01
LLM
The machineGenerates, reasons, explains, and creates with model routing and provider choice.
Public preview · Governed AI Factory
Turn enterprise intent into verified AI outcomes.
UmamiMind presents a unified operating model for LLMs, RAG, vector knowledge, AI agents, MCP tools, guardrails, and evaluations. Explore the architecture today while the authenticated platform is built in stages.
Evidence-first design Policy-aware architecture Cost-control model
FACTORY ARCHITECTURE PREVIEW
UmamiMindControl-plane design
RAG
MCP
Agents
Evals
7 layers mappedpolicy model: illustrated
7core factory layers
1control-plane design
Traceablerun operating model
0external keys required
Product vision · AI work, untethered
Since work became a chat, why keep sitting in front of a computer?
“Direct your AI workers from anywhere.”
The platform vision is to let teams assign objectives, approve sensitive actions, follow progress, and receive evaluated outcomes from the device already in their hand.
Experience the simulation Illustrative mobile workflow
Prepare the executive risk brief and notify me when it is ready.
Example objective acceptedResearch, analysis, policy review, and evaluation stages are illustrated below.
Research stageExample complete
Policy stageExample complete
Evaluation stage98% simulated
Simulated brief readyExample sources · Illustrative guardrails · Proposed audit evidence
The AI Factory Stack
Every layer required to produce reliable real-world results
A powerful model is only one machine. Production value comes from the complete operating system around it.
02
RAG
Raw material roomFinds grounded information before the model works.
03
Vector DB
Storage warehouseStores knowledge in vector form and retrieves by meaning.
04
AI Agent
Floor managerPlans work, selects tools, and completes multi-step tasks.
05
MCP
Power outlet standardConnects agents to tools, apps, APIs, files, and databases.
06
Guardrails
Safety systemDefines what AI should and should not do.
07
Evals
Quality checkTests correctness, safety, usefulness, cost, and latency.
Production flow
From objective to evidence, every step should remain visible
The architecture treats each AI outcome as a governed run with context, actions, controls, approval points, and measurable acceptance criteria.
IntentUser or system objective
RetrieveRAG and source ranking
ReasonLLM and context assembly
ActAgent and MCP tools
ProtectPolicy and approvals
VerifyEvals and evidence
Interactive simulation
See the intended orchestration model in action
This deterministic demonstration traces the complete seven-layer path without an external model key, enterprise data source, real tool execution, or database.
Illustrative pipelineReady for objective
7 stagesLLMModel routing
01RAGKnowledge retrieval
02Vector DBSemantic search
03AI AgentPlanning and execution
04MCPTool connectivity
05GuardrailsPolicy model
06EvalsQuality simulation
07One control-plane model
Explore the trust architecture Design quality, policy, cost, security, and SLA together
Operational controls should span the complete stack instead of being added only after deployment.
Model permission-aware tools, sensitive-data controls, approvals, and run evidence.
Define regression gates, scenario suites, grounding scores, and release evidence.
Set budgets, model-routing rules, latency targets, retries, and unit economics.
Keep models provider-neutral, vector stores pluggable, and tool connectivity standardized.
Public preview
Help shape the first live platform workflows.
Explore the architecture and deterministic simulation, then request early access to discuss models, enterprise knowledge, tools, identity, governance, and telemetry for a real pilot.