Choose a product
Choose AutoML for machine-learning models. Choose Reproducible Analytics Studio for statistical analysis, tables, figures, and reports.
Compare the two products, look at real examples, and choose the next step. AutoML is open now; Reproducible Analytics Studio starts with a fit conversation.
Start with the plain-language fit below. The detailed product pages explain the technical terms and evidence when you need them.
| Product | Best fit | Access status | Access and pricing | Next step |
|---|---|---|---|---|
| Xalec AutoML Services Automated machine learning |
For predicting or classifying an outcome from your data. Upload data, check it, train and compare models, see why results change, then review and save reports. | Available for self-service registration. | Plans and paid actions use credits. The platform shows current prices and an estimate before paid work starts. | Open AutoML platform → Ask a question about AutoML → |
| Reproducible Analytics Studio Statistical analysis and reporting |
For statistical analysis, tables, figures, and reports. Check the data, explore it, create source tables, review results, and export a report package. | RAS workspace access is currently by request. | Early access is currently offered through scoped pilots. Plan and usage details are confirmed during onboarding. | View product overview → Discuss RAS access → |
You can open AutoML now. Reproducible Analytics Studio is currently available by request, so we can understand the work, confirm the setup, and discuss pricing before access is granted.
Choose AutoML for machine-learning models. Choose Reproducible Analytics Studio for statistical analysis, tables, figures, and reports.
Look through a completed run or statistical-analysis example to see the checks, outputs, and reports each product produces.
Open AutoML directly. For Reproducible Analytics Studio, send a short note about your workflow so Xalec AI can confirm fit, onboarding, and pricing.
Exact checkout and usage estimates live in the product workspaces. Use these materials to understand the product before you create an AutoML account or request access to a request-based product.
Open the completed AutoML run, the Reproducible Analytics Studio example, or the product docs. You can see the checks, outputs, and reports involved before starting a conversation.
Tell us the product, dataset type, workflow, expected outputs, and timeline. A specific starting point makes it easier to decide whether a pilot or early-access path is realistic.
Discuss request-based access →For team or institution use, discuss privacy, retention, secure transfer, support expectations, and whether you need a dedicated setup before sending sensitive material.
Review security basics →This company site helps you compare products. AutoML and Reproducible Analytics Studio keep live uploads, saved jobs, pricing details, and support inside their own workspaces.
Review security, privacy, responsible disclosure, and legal basics before sharing data. Product-specific data handling should be confirmed in the relevant product workflow before live customer data is used.
A useful first note includes the product, dataset type, workflow you want to improve, whether the work is individual or team-based, and any security or timeline constraints. If your work sits outside AutoML or Reproducible Analytics Studio, we can discuss whether it should be scoped separately.