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sciFi is an AI-powered Python notebook workspace. Upload your data, describe what you want, and get runnable analysis — no environment setup, no context switching.
Which customers are most likely to churn next month?
from sklearn.ensemble import GradientBoostingClassifier model = GradientBoostingClassifier().fit(X_train, y_train) risk = model.predict_proba(customers)[:, 1] customers.assign(churn_risk=risk).nlargest(5, "churn_risk")
| customer_id | plan | churn_risk |
|---|---|---|
| C-10482 | Pro | 0.94 |
| C-77310 | Team | 0.91 |
| C-20915 | Pro | 0.86 |
| C-48822 | Starter | 0.82 |
| C-31207 | Team | 0.79 |
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Product demo
A walkthrough of the workspace — data analysis, an ML pipeline, and building or fine-tuning a model, end to end.
sciFi · product walkthrough
Data analysis, ML pipelines, and fine-tuning AI models
Features
Data work is fragmented — CSVs here, notebooks there, a chat window on the side. sciFi puts Python execution, AI assistance and data exploration in one place.
Drop in CSVs and structured files and start exploring immediately — schema, distributions and nulls at a glance.
Generate, refine and debug analysis in plain English. The copilot writes real, editable code — never a black box.
A full notebook environment with pandas, scikit-learn and the rest already installed. No venvs, no dependency hell.
Visualise results inline and ask follow-up questions about what a chart is actually telling you.
Publish any notebook as a link. Teammates read the code, the output and the reasoning — not a screenshot.
EDA, a trained model or a business metric — go from question to result without leaving the tab.
How it works
Upload a CSV or structured file. sciFi profiles it and loads it into a live Python session for you.
Describe the analysis you want. The copilot proposes an approach and writes the Python to carry it out.
Execute cells, tweak the code yourself, then publish the notebook as a link your team can open.
AI should assist, not replace. sciFi keeps you in control of both your data and your code — it suggests analysis paths, explains errors and results, and takes the repetitive work off your hands. Every line it writes is yours to read, change or throw away.
Work with us
Beyond the notebook platform, we take on custom model work for fintech teams — the problems where imbalanced data, hard latency budgets and explainability requirements all bite at once.
Fraud detection, credit risk scorecards, document extraction and forecasting — built on your data and evaluated against your metrics.
Learn moreMove a large model’s behaviour into a small one you can self-host — cheaper per call, faster at the tail, inside your own network.
Learn moreEval harnesses, grounding validators and the documentation a model validation committee will actually sign off.
Learn moreYou keep the weights, the training code and the eval harness — no black box.
Hackathons
Kaggle-style challenges, run end to end inside sciFi notebooks — a shared dataset, a live leaderboard, and every submission a real, runnable notebook.
Builders competed to detect anomalies in ICU patient vital signs — a noisy, heavily imbalanced time-series problem where a missed anomaly is what actually costs you.
Our next challenge moves to financial data — the problems where class imbalance, latency budgets and explainability all bite at once, and a good AUC is not the same thing as a good model.
Registration opens shortly. Tell us you want in and we’ll email you the moment it does.
Notify meOpens your mail app — or write to lucky@scifi.ink
Want to run a hackathon for your team or campus on sciFi? Get in touch.
See the workspace on your own data. Fifteen minutes, no slides.