Twinorg AI is an organizational digital twin and AI workforce platform. Map people, roles, work, systems, and decisions; simulate change; then hire, supervise, and govern AI employees with shared context, human approval, and traceable outcomes.
Every operating-model change moves through the same governed pipeline. Source data builds the twin, scenarios fork what-ifs, recommendations carry evidence and fairness checks, humans decide at the gate.
Twinorg AI models the whole organization — people, work, products, services, systems, costs, revenue, risks, and agents — in one editable, governed graph.
Model every team, role, process, system, product, service, cost center, revenue stream, and agent in one editable graph.
A flat, builder-first operating model is the proposed default. Bureaucracy drag, builder ratio, and approval load are first-class metrics.
Turn capacity requests into scoped, governed AI agents with explicit inputs, outputs, owners, guardrails, and approval gates.
Run what-if scenarios on the twin. Deterministic compute, fingerprinted runs, before/after diff — never mutate live state until approved.
Every twin maintains four parallel states so leaders can plan, simulate, recommend, and approve without mutating live operations.
Choose from 25+ ready-made AI agent templates or design your own digital worker. Each agent has declared inputs, outputs, required connectors, autonomy level, guardrails, evaluations, and a human owner. Nothing runs in production without confirmed scope and approval.
Twinorg AI intercepts capacity requests, asks what work the new person would actually do, and proposes a scoped AI agent with explicit tasks, inputs, outputs, owner, SLA, quality bar, and human approval gates. Human hire only when the work genuinely needs one.
Twinorg AI simulates, recommends, and explains — it never autonomously executes employment, restructuring, budget, regulatory, customer-impacting, or operational-control decisions. Every sensitive action surfaces an approval gate with evidence and audit trail.
Each module shares the same tenant, the same audit trail, and the same approval gates. Built for executive operators, not dashboard tourists.
Guided interview that produces an editable proposed operating model.
ExploreDrag-and-drop canvas with typed shapes, connectors, and live impact.
ExploreWeekly inputs/outputs per role, agent, and product. Unowned and duplicated work surfaced.
ExploreScenario builder with what-if operations, deterministic seeds, and promotion to recommendation.
ExploreReady-made agent templates, custom builder, evaluations, deployment lifecycle, and run tracing.
ExploreApproval gates by category, audit trail, fairness review, evidence viewer, policy guardrails.
ExploreDecision-ready exports: org structure, weekly work, economics, leaks, scenarios, approvals.
ExploreTenant settings, SSO/MFA, retention, integrations, RBAC, marketplace administration.
ExploreTwinorg AI surfaces the metrics that actually drive operating decisions: builder ratio, bureaucracy drag, automation coverage, revenue leaks, approval drag, agent quality, and projected savings — all tied to a specific scenario state.