Built for AI

Get AI Projects Moving Faster with Built-in Rules for Files, Objects, Streaming, and Caching.

PumaMesh helps AI teams handle models, weights, and data—streaming, caching, searching, and moving training data, retrieval data, and results without creating shadow pipelines or separate audit projects.

Use one platform to protect sensitive AI data, understand where it came from, move it to approved systems, and prove which policy applied.

See the AI benchmarks →

AI delivery models, weights, data, and results Governance policy travels with the work Lineage source context stays visible Proof benchmark and audit evidence

AI data pipeline illustration: edge nodes flowing through a neural network brain into a governed dashboard on a dark navy background

Windows + Linux, Natively

One governance path from the laptop to the GPU cluster.

AI work often starts on one system and runs somewhere else. PumaMesh keeps data governed from export through training, delivery, and reuse.

Works across Windows, Linux, cloud AI platforms, on-prem GPU clusters, and edge environments
Moves training sets, model artifacts, inference outputs, and partner data under one policy story
Keeps protection and audit in the movement path
No SDK integration required; applications keep working unchanged

Three AI Motions

Move AI work between systems, people, and partners.

AI work moves between systems, then to people, then across teams and partners. PumaMesh keeps those handoffs under the same governance and evidence model.

Systems and pipelines

Between Systems: Keep Your AI Workflows Fed with Files and Objects via Streaming and Caching

Model delivery, training-set replication, fine-tune pushes, and inference-cache sync move quickly while protection and audit stay in the path.

Large model bundles and datasets move across distance
Training and fine-tune artifacts reach approved systems
Federated learning workflows can respect sovereignty zones
Benchmark proof is available for performance review

Systems to people

Deliver AI outputs with source context

Model outputs, findings, and reports reach humans with the classification and provenance of the underlying records intact.

Response lineage links results to source sensitivity
Delivery checks stay fresh as user access changes
Operator dashboards show what the model saw, from where, under which policy

People and partners

Collaborate on AI data without losing control

Partner exchange, cross-institution research, and legal/IP sharing move between people and organizations with policy attached to the object.

Training data shared across partners under object-level policy
Sensitive records blocked or quarantined before any hop if policy requires
Chain-of-custody evidence attached to the transfer, exportable for IRB, legal, or compliance review

AI Control Surfaces

Keep governance attached after data leaves storage.

AI pipelines create new handoffs across training, retrieval, tools, and results. PumaMesh keeps policy and evidence connected to those handoffs instead of stopping visibility at the bucket.

Training Boundary

Keep restricted records out of the wrong training set

Classification, jurisdiction, and customer labels help decide which records can enter which training artifact before bytes leave the source.

Retrieval Boundary

Control what retrieval can expose

Retrieval workflows can use data attributes before sensitive records enter model context.

Tool-Call Boundary

Limit what AI agents can reach

The same data context that gates file movement can guide what an AI agent is allowed to read, write, or act on.

Fine-Tune Provenance

Show which data shaped the model

Training set posture, fine-tune lineage, and model-to-source mapping help teams answer what data influenced which model artifact.

Evidence for the AI Era

Give AI reviewers evidence they can actually use.

Every governed AI movement can produce evidence for the reviews that now surround high-risk models, sensitive training data, and regulated outputs.

AI activity records

Operator, classification, policy, and model-lineage context can support record-keeping obligations for high-risk AI systems.

AI risk management evidence

Training-set posture, fine-tune provenance, and retrieval lineage reporting support model risk and governance reviews.

AI management system support

Data governance, lifecycle controls, and model-to-source traceability help teams explain how AI data is governed.

CUI and regulated AI data

AI training data can inherit the same movement and evidence controls as other regulated records.

Federal control alignment

AI pipeline boundaries can inherit the same enforcement and audit evidence used for federal deployment conversations.

Cyber insurance and model risk

AI data surface inventory, sensitive-data flow maps, and training provenance help underwriters and model-risk teams ask better questions.

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