The control plane for
production AI

While most platforms focus on model access, GenAI Foundry is built for model control. It differs across five structural dimensions:

• Private by design: All models and data run within the customer's own infrastructure.
• Governance enforced at runtime: Compliance is built into operational decisions.
• Human-supervised learning: No model update happens without visibility and rollback capability.
• Permanent enterprise ownership: The enterprise retains full ownership of data and model weights.
• Platform of platforms: Vertical AI systems (like InsurancGPT) inherit its safety architecture.

GenAI Foundry serves as core infrastructure for governed, production-ready AI deployment. Common use cases include:

• Building domain-specific language models trained on proprietary data.
• Automating document-intensive workflows (underwriting, claims, contract analysis).
• Creating private knowledge retrieval systems using RAG pipelines.
• Operationalizing human-in-the-loop learning loops for continuous improvement.
• Enforcing AI governance and regulatory compliance across all outputs.

It covers the complete lifecycle through governed stages:

• AI Data Studio: Cleans and prepares training-ready datasets.
• Training and RLHF: Fine-tuning with supervised learning and human feedback.
• Evaluation: Testing against accuracy and consistency thresholds.
• Deployment: Version-controlled releases with environmental isolation.
• Guardrails: Runtime safety enforcement for PII, toxicity, and policy violations.

Yes. Private deployment is a foundational design principle. Every component runs entirely within the enterprise's own infrastructure (on-premises, private cloud, or hybrid). No data is sent to shared cloud services, satisfying residency and sovereignty requirements for financial services, healthcare, and government sectors.

GenAI Foundry is built for organizations where AI decisions carry regulatory or financial consequences. Key industries include:

• Insurance: Powers InsurancGPT for underwriting and claims.
• Financial Services: Risk assessment and compliance monitoring.
• Healthcare: Clinical documentation while maintaining HIPAA compliance.
• Government: Meets strict data residency and regional language needs.
• Logistics: Large-scale operational optimization.

Security is architectural, not just contractual. Key mechanisms include:

• Private Infrastructure: Operations remain within the customer's environment.
• Runtime Guardrails: Real-time detection of PII exposure and policy violations.
• Automatic Audit Trails: Every decision is logged with full reasoning context.
• Certified Standards: Meets ISO 27001, SOC 2 Type 2, HIPAA, and CCPA.

Yes. It governs AI from raw data through continuous production operation as a single integrated control plane. Stages include Data Studio preparation, RLHF alignment, pre-production evaluation, versioned deployment, and ongoing safety monitoring via Guardrails.

GenAI Foundry is organized around eight integrated capabilities:

• AI Data Studio: Training-ready dataset preparation.
• Training and RLHF: Governed model alignment.
• Evaluation: Testing against task-specific criteria.
• Deployment: Controlled production releases.
• Playground: SME validation of model behavior.
• Guardrails: Runtime policy enforcement.
• RAG and Knowledge: Project-scoped knowledge retrieval.
• Prompt Library: Pre-approved, reusable prompt management.

It provides the infrastructure to transform proprietary data and domain expertise into production-grade systems. This is achieved by assembling domain-specific datasets, fine-tuning models to business rules via RLHF, and promoting models through evaluation gates to controlled production endpoints.

Yes. Customization goes deeper than prompt engineering through three mechanisms: Supervised fine-tuning for domain language, RLHF for aligning behavior with expert institutional knowledge, and RAG for reasoning over enterprise-specific document sets.