Collaborate on AI
without sharing data

A vendor claims 95% accuracy on their cherry-picked demo set

You can't test on your data. You decide based on their benchmarks. Whether it works on yours — you find out after you've signed.

Five vendors submit to your workspace. Same holdout, same pipeline, one leaderboard. Numbers decide.

You need different approaches on the same problem. But the data can't leave.

Getting external people on your data means NDAs, security reviews, and legal approvals. Most teams build in isolation.

One workspace, identical environment. Submit, compare, improve, resubmit. Focus on models, not infra.

Your model plateaued. More diverse training data would push it further.

No audit trail. Manual review catches 2-5% of decisions. If you can't prove it, it didn't happen.

Auditable containers. Full decision trails, logged and reproducible. Evidence on demand.

You need a model tuned on your clinical notes, legal contracts, or manufacturing logs. Can't send that data out.

External API means losing control over retention and governance blocking it. For legal teams, may waive attorney-client privilege.

LoRA, adapters, full fine-tuning. Executes on your machine. No upload, no approval needed.

Performs well on your test set. Zero signal on other distributions. Drops hard on external data.

External data means sharing agreements, format alignment, and months of back and forth before a single evaluation.

Runs on their data at their site in an identical container. Metrics back. No data moves.

Paper claims SOTA. You reproduce it. Different numbers.

Different OS, library versions, data splits, random seeds. Can't tell if the model is better or the setup is different.

Isolated containers. Pinned dependencies. Same conditions for every submission. Reproducible by design.

Stuck at 78% accuracy. Every architecture and technique exhausted.

The people who could help can't access your data. Getting them access means half your timeline on legal and compliance.

They submit models to your workspace. Containerized execution on your data. No transfer, no setup, no approvals.

The datasets you need sit in other organizations. They can't be shared.

Pooling means ETL pipelines, schema alignment, governance sign-off, and weeks of data engineering. Nobody wants to go first.

Federated training across sites. Each dataset stays local. Model learns from all. Skip the pipeline and approvals.

High-value data. Genomic, financial, industrial. Others would train on it.

Exporting means anonymization, format conversion, legal review. You lose control the moment it leaves.

Others submit models to your workspace. Compute runs locally. You meter access. Data stays put.

Four offices, three time zones. Every team thinks their model is best.

Reconciling preprocessing, test splits, metrics, and environment configs.

One dataset, one pipeline, one leaderboard. Same containerized environment. Scores settle it.