Xalorra | AI/ML

Stop shipping AI features you can’t audit six months later.

Lakehouse ops: query, transform, materialize, and profile datasets.

ML lifecycle: train, version, metrics/leaderboard, and predict.

RAG (Beta): gateway-based — models run elsewhere, lineage stays here.

A backend-first control plane for lakehouse, ML, and RAG — with multi-tenant lineage by design

Build AI systems that stay explainable from data to deployment.

Lakehouse operations (Core)

Query, transform, materialize, and profile datasets on a DuckDB + Parquet-native backbone.

ML lifecycle (Core)

Train and version models with clear dataset scope, metrics/leaderboards, and stable predict endpoints.

GenAI RAG gateway (Beta)

Index, retrieve, and assemble context with traceable runs — without hosting foundation models.

Control plane, not a black box

Xalorra keeps systems coherent: tenant isolation, namespace boundaries, versioned data + models, and lineage across lakehouse ops, ML runs, and RAG runs — behind stable HTTP contracts.

DuckDB-first lakehouse

Multi-tenant by default

Versioned datasets & models

Lineage & reproducible runs

Build with clarity.

Teams that build workflows

Promote model versions across dev/staging/prod with explicit stage control.

Keep every deployment decision auditable: version label, metrics, and artifacts in one place.

Move fast without losing the system truth behind “what is running”.

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Operate safely.

Teams that operate systems

Monitor predictions with actual-vs-predicted views tied to a dataset + model version.

Serve versioned models behind stable HTTP contracts and predictable resolution rules.

Reduce drift by grounding outputs in dataset versions and persisted artifacts.

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Govern without blind spots.

Teams that govern changes

Compare runs with explainability signals (e.g., feature importance) per model version.

Audit decisions with reproducible lineage — not screenshots and guesswork.

Keep controls enforceable as teams and tenants scale.

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Built for serious teams

If you’re tired of prototypes that die in production, this is the opposite: strict contracts, tenant-safe storage paths, and reproducible runs with lineage you can defend.

Platform

Predictable platform contracts

Stable endpoints for lakehouse ops, transforms, ML serving, and RAG tooling.

Security

Governance-ready by design

Tenant isolation, namespace boundaries, and consistent access patterns across the stack.

Repro

Versioned data, models, artifacts

Reproducibility without brittle notebooks and ad-hoc scripts.

Teams

Built for multi-role workflows

Data teams operate. ML teams iterate. Product teams ship — without losing system truth.

Built for clarity

A platform that stays coherent as you scale.

Lakehouse workflows, ML lifecycle, and RAG tooling — tied together by tenant isolation and lineage, not fragile glue code.

Lakehouse

Query fast, version everything

DuckDB-first analytics with dataset versioning and predictable artifacts. Keep work reproducible, not fragile.

Transforms

Shape data without breaking lineage

Transform and materialize datasets while keeping a clear chain of versions. Clean, strict, and debuggable.

RAG (Beta)

RAG runs that you can reproduce

Index, retrieve, and assemble context with traceable inputs/outputs and lineage per tenant and namespace.

Registry

Models, metrics, and artifacts

Browse versions, stages, metrics, and artifacts — tenant-safe by default and ready for UI consumption.

Governance

Secure by default

Multi-tenant isolation with consistent HTTP contracts. Build without inventing a new security model.

Explore RAG Request a demo

Deployment

Deploy (Beta): serve versioned models with stage control.

Promote models across dev/staging/prod, roll back fast, and keep lineage tied to data and artifacts. Xalorra is not a foundation model host — LLMs run outside the platform.