Orchestrate yourML Lifecyclewith Multi-Agent Precision.
Nine specialized AI agents transform your objective and dataset into a validated, explainable and exportable machine-learning workflow-with you controlling every critical decision.
The Orchestration Interface
Watch the pipeline unfold.
Why traditional AutoML isn't enough.
Black-box automation hides critical data issues. OrchestraML builds trust through explicit agent decisions and human oversight.
Traditional AutoML
- Hidden preprocessing decisions
- Weak leakage protection
- Metrics without context
- Limited human control
- Difficult-to-reproduce outputs
OrchestraML
- Explicit agent decisions
- Dataset risk validation
- Human approval at critical stages
- Trust-aware model evaluation
- Complete audit trail and export bundle
Agents
Nine specialized agents.
A precise, multi-agent lifecycle that transforms raw data into a trustworthy pipeline.
Orchestrator
Pipeline Planner
Dataset Intelligence
Data Sourcing
Data Guardrails
Risk Detection
EDA
Data Profiling
Cleaning
Data Preparation
Feature Engineering
Signal Extraction
Modeling
AutoML Training
Evaluation
Model Analysis
Deployment
Output Generation
"Understands the objective, target, task type, risks and success metric."
Orchestrator
Pipeline Planner
Understands the objective, target, task type, risks and success metric.
Dataset Intelligence
Data Sourcing
Data Guardrails
Risk Detection
EDA
Data Profiling
Cleaning
Data Preparation
Feature Engineering
Signal Extraction
Modeling
AutoML Training
Evaluation
Model Analysis
Deployment
Output Generation
Human-in-the-Loop
Automation where it helps.
Human judgment where it matters.
OrchestraML never hides critical decisions. At critical stages, the pipeline pauses. You review what the agents found, their reasoning, and their recommended action.
Review Examples
Confirm pipeline plan
Select a dataset
Review dataset risks
Confirm target and columns
Approve feature strategy
Select model or Auto Select
Review model results
Choose export action
Risk Assessment
Risk Score
84/100
Recommended drops: 3
Review required: 2
Target Leakage Detected
Column status_updated_at perfectly predicts target.
Identifier Column
Column customer_id has 100% unique values.
AI Recommendation
Drop all leakage and identifier columns before proceeding to EDA to prevent model overfitting.
Dataset Guardrails
Catch fatal errors before they train.
Traditional AutoML blindly trains on garbage data. Our dedicated Guardrails agent intercepts datasets and scans for modeling risks.
Evidence-Driven Features
Features validated by math, not just vibes.
Our upgraded Feature Agent doesn't accept features simply because they look statistically interesting. It builds a cleaned baseline, engineers candidates, and uses cross-validation to prove actual model gain.
Validation Process
Features Accepted
14
Out of 42 candidates
Features Rejected
28
Failed stability test
Actual Model Gain
+2.4%
Over cleaned baseline
Predictive Strength
High
Mutual Info > 0.05
Stability Analysis
Trust-Aware Evaluation
Beyond accuracy.
Accuracy alone doesn't mean a model is safe to deploy. OrchestraML translates technical performance into Reliability, Limitations, Generalization, Business meaning, and Monitoring requirements.
Model Trust Score
Comprehensive assessment of deployment readiness
* The Trust Score is an analytical tool, not a guarantee of real-world safety.
The Experience
Four steps to production.
Describe the objective
Type your ML goal in plain English. The Orchestrator agent plans the pipeline and identifies the target task.
Provide or discover a dataset
Upload a CSV, or let the Dataset agent automatically find a suitable dataset from Hugging Face or Kaggle.
Review agent decisions
The pipeline pauses at critical stages. You review risks, approve feature strategies, and select the final model.
Download the completed model package
Get a reproducible zip containing your model, preprocessor, predict script, and full PDF report.
The Report
Every insight.
In one place.
After your pipeline completes, get a full tabbed report with dataset risk analysis, feature validation, metrics, SHAP explainability, and a complete AI audit trail. Then download a reproducible model bundle.
Build a customer churn prediction model
Selected Champion Model
LightGBM
ID: lgbmValidation Split
Train: 5,634 | Test: 1,409
Features Supplied
28 Variables
Production Bundle
A complete, reproducible zip package ready for deployment.
model.pkl
Trained estimator
preprocessor.pkl
Fitted transformations
predict.py
Ready-to-use inference script
requirements.txt
Exact dependencies
sample_input.json
Test payload schema
report.pdf
Exported pipeline report
Winning Validation Score
0.9338
Metric: Accuracy
Training Time
120.1s
Budget used: 120s
Optimization Goal
Balanced
AutoML search objective
Validation Framework
Winning Hyperparameters
Auditability
Every decision leaves a trail.
From the first dataset scan to the final model selection, every agent recommendation and user approval is recorded.
Pipeline Audit Log
Data Guardrails
Risk Detection
Flagged 'SSN' as PII. Recommended drop.
EDA
Profiling
Identified severe class imbalance (92:8).
Cleaning
Preparation
Applied SMOTE to balance minority class.
Modeling
AutoML
Selected Random Forest (ROC: 0.94) over XGBoost (ROC: 0.93) due to faster inference.
Turn your ML objective into a trusted pipeline.
Start with an objective and a dataset. OrchestraML coordinates the rest-with you in control.