Orchestrate yourML Lifecyclewith Multi-Agent Precision.
Describe your ML goal. Our agents handle dataset search, EDA, cleaning, feature engineering, AutoML, and deployment - with you approving every critical decision.
The Pipeline
Eight agents. One seamless flow.
Understands your goal and plans the pipeline
Finds or uploads the perfect dataset
Profiles your data and detects issues
Fixes nulls, outliers, and imbalances
Engineers and selects the best features
Runs AutoML to find the best model
SHAP analysis, metrics, and bias check
Downloads package or deploys live API
Orchestrator
Understands your goal and plans the pipeline
Dataset
Finds or uploads the perfect dataset
EDA
Profiles your data and detects issues
Cleaning
Fixes nulls, outliers, and imbalances
Features
Engineers and selects the best features
Modeling
Runs AutoML to find the best model
Evaluation
SHAP analysis, metrics, and bias check
Deployment
Downloads package or deploys live API
Best model
Logistic Regression
Selected by AutoML
Accuracy
94.2%
On test set
Deployed in
8 mins
End to end
How It Works
From prompt to deployed model
in four steps.
Describe your goal
Type your ML goal in plain English - like 'Predict customer churn from my CSV'. Upload a dataset or let our agents find one for you.
→ No ML expertise required. Just describe what you want.
Agents go to work
8 specialized agents handle dataset search, profiling, cleaning, feature engineering, and AutoML training - fully automated.
→ Each stage logs every decision in real time.
You approve decisions
At 6 critical checkpoints, you review what the AI found and chose. Approve to continue, or guide the direction.
→ You're always in control. Nothing runs without your sign-off.
Deploy or download
Get a full report with SHAP explainability, metrics, and bias analysis. Then download your model package or deploy a live API.
→ Everything you need to use your model - in one ZIP.
Features
Everything you need.
Nothing you don't.
Built for tech students who want production-grade ML workflows without writing a single line of ML code.
AI Audit Trail
Every decision the AI makes is logged with plain-English reasoning. See exactly why features were dropped, which model was chosen, and how issues were handled.
Human-in-the-Loop
6 checkpoint gates give you full control. The pipeline hard-pauses and waits for your approval before any critical action is taken.
Smart AutoML
FLAML AutoML with adaptive time budgets. Small datasets get simple, fast models. Large datasets get heavy hitters. No one-size-fits-all.
SHAP Explainability
Global feature importance, beeswarm plots, and per-prediction explanations. Know exactly why your model makes every prediction.
Bias Detection
Automatic bias check across sensitive columns. Flags performance gaps above 10% between demographic groups before you deploy.
Ready-to-Run Package
Download a ZIP with model.pkl, scaler.pkl, predict.py, requirements.txt, and a step-by-step README. Run locally in minutes.
AES-256 Encryption
Your datasets are encrypted at upload and deleted after the pipeline completes. Only your trained model is kept.
Full EDA Charts
Automatic distributions, correlation heatmaps, class balance charts, and outlier boxplots - generated and saved in your report.
The Agents
8 specialists.
One pipeline.
Click any agent to learn what it does.
Orchestrator
AI AgentThe only true AI agent. Reads your goal, analyzes your dataset summary, and plans the entire pipeline. Uses Gemini Flash for consistent, low-cost reasoning.
The Report
Every insight.
In one place.
After your pipeline completes, get a full tabbed report with metrics, charts, SHAP explainability, AI decisions audit, and deployment options.
Evaluation Report
Logistic Regression (L1) · Classification · 800 train / 200 test
Accuracy
94.2%
F1 Score
0.931
ROC AUC
0.978
Precision
0.944
Top Features (SHAP)
AI Decisions Audit
Selected Logistic Regression over LightGBM — small dataset, gap < 2%
Dropped 4 features with mutual info score < 0.001
Detected class imbalance — minority 27%