XASE

GovernedAccessToReal-WorldDataForAI

Use real data without legal risk, without loss of control, and without transferring ownership.

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Policy-evaluated access • Cryptographic evidence • Zero ownership transfer

Production Ready

End-to-end

Policy enforcement + billing + evidence

AI Labs

Complete testing environment

Live metrics

Real-time usage & revenue tracking

Ownership with control

The Data Holder defines policies. The AI Lab executes within them. Evidence is automatic.

Data Holder
Creates Policy

AI Lab
Requests Access

Evidence
Generated

Data Holder

Create Access Policy

Define who can access

Set purpose and duration

Specify cost per hour

Enable runtime enforcement

No files sold. No ownership transferred.

AI Lab

Request Access

Authenticate with API key

Specify intended purpose

System evaluates policy

Access granted or denied

No downloads. No file custody.

Evidence

Automatic Proof

Every access logged

Every denial recorded

Cryptographic signatures

Exportable evidence bundles

Court-admissible. Offline verifiable.

Turn idle data into revenue

Data Holders monetize datasets without selling files. Set your price, define access rules, track usage in real-time.

$X/hour

You set the price

Define cost per compute hour. AI Labs pay for usage, not ownership. Automatic billing and settlement.

Live metrics

Real-time dashboard

Track active sessions, total usage hours, revenue per dataset, and access patterns as they happen.

Take-rate

Platform fee model

Xase takes a percentage of each transaction. You keep control, we handle infrastructure and compliance.

What you get

Revenue stream: Monetize data you already own

Zero risk: No file transfers, no custody loss

Full control: Revoke access anytime, audit everything

Automatic billing: Usage tracked to the second

Compliance proof: Every access logged and exportable

Policy enforcement: Runtime guarantees, not promises

Real world data is valuable but ungoverned data is unusable

AI Labs need real data to improve models, but legal exposure makes it impossible. Data Holders have valuable assets sitting idle.

For AI Labs

Real data improves models significantly

But brings massive legal exposure

No proof of correct usage

Compliance teams always block

For Data Holders

Valuable data sits completely idle

Monetization seems impossible

Legal always says "too risky"

No control after data transfer

"The current model is: give us your data and trust us. That's not governance — that's hope."

Data Access as a Runtime

The AI Lab never downloads the dataset. It executes an authorized access. Every call is evaluated, allowed or denied, with evidence generated.

access_request.py

# AI Lab requests access to data
import xase

# Authenticate and specify purpose
client = xase.Client(api_key="lab_key_abc123")

# Request access with clear intent
access = client.request_access(
    dataset_id="customer_calls_2024",
    purpose="model_training",
    duration_days=30,
    tenant="ai_lab_beta"
)

if access.granted:
    # Use data within policy constraints
    for batch in access.stream_batches():
        model.train(batch)
        
# Evidence automatically recorded

How it works:

1. Policy Check: Every access request evaluated against data holder's rules

2. Runtime Control: Data never leaves the secure environment

3. Evidence Generation: Every action cryptographically logged

4. Automatic Compliance: Audit trail generated in real-time

Complete AI Labs environment

Test access policies, simulate workloads, export evidence — all before paying for production usage.

Sandbox Testing

Free tier: Test policies without consuming credits

Policy simulation: See what gets allowed or denied

API playground: Try access patterns before production

Evidence preview: Export sample bundles to verify format

Production Workflow

Authenticate: API key-based access control

Request access: Specify purpose and duration

Execute: Run models in secure environment

Track usage: Real-time billing and metrics

Use cases

Model Evaluation

Test against real-world edge cases, benchmark on actual user data, validate before production

Domain Adaptation

Fine-tune for specific industries, learn domain patterns, adapt to regional contexts

Research & Development

Experiment with architectures, study failure modes, develop safety mechanisms

Evidence bundles explained

Not just logs. Cryptographically signed, offline-verifiable proof of policy enforcement.

What's inside

Policy snapshot: Exact rules that were enforced

Access records: Who, when, what, why — timestamped

Denials logged: Every rejected request with reason

Cryptographic signatures: Tamper-proof chain of custody

Audit metadata: Environment, versions, checksums

Why it matters

Court-admissible: Designed for legal proceedings

Offline verifiable: No database access needed

Compliance ready: GDPR, SOC2, industry standards

Export anytime: ZIP file with all proofs

Third-party auditable: Hand to regulators directly

Difference from simple logs

Traditional logs can be edited, deleted, or lost. Evidence bundles are cryptographically signed at generation time, include the complete policy context, and provide offline verification. You can prove compliance without giving auditors database access or trusting a third party's word.

Real-world applications

Production deployments across regulated industries where data governance is non-negotiable.

Healthcare & Medical AI

Hospital network monetizes de-identified patient records for AI research. Research labs access data for model training without HIPAA violations.

Policy example

Purpose: medical research only. Duration: 90 days. Cost: $500/hour. No PII export.

Evidence generated

Every query logged, access timestamps, denied requests, audit trail for compliance officers.

Revenue impact

$45K/month from 3 research partners. Zero legal incidents. Full audit trail.

Financial Services & Fraud Detection

Bank provides transaction data to fintech for fraud model training. Data never leaves secure environment. Every access logged for regulators.

Policy example

Purpose: fraud detection. Duration: 6 months. Cost: $1000/hour. No customer data export.

Evidence generated

Model training runs, feature extraction logs, compliance reports for SOC2 and PCI-DSS audits.

Revenue impact

$120K/month from 2 fintech partners. Regulator-approved evidence bundles. Zero breaches.

Call Centers & Voice AI

Enterprise call center licenses conversation data to voice AI startups. Models train on real customer interactions with full consent tracking.

Policy example

Purpose: voice model training. Duration: 12 months. Cost: $300/hour. Anonymized transcripts only.

Evidence generated

Training session logs, data access patterns, consent verification records, GDPR compliance proof.

Revenue impact

$36K/month from 4 AI labs. Automated billing. Legal approved all evidence bundles.

How it works

If it's not a download, how does training happen?

Your model executes within our secure environment. Data never leaves our infrastructure. You get gradients, weights, and results — not raw files.

What does my model actually receive?

Processed data streams, embeddings, or API responses — whatever the policy allows. The data holder defines exactly what format and level of access you get.

How do I prove compliance to auditors?

Every access generates cryptographic evidence bundles. Hand auditors a ZIP file with policy enforcement proof — no database access needed.

What's the economic model?

Usage-based access. Data holders set price per hour, AI Labs pay to use, Xase facilitates settlement. No upfront costs, no minimums.

Build AI with real-world data

Stop avoiding real data because of legal risk. Use infrastructure designed for governed access.

Questions? founders@xase.ai • We respond in <24h.