steav

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1. 11/2/2026

   Updated: best for, features, platforms, sources, summary, use cases, walkthrough

   See recorded changes

   bestFor

   Before: ["Companies needing AI models that comply with strict security and regulatory standards like ISO/IEC 42001, NIST AI RMF 1.0, SOC 2, HIPAA, and EU AI Act."]

   After: ["Companies needing full control over AI models and agents built on proprietary data, especially in regulated industries."]

   features

   Before: ["Models are trained on company data and do not change unless the company changes them.","The platform runs the entire build process from raw data to deployed model, with STEAV engineers assisting for specialist parts.","Models can be upgraded to better base models without starting over.","The platform provides a seven-stage model pipeline including ingest, context assembly, training where data lives, evaluation, validation, signing, and deployment.","The platform supports training models across teams without sharing data by only sharing model updates."]

   After: ["Data preparation, training, evaluation, deployment, and agent orchestration in one platform.","Encrypted and federated training for private data, with centralized training for other data.","Safety testing and guardrails before model deployment, with tampering detection.","Versioning and retuning of models as data changes, with full ownership of every version.","Agents built on company-specific models that automate workflows and operate under governance rules."]

   platforms

   Before: ["Deploy on-premises, air-gapped, or in a region-pinned sovereign cloud with customer-controlled keys."]

   After: []

   sources

   Before: [{"url":"https://www.steav.io/","label":"STEAV · Build, run, and own your AI."},{"url":"https://www.steav.io/product/cid","label":"Platform · STEAV"}]

   After: [{"url":"https://www.steav.io/","label":"STEAV | Sovereign AI Platform: Build, Automate, Centralize"}]

   summary

   Before: "STEAV is a platform that enables companies to build, run, and own AI models trained on their own data within their own environment, ensuring data sovereignty and control. It supports building, customizing, testing, and deploying AI models while maintaining a full record of the model's creation and compliance with security standards."

   After: "Building AI models specialized for company-specific data such as documents, records, and images. Data preparation, training, evaluation, deployment, and agent orchestration in one platform. Encrypted and federated training for private data, with centralized training for other data."

   useCases

   Before: ["Organizations with sensitive data such as banks, insurers, hospitals, governments, and enterprises that require AI they control."]

   After: ["Building AI models specialized for company-specific data such as documents, records, and images.","Automating enterprise workflows with AI agents governed by company policies and departmental rules."]

   walkthrough

   Before: ["Bring your data and keep it where it is, build or customize a model starting from an open model, then run it in your cloud, data center, or hosted by STEAV.","The model pipeline includes data preparation, training, aggregation, evaluation, benchmarking, validation, and deployment, each stage signing its work into a record."]

   After: ["Ingest and track data lineage, assemble context, train models, evaluate and validate them, sign and attest, then deploy where chosen."]

2. 9/15/2026

   Updated: best for, features, limitations, platforms, sources, summary, use cases, walkthrough

   See recorded changes

   bestFor

   Before: ["Organizations in healthcare, financial services, government, and insurance sectors that require compliance, auditability, and control over AI models.","Organizations that want to build, run, and own their AI models instead of renting from third-party APIs."]

   After: ["Companies needing AI models that comply with strict security and regulatory standards like ISO/IEC 42001, NIST AI RMF 1.0, SOC 2, HIPAA, and EU AI Act."]

   features

   Before: ["Cryptographic signing and recording of every stage of AI model building, creating tamper-evident audit logs.","Federated training that runs where data lives, ensuring raw data never leaves the environment.","AI model lifecycle management with versioned, access-controlled datasets and context tracking.","AI agents and workflows composed with every step running as an attested operation for continuous improvement."]

   After: ["Models are trained on company data and do not change unless the company changes them.","The platform runs the entire build process from raw data to deployed model, with STEAV engineers assisting for specialist parts.","Models can be upgraded to better base models without starting over.","The platform provides a seven-stage model pipeline including ingest, context assembly, training where data lives, evaluation, validation, signing, and deployment.","The platform supports training models across teams without sharing data by only sharing model updates."]

   limitations

   Before: ["Some offerings like MONOLITH for precision annotation and labeling are coming soon and not yet available.","STEAV is not a blockchain or cryptocurrency company; blockchain is used only as a tamper-evident ledger for auditability, not for trading or crypto purposes."]

   After: []

   platforms

   Before: []

   After: ["Deploy on-premises, air-gapped, or in a region-pinned sovereign cloud with customer-controlled keys."]

   sources

   Before: [{"url":"https://www.steav.io/","label":"Verifiable AI Infrastructure | STEAV"},{"url":"https://www.steav.io/product/cid","label":"CID: Verifiable AI Infrastructure · STEAV"}]

   After: [{"url":"https://www.steav.io/","label":"STEAV · Build, run, and own your AI."},{"url":"https://www.steav.io/product/cid","label":"Platform · STEAV"}]

   summary

   Before: "STEAV provides AI infrastructure that enables organizations to build, train, and own AI models on their own infrastructure with cryptographic verification of every step. It is designed for regulated industries requiring control, privacy, and auditability, allowing federated training without moving raw data and producing tamper-evident records for compliance."

   After: "STEAV is a platform that enables companies to build, run, and own AI models trained on their own data within their own environment, ensuring data sovereignty and control. It supports building, customizing, testing, and deploying AI models while maintaining a full record of the model's creation and compliance with security standards."

   useCases

   Before: ["Federated AI training across organizations without moving raw data, enabling privacy-preserving model building.","AI governance and lifecycle management with cryptographic audit trails for compliance and regulatory scrutiny."]

   After: ["Organizations with sensitive data such as banks, insurers, hospitals, governments, and enterprises that require AI they control."]

   walkthrough

   Before: []

   After: ["Bring your data and keep it where it is, build or customize a model starting from an open model, then run it in your cloud, data center, or hosted by STEAV.","The model pipeline includes data preparation, training, aggregation, evaluation, benchmarking, validation, and deployment, each stage signing its work into a record."]