Private AI Platform for Sensitive Workloads
On-prem and hybrid AI operations for sensitive workloads: inference control with FlexInfer, context orchestration with Loom Core, and healthcare-aligned integration workflows with fi-fhir.
Start with platform posture, then move into playground and docs for implementation details.
FlexInfer focuses on operational readiness in private environments: repeatable deployment controls, healthcare-aligned integration tooling, and a roadmap for enterprise context governance.
Kubernetes-native routing, deployment controls, and operational workflows for predictable on-prem or hybrid inference.
Deployment: Model runtime placement, rollout safety, and capacity controls inside your cluster boundary.
Integration: Inference APIs and platform operations remain inside your network and observability stack.
Registry-driven MCP config generation, daemon routing, and enterprise context controls roadmap (gateway, RBAC, executor).
Deployment: Centralizes MCP server lifecycle and policy boundaries for internal tools and agent access.
Integration: Defines how agents reach internal systems with auditable routing and least-privilege intent.
Healthcare-focused ingestion and transformation workflows (HL7v2 to FHIR) with profile-driven, testable data handling.
Deployment: Data transformation pipeline runs in your controlled environment and deployment topology.
Integration: Profile-driven mapping and validation isolate source variability while preserving operational traceability.
Product pages cover lane fit. Docs and playground cover execution. MentatLab adds the orchestration UI surface for DAG run control, while Loom Core continues to govern context and policy boundaries.
Loom Core governs context routing and policy boundaries; MentatLab provides the operator UX for DAG design and run visibility.
CLI + daemon for registry-driven MCP configuration, server orchestration, and lifecycle management.
VS Code extension for multi-platform MCP config sync, skills registry, and agent context workflows.
Zed integration for Loom workflows: consistent servers, shared registry, and repeatable profiles.
Kubernetes-native inference routing: ship GPU workloads with predictable configuration and operations.
Healthcare integration tooling: Source Profiles, parsing pipeline, and HL7v2 to FHIR workflows.
Loom Core enterprise controls are in progress. Operational foundations are available today and already documented.
Centralized MCP routing layer for enterprise tool aggregation and policy enforcement.
Role-aware permissions for MCP tool access across teams and environments.
Containerized execution path for development and automation workloads in Kubernetes.
Production patterns already available: observability, workflow docs, and deployment controls.
Provides the baseline operating model while enterprise controls are being rolled out.
Playground pages are designed to match the docs: config editor, schema validation, and entrypoints into Loom Core workflows.
Start with deployment and integration boundaries, then map the right product lane.
Use docs and playground workflows to validate config, routing, and integration behavior.
Use the consulting path for architecture hardening in private environments.
If you want this stack inside your environment, the fastest path is a scoped audit or build engagement.
Fixed-scope diagnostic for sensitive workloads: architecture review, risk register, deployment constraints, and a 90-day plan.
Build private or hybrid inference infrastructure with observability, GitOps, and safer rollout controls.
Hands-on senior guidance to align teams around an executable platform roadmap.
Implementation notes, architecture decisions, and reusable patterns.