Fermi FAQs
Fermi is an AI platform that connects enterprise tools and automates workflows with intelligent agents, delivering real‑time insights and predictive optimization.
FAQs of Fermi
What is Fermi and what are its main capabilities?
Fermi is an AI‑driven operational brain that captures all enterprise data, models entities and their relationships, and autonomously predicts and recommends actions. It connects disparate tools, learns from real‑time inputs, and exposes a unified view through natural‑language queries. The platform’s architecture allows scaling from a few dozen nodes to thousands, with automated workflow design and execution driven by intelligent agents.
How does Fermi integrate with existing business systems?
Fermi uses a modular integration layer that supports standard APIs, webhooks, and direct database connections. During onboarding, connectors map data fields to the unified model, preserving data integrity and security. The system automatically detects overlapping entities across sources, resolves conflicts, and updates the operational graph in real time. This integration ensures consistency without requiring custom code for each tool.
What is the onboarding process for a new company using Fermi?
Self‑onboarding begins with a quick configuration wizard that imports business goals, maps key systems, and defines policies. The platform performs data extraction, entity resolution, and automatic graph construction in hours. Users can validate and adjust models through an intuitive visual editor, after which AI agents are provisioned. Continuous learning cycles refine the model as new data arrives, enabling rapid operational readiness.
How do the AI agents in Fermi execute operational tasks?
Fermi’s agent layer comprises pre‑built workflows for common functions—order processing, customer support, invoice handling—and a no‑code builder for custom scenarios. Agents share context from the unified graph, allowing them to access cross‑department data. Each task is evaluated against policy rules; if approved, the agent triggers the necessary system calls via secured APIs. Agents run continuously and can be scaled on demand, paying only for executed operations.
What monitoring and change‑management features does Fermi provide?
Fermi’s Conductor module enables simulated changes through a “Simulate > Approve > Apply” pipeline. Before deployments, the system runs predictive models to assess impact, providing confidence scores and risk flags. Post‑deployment, dashboards track key metrics—task throughput, error rates, and SLA adherence—in real time. Auditable logs record every agent action, ensuring compliance and traceability across the organization.
How to use Fermi
Begin by accessing the Fermi dashboard, logging in, and opening the self‑onboarding interface that maps your company’s objectives and existing tools.
During onboarding, the panel displays the菲尔米诺 integration graph, illustrating connections between departments and highlighting the system’s fermi level of data flow.
Connect each enterprise tool by selecting the appropriate adapter; the wizard verifies authentication and establishes a two‑way sync, matching the fermi surface of business processes.
Next, define company policies within the Policy Configurator, using declarative rules that mirror the fermi golden rule to predict task transition probabilities.
Deploy the pre‑built agent library or construct custom agents through the Agent Builder; treat each agent as a fermion, ensuring unique state allocation across workflows.
Use the “Conductor” to simulate changes: run Simulate, review the projection, approve, then apply the update safely across the unified model.
Engage the live AI Assistant by entering natural language queries; the assistant retrieves real‑time analytics from all connected sources and answers with contextual clarity.
Visualize results on the analytics dashboard; observe key metrics such as revenue, churn, and throughput, then interpret them through fermi energy analogies to assess system load.
Address potential data gaps—the fermi paradox of missing entries—by running completeness checks; the system flags anomalies and suggests remediation tasks for agents.
Leverage the insights to refine business strategies; schedule quarterly reviews with the AI Assistant, then implement automated optimizations through the Conductor or new agents.
