Private AI operating systems
Grace OS controls the system. The R.A.I.S.E. secure kernel executes protected AI work. Together, they deliver governed agents, local RAG, and model-flexible infrastructure from portable edge hardware to private GPU clusters.
Model scale is determined by infrastructure, licensing, power, cooling, and operating budget. Governance improves control and auditability; it does not make AI infallible.
The core product
Chatbots bolted onto sensitive data keep failing the organizations that trust them. We build the operating layer, secure execution environment, governance controls, and infrastructure required to run AI as an accountable organizational system.
The orchestration and control plane for the entire system: knowledge, identity, tools, agents, workflows, model routing, approvals, and organizational memory.
The Redacted AI Secure Environment: an on-premise or fully disconnected execution boundary for models, RAG, evidence, agents, and protected data.
Identity, policy, approvals, evaluation, logging, and human authority connect the OS to every action performed inside the secure environment.
See the governance architecture →Governed agentic work
Agentic workflows become useful when organizations can see what acted, under which policy, with which evidence, using which tool, and whether a person approved the consequence.
Explore accountable AI workflows ↗Agents receive defined roles, tools, data access, and limits instead of broad, invisible permission.
High-consequence steps can be checked against rules, stopped, escalated, or routed for human approval.
Tasks, tool calls, source context, approvals, and outcomes are designed to remain attributable and reviewable.
People retain the authority to approve, reject, override, pause, and investigate governed workflows.
Infrastructure without a ceiling
R.A.I.S.E. is not tied to one box or one model. We scope the workload and provide the hardware architecture it requires, from compact edge systems to data-center-class GPU infrastructure.
Local inference and focused workflows close to the data.
Larger models, retrieval, and multi-user departmental work.
Controlled organizational deployment with dedicated infrastructure.
Data-center-class model capacity when workload and budget require it.
Capacity is bounded by model licensing and availability, memory, compute, storage, power, cooling, resilience requirements, and budget. We design the system around those realities instead of selling a single appliance and calling it a fit.
Scope the workload →Deployment verticals
The OS, kernel, and governance remain consistent. The corpus, workflows, policy, hardware, and accountability model are configured for each domain.
Source-grounded RAG, literature and corpus intelligence, traceable synthesis, and protected institutional knowledge.
Explore this specialty →Capable AI where bandwidth, latency, mobility, resilience, or data sovereignty make the cloud the wrong answer.
Explore this specialty →Local legal RAG, evidence and document workflows, source lineage, and attorney-controlled agentic processes.
Explore this specialty →Controlled knowledge and research workflows with engagement-specific governance.
View deployment path →Local and disconnected AI for sensitive missions, records, and operational knowledge.
View deployment path →Internal knowledge systems and governed automation without an external inference path.
View deployment path →All-in-one air-gapped service
Architecture, model and hardware selection, RAG, agent workflows, governance, local deployment, disconnected operations, evaluation, documentation, and scale planning, designed as one accountable system.
Define the mission, data boundary, model class, users, risk, and operating environment.
Select and configure the right edge, workstation, server, or cluster infrastructure.
Apply identity, policy gates, approvals, logs, evaluation, and workflow accountability.
Deploy locally or disconnected, document the system, and plan maintenance and scale.
Local and disconnected deployments are designed without a routine cloud inference path. Security still depends on correct configuration, physical safeguards, identity, media controls, maintenance, and disciplined operations.
A clear path to buy
The recommended entry point is one bounded 45-day private-AI implementation. There is no software subscription to buy. A typical scope represents 80–120 hours of specialized delivery, with hardware and exceptional licensing quoted separately.
≈80–120 hours of typical delivery · plus workload-fit hardware
Start with a no-charge founder call and define what must remain local.
See the exact service, hardware, licensing, timeline, responsibilities, and exclusions before committing.
Evaluate the workflow for 45 days, document failures, and decide whether production is justified.
Move to a custom per-environment, office, or node deployment only when the pilot earns it.
Built in Michigan
Redacted AI Solutions LLC is a queer-owned, Latino-led company building private AI infrastructure inside Michigan's research and commercialization ecosystem.
Technology Innovation CenterEast Lansing operating base
MSU Research FoundationEntrepreneurial ecosystem support
LEAPLansing Economic Area Partnership
Michigan SBDCTechnology commercialization guidanceFounder + development journal
Private AI here comes from lived systems experience, hard product decisions, and months of visible iteration. Meet the person behind the build and inspect the decision record.
Thirty-plus years in hands-on computing, queer and Latino leadership, a family move to Michigan, and a decision to keep building through the hardest stretch.
Read the founder story →From the working CLI pipeline and Z13 edge platform to Grace OS, eleven-stage retrieval, the secure kernel, air-gapped service design, and accountable agentic workflows.
Open the complete build log →Private by architecture
Founder-led architecture and opportunity discovery.