat Context
churn_embeddings.ipynb
We shipped a new embedding model. Compare it against the current churn model, check for leakage, evaluate lift and calibration, then create a notebook I can send to the growth team.
AI answer
I will inspect the notebook, run a churn model, and add the most useful diagnostic chart and conclusion back into the notebook.
3 tool calls
Read notebook
Run cell
Update cell +18 -12
Strongest signals: onboarding completion, account age, and weekly active usage.
Generating
Ask about the notebook...
New Chat

Delivered notebook

analysis/churn_embeddings.ipynb

Complete
01 Creates a clean notebook plan with assumptions and evaluation criteria
02 Loads experiment data, validates schema, and checks leakage
03 Builds a baseline model, then compares stronger candidates
04 Adds charts for lift, calibration, feature importance, and error cases
05 Writes a markdown conclusion with next experiments

Built for serious analysis

The AI notebook workspace for quantitative work.

One cloud workspace for notebooks, data, experiments, and autonomous agents that can work through hard mathematical, statistical, and ML problems with you.

01

Describe the objective

02

Agent works through cells

03

Keep the reproducible notebook

01

Hand off hard problems

Agents turn research questions into notebook work.

Give Avenlo a serious objective: prove a result numerically, explore a dataset, fit a model, debug an experiment, or produce a chart. The agent plans the work, writes cells, runs them, reads outputs, and keeps going while you stay in control.

  • Plans multi-step mathematical, statistical, and ML workflows
  • Writes Python directly into executable notebook cells
  • Runs experiments and adapts when outputs or errors change the path
Agent workspace Running

Prompt

Test whether the new embedding features improve churn prediction and explain the result.

01 Loaded feature matrix
02 Compared baseline and boosted models
03 Writing interpretation cell Running
02

One cloud workspace

All your notebooks, files, outputs, and agents in one place.

Avenlo is a cloud notebook environment, like Colab for serious autonomous analysis. Your notebooks, datasets, model outputs, and agent conversations live together, so the assistant can understand the full project instead of a single prompt.

  • Organize analysis notebooks without local setup
  • Use uploaded files, prior cells, outputs, and chat as context
  • Let agents continue from the actual state of the workspace
Agent workspace Context loaded

Prompt

Use the uploaded experiment logs and summarize what changed across runs.

01 experiment_logs.csv
02 training_curves.ipynb
03 model_notes.md
03

Reproducible by default

The final answer is a notebook you can inspect.

For mathematicians, statisticians, physicists, and ML engineers, the reasoning matters as much as the conclusion. Avenlo leaves the code, plots, intermediate results, and markdown interpretation in the notebook so the work can be reviewed, edited, and rerun.

  • Keep generated code visible instead of buried in chat
  • Review plots, tables, assumptions, and explanations together
  • Share a reproducible artifact rather than a transient answer
Agent workspace Ready to review

Prompt

Turn this exploration into a clean notebook with assumptions, plots, and conclusions.

01 Methods section added
02 Diagnostic chart rendered
03 Conclusion cell written

Supercharge your workflow

Supercharge your data science workflow.

Keep your existing notebook flow, but add agents that can execute the heavy analytical lifting between your decisions.

01

Start from a real DS objective

Describe the modeling, experiment, or analysis goal in plain language, then set the metric and constraints that matter.

02

Let agents execute notebook work

Agents write and run cells, inspect outputs, use your files and context, and adapt the plan as new results arrive.

03

Review, iterate, and share quickly

Inspect code and charts, edit assumptions, rerun key cells, and ship a notebook artifact your team can trust.

Get started

Your AI notebook workspace awaits.

Work on data science, math, and ML problems with autonomous agents built directly into cloud notebooks.

Get Started Free