Xalorra

Learn solutions
for real teams.

Xalorra is built for the boring-critical parts: tenant isolation, versioning, lineage, and stable HTTP contracts. These solutions pages explain how the system fits production use cases.

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Audit-friendly by default
Versioned datasets & models
Traceable runs & artifacts

Replace screenshot paths as needed. This page is a hub; each card is a “learn more” entry.

Solutions

Pick the problem you’re solving.

These pages are written for teams shipping production workflows: not demos, not vibes. Each section links to a dedicated “learn more” page.

Governance
Use Case

Tenant isolation, stable contracts, and auditable artifacts so governance doesn’t collapse in production.

Tenant isolationStable HTTPArtifactsLineage
Learn more Open Studio
Analytics Copilots
Use Case

Build grounded copilots on your datasets: keep outputs traceable to dataset versions and retrieval context.

Grounded answersDataset scopeTraceable runsNo magic
Learn more Open Studio
Lakehouse Operations
Product

Query, transform, materialize, and profile on DuckDB + Parquet — inside a tenant + namespace boundary.

DuckDB-firstParquet-nativeNamespace scopeControlled writes
Learn more Open Studio
Audit Readiness
Governance

How to pass audits without screenshots: deterministic artifacts, reproducible history, and predictable resolution.

ReproducibleArtifactsResolution rulesTrace logs
Learn more Open Studio
A simple way to evaluate fit

If your system needs to stay explainable months later, you want first-class versions and lineage. If you only need a one-off demo, you’ll feel the “boring constraints” quickly — and that’s the point.

Dataset scope is explicitWrites are controlledArtifacts are predictableMulti-tenant boundaries are enforced
Next steps
Read Product to understand the system pieces.
Start with Lakehouse if data ops is your bottleneck.
Start with Governance if compliance is non-negotiable.

What Xalorra is (and is not)

A control plane for audit-friendly AI workflows.

Xalorra unifies lakehouse ops and versioned ML behind stable HTTP contracts. RAG is gateway-based (Beta). Xalorra does not host foundation models.

Lakehouse ops (Core)
Query, transform, materialize, and profile datasets on DuckDB + Parquet.
ML lifecycle (Core)
Train, version, track metrics/leaderboards, and serve predict endpoints.
RAG + Deploy (Beta)
Traceable runs and deployment workflows while keeping governance coherent.
The “boring” checklist that matters
Tenant isolation, namespace boundaries, versioned datasets + models, lineage across runs, and predictable artifacts — all enforced consistently.
Stable HTTP contracts
Versioned artifacts
Lineage & traceability
Tenant-first boundaries

Solutions are easy to claim.

Shipping them in production is harder. If you want audit-friendly AI workflows, start with the contracts, versions, and lineage.

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Want product context instead? Go to Product.