FlexInfer
Private inference operations for teams that need model lifecycle, routing, GPU placement, quantization, adapters, and OpenAI-compatible endpoints inside their own boundary.
Explore what each product does, how I use it, and the docs, examples, and implementation notes behind it.
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Private inference operations for teams that need model lifecycle, routing, GPU placement, quantization, adapters, and OpenAI-compatible endpoints inside their own boundary.
MCP and agent-fleet coordination for private operations: editor sync, single-entry tool access, daemon routing, tiered context, Weaver orchestration, and the Mills delivery pipeline.
Mission Control for DAG-based agent workflows that call direct API model nodes, internal services, MCP-governed tools, and private FlexInfer endpoints.
No public telemetry
MentatLab does not publish run data to this site, so no figure is shown.
Healthcare ETL and integration tooling for Source Profiles, semantic events, workflow routing, terminology mapping, and HL7v2 parsing and FHIR output.
The parsing and FHIR mapping kernel compiled to WebAssembly; synthetic samples, and nothing you paste is uploaded.
LocalConnect private inference to controlled tool access, repeatable workflows, and healthcare integration. Each component has its own deployment requirements and capability limits.
FlexInfer gives teams a Kubernetes-native control plane for model lifecycle, OpenAI-compatible routing, GPU-aware placement, scale-to-zero activation, quantization, adapters, and model artifact delivery.
Can this model run, scale, route, and roll back inside our cluster boundary?
FlexInfer productLoom and Loom Core turn MCP sprawl into a single governed entrypoint with registry sync, daemon routing, tiered memory, fleet state, sandbox execution, RBAC, audit, HUD visibility, and Mills autonomous delivery pipelines.
Which agents can reach which tools, with what context, and what audit trail?
Loom Core productMentatLab is the mission-control surface for DAG agent orchestration over direct API model nodes and internal services. In this stack, those model calls can target FlexInfer private endpoints instead of a frontier coding harness.
Can operators design, validate, run, and observe the workflow as a graph?
MentatLab productfi-fhir provides healthcare ingestion, transformation, and delivery: Source Profiles, HL7v2 parsing and FHIR output, semantic events, workflow routing, terminology mapping, authenticated durable ingress, and AI-assisted explanations inside a controlled environment.
Can legacy clinical data become validated, explainable, routable events?
fi-fhir productIf you want to see how I think, start with the case studies and writing that explain the decisions behind these products.
Diagrams of model hosting, public dashboards, and GPU deployment automation.
Registry-driven client sync, loom proxy ingress, daemon routing, and HUD-backed operator surfaces for private MCP operations.
Session-start recall, tiered memory, and prompt-pressure compaction keep agent context bounded and queryable.
FlexInfer-backed multi-domain routing with curated tool bundles and compound tools for reusable agent work.
OpenAI-compatible proxying, model lifecycle control, and GPU-aware execution inside a private cluster boundary.
Artifact preparation, cache gating, quantization, and preload behavior that turns model intent into a ready endpoint.
GlobalProxy and FederatedModel coordinate cross-cluster reach while keeping runtime execution local to each cluster.
Profile-driven transformation flow for sensitive interoperability workloads.
models warm now
On llama.cpp · diffusers · vLLM
LiveMCP servers
498 tools allowlisted
Snapshot · Sep 25merged by Mills, 30 d
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