Open learning. Private plant advantage.

Our public / private boundary

Matereal.ai keeps a strict separation between open learning and private plant advantage. Open assets are reference material; anything specific to your plant stays private.

Open models are reference, public-data, or synthetic-data implementations, or Matereal-owned base models. Customer-specific fine-tuning remains private.

What we publish

We publish open reference models, synthetic datasets, benchmarks, methodology notes, and white papers.

Everything published is designed to be non-confidential: reference intelligence for learning and comparison, not customer secrets.

What we never publish without permission

• We do not publish customer plant data without written permission.
• We do not publish plant-specific models without permission.
• We do not publish customer performance results without permission.
• We do not expose recipes, proprietary features, operating practices, or plant-specific benchmarks.

Customer data policy

Customer data remains customer-controlled. It is not used to cross-sell, expose, benchmark, or train public models without explicit approval.

Data provided for a private engagement is isolated to that engagement.

Web diagnostic policy

Inputs entered in the Material Value Diagnostic are not published and are not added to open research. Free-text descriptions are never sent to analytics.

The web diagnostic is for preliminary screening. Avoid submitting confidential operational data through it; detailed plant analysis is handled through a secure private process.

Model fine-tuning policy

Customer-specific fine-tuning remains private. A model fine-tuned on plant data is never published, benchmarked publicly, or reused for other customers without written approval.

Private deployments can create customer-owned IP.

Benchmark policy

Benchmarks use synthetic, public, or sample datasets only. We do not publish plant-specific benchmarks or customer performance results.

Leaderboard scores illustrate method comparison on open splits, not industrial performance.

Customer IP policy

Recipes, operating practices, and results belong to the customer. Open industry learning and private plant advantage are kept separate.

NDA policy

Private engagements operate under NDA. NDA-protected deployment details are never disclosed, referenced, or implied in public materials.

We do not reference customer projects publicly.

Data isolation principles

Customer data is isolated per engagement and not commingled across customers.

Private deployments can run on-prem or in a private cloud, keeping data within your environment.

Open-source contribution policy

Open contributions such as reference models, synthetic datasets, research, and benchmark tasks are welcomed under open licenses.

Contributors submit only non-confidential material, with no customer data or plant-specific know-how.

Private collaboration policy

Private collaboration covers data readiness reviews, private fine-tuning, plant-specific development, secure deployment, and joint research without public disclosure.

Outputs remain private unless explicitly approved for publication.