A generic operating layer for distributed intelligent systems.
RevoLynx gives distributed teams a shared operational memory, governed action layer, integration fabric, and full observability across humans, AI agents, robots, sensors, and external systems.
Distributed teams fail when context disappears.
Humans, AI agents, robots, and software systems often operate across fragmented tools, channels, logs, documents, and control interfaces. Decisions are made, but not remembered. Actions are taken, but not governed. Systems integrate, but do not share operational context.
Lost Context
Teams repeatedly rediscover the same operational facts. Decisions are made, but not preserved across shifts, systems, or agents.
Unsafe Autonomy
AI agents and robots need policy boundaries, human approvals, and auditability before acting in critical environments.
Fragmented Systems
Mission tools, business systems, sensors, communication channels, and documents remain disconnected from each other.
No Unified Observability
Operators cannot easily see what agents know, what they did, why they acted, and what it cost across the distributed team.
RevoLynx turns distributed agents into coordinated teams.
It captures events, observations, decisions, tasks, approvals, tool calls, telemetry, and lessons into a shared operational memory. Agents and robots can act through controlled connectors, but only within explicit policy, permission, and audit boundaries.
Shared Operational Memory
Persistent mission, team, task, decision, observation, and procedure memory that survives across shifts, agents, and system restarts.
Governed Action Layer
Policy-based permissions, human approvals, safety rules, role boundaries, and audit trails for every operation.
Integration Fabric
Connectors for robotics, UAVs, sensors, enterprise tools, documents, communication channels, and APIs — all governed and audited.
Observability & Audit
Trace every agent run, tool call, robot command, memory update, approval, cost event, and mission state across the full system.
Observe → Retrieve → Reason → Govern → Act → Audit → Learn
Observe
Ingest messages, telemetry, documents, sensor events, human reports, and system updates.
Retrieve
Assemble relevant operational memory, mission state, policies, and actor context.
Reason
AI agents propose actions, summaries, decisions, and task plans informed by context.
Govern
Policies determine what is allowed, blocked, or routed for human approval before execution.
Act
Execute approved actions through controlled connectors and external systems.
Audit
Record who acted, why, using which source, policy, model, and tool — every action traced.
Learn
Update shared memory, procedures, open loops, and mission context from every run.
The primitives for distributed coordination.
RevoLynx is not a chatbot, not a RAG app, and not a workflow automation tool. It is a coordination infrastructure built around shared memory, governed action, integration fabric, and full observability.
Actors and Teams
Define humans, AI agents, robots, UAVs, sensors, and services as typed actors. Organize into teams with roles, capabilities, and constraints.
Shared Operational Memory
Persistent memory graph stores events, observations, facts, procedures, decisions, and relationships. Agents retrieve relevant context before acting.
Mission and Task Coordination
Long-running missions with task allocation, dependency tracking, state management, and progress visibility across the distributed team.
Integration Fabric
Connector SDK wraps external systems with typed actions, permissions, risk levels, and audit logging for every call.
Policy and Governance Engine
Evaluate every action against policies. Allow, require human approval, or block based on actor role, action risk, and operational context.
Observability and Audit
Full trace of agent runs, tool calls, telemetry, approvals, memory changes, LLM costs. Dashboard, alerting, and audit exports.
Domain Packs
Pre-built configurations for UAV operations, tourism, construction, technical support, emergency field ops, and robotics.
Deployment Options
Cloud, on-premise, air-gapped, and hybrid deployment. Tenant isolation, role-based access, EU hosting, and self-hosting.
Built for high-context, high-coordination operations.
Domain packs adapt RevoLynx to specific operational environments while preserving the same core memory, governance, integration, and observability primitives.
Generic core. Domain-specific deployments.
RevoLynx separates memory, orchestration, integrations, governance, and observability into modular layers that adapt to mission, enterprise, and robotic deployments.
UAV MissionOps · TourismOps · ConstructionOps · Technical SupportOps · RoboticsOps
Autonomy without governance is not operational readiness.
RevoLynx is designed for bounded autonomy. AI agents and robots can observe, reason, recommend, and execute — but only within explicit policy, permission, approval, and safety boundaries.
Summarize telemetry, retrieve mission memory, draft task plans, classify incidents.
Modify mission plan, contact external stakeholders, change access codes, update critical operational state.
Execute unsafe commands, use unverified facts as confirmed state, disclose restricted information, bypass audit logging.
See every run, action, tool call, and memory change.
Full observability across LLM costs, tool calls, robot telemetry, memory updates, policy evaluations, and audit events. Operators always know the distributed team's state.
Request a technical briefing.
Tell us about your operational environment, coordination challenges, and system landscape. We'll schedule a technical discussion tailored to your domain.