From raw data to insight, without the friction

sciFi is an AI-powered Python notebook workspace for data science and analysis — notebooks, code execution, and an AI copilot in one focused environment.

Why we built it

Data work today is fragmented. Data scientists and analysts bounce between CSVs, notebooks, scripts and chat tools just to answer one question — whether that question is an exploratory analysis, a trained model, or a single business metric.

Every one of those context switches costs something. You lose the shape of the data you had in your head, you re-import the same libraries, you paste code between windows and lose track of which version actually ran. The analysis itself is rarely the hard part; the surrounding friction is.

sciFi removes that friction by putting Python execution, AI assistance and data exploration into one workspace. No setup. No context switching.

What you can do

• Upload and explore data. Drop in CSVs and structured files and explore them immediately — schema, distributions and nulls at a glance.
• Write Python with a copilot. Generate, refine and debug analysis in plain English, and get real, editable code back.
• Work in a notebook. A full notebook environment with the standard data stack already installed — no virtualenvs, no dependency conflicts.
• Move faster. Go from question to result without leaving the tab.

How we think about AI

AI should assist, not replace. sciFi keeps you in control of both your data and your code. The copilot suggests analysis paths, explains errors and results, and takes the repetitive work off your hands — but every line it writes is yours to read, change, or throw away.

That principle has a practical consequence: we generate code, not conclusions. A notebook you can read and re-run is auditable in a way that a chat answer never is, and in most of the domains our users work in — finance, healthcare, research — that difference matters more than convenience.

Who it's for

Data scientists, analysts, ML practitioners, engineers, researchers and learners — anyone who works with Python and data.

Community

We run Kaggle-style hackathons on the platform. Our first, a Healthcare AI Hackathon on detecting anomalies in ICU patient vital signs, drew 213 participants and over 700 notebook submissions. A Fintech AI Hackathon is opening for registration shortly.

We also publish practitioner writing on the blog — small language models, regulated ML, and the engineering that decides whether a model ever reaches production.

We also build models with you

Alongside the platform we take on custom model work for fintech teams — building and fine-tuning AI models for fraud detection, credit risk, document extraction and forecasting. The engagement ends with weights, training code and an eval harness your team owns outright, not a service you rent from us.

Backing

sciFi is supported by the NVIDIA Inception Program, the Google for Startups Cloud Program, and the Wadhwani Foundation.