What does OctOpus mean? Where does the name come from?

OctOpus = Octo + Opus. Octo refers to the 8-armed octopus — parallel experiments running concurrently. Opus refers to Claude Opus, Anthropic's most capable reasoning model, which directs every experiment decision. Pronounced like the animal (OCK-toh-pus). The domain is octoopus.dev (two 'o's) so 'octo + opus' reads cleanly.

OctOpus is the first autonomous AI data scientist. You upload a CSV, describe your goal in plain language, and an AI agent autonomously profiles the data, picks the right models, writes the training code, runs multiple experiments, diagnoses its own failures, validates the winner on unseen data, and deploys a production-ready prediction API — in minutes, not weeks. It owns the full research loop a senior data scientist runs by hand.

Why is OctOpus called the first autonomous AI data scientist?

Existing tools either generate code when a human drives them (Cursor, Claude Code, ChatGPT) or run constrained grid-search AutoML inside a fixed workflow (DataRobot, H2O, Google AutoML). OctOpus is the first system to own the entire data-science research loop end-to-end — hypothesis, experiment, diagnose, revise — without a human in the loop. It writes a fresh train.py per experiment, rotates model families intelligently, recovers from errors with targeted fixes, learns from past runs via meta-learning priors, validates on a holdout the LLM never sees, and deploys the winner.

How is OctOpus different from AutoML and AI coding tools?

AutoML platforms (DataRobot, H2O, Google AutoML) automate model selection inside a fixed workflow — they don't run the research loop around it. AI coding tools (Cursor, Claude Code, ChatGPT) generate code only when a human drives them — the human is still the data scientist. OctOpus is autonomous: it plans, writes code, runs experiments, reads errors and metrics, diagnoses what failed, changes strategy, and tries again, until it ships a model. It writes a custom train.py for every experiment, rotates model families intelligently, verifies tier compliance at the AST level, and scores every winner on a held-out slice the LLM never sees.

What does OctOpus cost?

Free tier: 3 runs/month. Pro: $20/mo for 40 runs. Pro+: $60/mo for 150 runs plus bring-your-own API keys. Team: from $80/mo (2 seats min, $40/seat). Enterprise: custom (includes desktop app, SSO, private deployment).

Which ML models does OctOpus use?

LightGBM, XGBoost, CatBoost, scikit-learn, TabPFN, TabNet, FT-Transformer for tabular. NeuralForecast (NBEATS, PatchTST, xLSTM, TFT), Chronos, TiRex, TimesFM for time series. HuggingFace Transformers for NLP. The AI picks the right family per dataset and rotates through tiers: baseline GBM → tuned GBM → deep/modern → foundation model → stacking ensemble.

Is OctOpus safe for enterprise data?

Yes. Multi-tenant isolation is audited end-to-end. User API keys are encrypted at rest with Fernet. Subprocess execution scrubs provider credentials from the environment. All log streams pass through a secret-redaction layer. Holdout data lives outside the workspace where the LLM cannot see it. Enterprise plan adds local data residency via the desktop app, SSO/SCIM, and private/VPC deployment.

Can I talk to OctOpus? Does it have a voice feature?

Yes. OctOpus has a real-time voice mode — tap the mic and talk to it hands-free. Ask questions about your data, launch a research run by saying 'go', steer experiments mid-run, and hear the agent answer back out loud (speech to speech). When low-latency realtime voice isn't available it falls back to the browser's built-in speech so the conversation never breaks. Live voice is currently free for everyone to try. Learn more at /voice-ai-data-scientist.

Can I integrate OctOpus with Claude Code or Cursor?

Yes. OctOpus ships an MCP (Model Context Protocol) server that exposes profile_csv, discover_direction, start_research_run, get_run_status, get_artifact_url, and bootstrap_project tools to any MCP-compatible agent — Claude Code, Cursor, and others. See the .mcp.json in the repo.

What is the best DataRobot alternative?

OctOpus is a modern DataRobot alternative built for the agent era. Where DataRobot runs constrained model selection inside a fixed, license-heavy workflow, OctOpus is an autonomous AI data scientist: it profiles your data, writes a fresh train.py per experiment, rotates model families (GBM → tuned GBM → deep tabular → foundation model → stacking), diagnoses its own failures, validates the winner on a holdout the LLM never sees, and deploys a prediction API — all from a plain-language goal. There is a free tier (3 runs/month) so you can evaluate it without sales calls or seat licenses.

Is there a free alternative to H2O.ai, DataRobot, or Driverless AI?

Yes — OctOpus has a free tier with 3 full autonomous runs per month, no credit card and no enterprise sales process. Unlike H2O Driverless AI or DataRobot, which gate AutoML behind seat licenses, OctOpus lets you upload a CSV, state your goal in plain English, and get a validated, deployable model in minutes. Paid plans start at $20/month for 40 runs.

What is a good alternative to Amazon SageMaker Autopilot, Google Vertex AI, or Azure AutoML?

OctOpus is a cloud-agnostic alternative to SageMaker Autopilot, Vertex AI, and Azure AutoML. The cloud AutoML services run grid/HPO search inside their own platform and assume you already know the ML workflow. OctOpus owns the whole research loop autonomously — it picks the task, writes the code, runs and diagnoses experiments, and ships a downloadable model.pkl plus a prediction API you can host anywhere. No cloud lock-in, and a free tier to start.

What is the best no-code AutoML tool or AutoML alternative?

OctOpus is a no-code AutoML alternative that goes beyond classic AutoML. Traditional AutoML automates model and hyperparameter selection but still needs a data scientist to frame the problem, engineer features, and deploy. OctOpus is autonomous and agentic: drop in a dataset, describe the outcome you want in plain language, and the AI agent handles profiling, feature engineering, model rotation across LightGBM/XGBoost/CatBoost/TabPFN/foundation models, validation, and deployment end-to-end.

OctOpus — Your AI Données Scientist

OctOpus Your AI Données Scientist

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OctOpus AI Données Scientist

Autonomous data science from business goal to deployed model.

OctOpus AI Données Scientist turns a dataset and a plain-language goal into a complete data science workflow: profile the data, understand the target, write the plan, run experiments, compare models, deploy the best model, and generate a decision-ready report.

Drop a dataset, say your goal in plain language.

What OctOpus does

Connect data from CSV, Excel, warehouses, or local files.
Understand the prediction, forecasting, or dashboard goal.
Exécuter ML experiments, validate on holdout data, and ship artifacts.

Who uses it

Données scientists who need faster research loops.
Analysts, consultants, and operators with business questions.
chercheurs and enterprise ML teams that need auditability.

High-intent use cases

Customer churn prediction and lead scoring.
Revenue forecasting and demand planning.
Fraud detection, anomaly detection, and risk modeling.

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