FAQ
COGNIT FAQ
Common questions about COGNIT distributed computing, neurons, neural signals, and MCP compatibility.
General Questions
What is COGNIT?
COGNIT is a distributed computing system inspired by biological neural networks. It allows you to add any device as a compute node (neuron) and process AI workloads across your entire infrastructure with real-time coordination via neural signals.
What devices can be neurons?
Any device can be a neuron — servers, laptops, phones, tablets, or specialized hardware. COGNIT automatically detects capabilities and routes appropriate tasks based on device resources and availability.
How does COGNIT differ from traditional distributed computing?
COGNIT uses a biologically-inspired neural signal protocol for communication, self-organizing mesh topology, and continuous self-learning. Unlike traditional systems, it automatically optimizes itself based on experience and can integrate personal devices as compute nodes.
Is COGNIT open source?
COGNIT is available as part of the HYBRID IN. platform. Contact us for enterprise deployment options and licensing details.
Neurons and Mesh
How do I add a device as a neuron?
Install the COGNIT neuron agent on any device. The agent automatically discovers the mesh, reports capabilities, and begins accepting tasks. No complex configuration required — the mesh self-organizes.
What happens when a neuron goes offline?
COGNIT includes fault tolerance with circuit breakers and automatic failover. When a neuron goes offline, tasks are automatically rerouted to healthy nodes. The system continuously monitors health and attempts recovery.
Can I mix on-premise and cloud neurons?
Yes, COGNIT supports hybrid deployment. Mix on-premise servers with cloud instances for flexible infrastructure. Keep sensitive data on-premise while leveraging cloud for burst capacity.
How does task routing work?
COGNIT automatically routes tasks based on device capabilities, current load, and network conditions. Heavy AI tasks go to GPUs, lightweight tasks to edge devices, and the system adapts routing based on performance.
MCP and Integration
What is MCP compatibility?
MCP (Model Context Protocol) is an open standard for connecting AI systems to data sources. COGNIT MESH implements MCP, allowing any MCP-compatible AI system to access COGNIT tools, resources, and knowledge.
Which AI systems work with COGNIT via MCP?
Any MCP-compatible AI system, including Claude, and other major AI providers that have adopted the MCP standard. The protocol is open and widely supported.
What tools does COGNIT expose via MCP?
COGNIT exposes distributed computing, knowledge retrieval, signal processing, and mesh management as MCP tools. AI systems can trigger distributed tasks, query knowledge bases, and monitor mesh health.
How do I integrate COGNIT with my AI system?
Configure your MCP-compatible AI system to connect to the COGNIT MESH endpoint. The system will automatically discover available tools and resources. Contact us for integration support.
Performance and Scaling
How scalable is COGNIT?
COGNIT scales horizontally — add more neurons to increase capacity. The mesh self-organizes and automatically balances load. From a few devices to thousands of neurons, performance scales linearly.
What is the latency of neural signals?
Neural signal latency depends on network conditions but is optimized for real-time communication. Local mesh communication typically achieves sub-millisecond latency for coordination signals.
How does COGNIT handle network partitions?
COGNIT is designed for network resilience. Neurons can operate independently during partitions and automatically resynchronize when connectivity is restored. Knowledge is replicated to prevent data loss.
What are the resource requirements for neurons?
Neurons can run on minimal resources — even low-power devices can contribute. For optimal performance, we recommend at least 2 CPU cores and 4GB RAM for general-purpose neurons, with GPU support for AI workloads.