End-to-End Troubleshooting
Everest supports full guided troubleshooting journeys: symptom collection, dynamic diagnostic steps, log and serial capture, resolution, and escalation. The agent selects the next diagnostic step based on what the user actually says, so the flow adapts per response.- Collects symptoms conversationally and asks targeted follow-ups only when information is missing.
- Runs diagnostics and captures artifacts such as device serials and logs through tool calls mid-conversation.
- Escalates to a human agent with the full collected context when the issue is not resolved.
Setup and Onboarding Guidance
Everest delivers step-by-step installation and configuration walkthroughs drawn from your knowledge base. Multi-step flows support conditional branching, so the path changes based on device model, firmware version, or the user’s answers. Guidance follows structured workflow steps while adapting to the user, so the agent can rephrase, repeat, or skip steps the user has already completed.Natural Language Understanding
Ambiguous, multi-intent, or poorly phrased inputs are interpreted in context, and the agent asks a clarifying question when intent genuinely cannot be resolved.- Tolerates typos, acronyms, and domain-specific terminology without pre-registered synonyms.
- Handles multiple intents in one message and addresses each in turn.
- No intent catalog to build or maintain.
Context Persistence Across Turns
Variables collected during a conversation, such as device serial, account ID, and issue description, are captured into session state and persist across every turn. The session store retains these variables for the life of the session, and the engine reuses them automatically. Users are never asked for the same information twice, even when the conversation moves between free-form dialogue and structured workflows.Sentiment Detection and Priority Escalation
The engine classifies frustration and urgency on every turn, so signals like sarcasm and indirect anger are caught, not just keyword matches. Each classification is logged as a sentiment signal on the conversation transcript for review and reporting.- Configurable thresholds define when sentiment triggers an action.
- Available actions include raising queue priority and initiating human handoff.
- Sensitivity and actions are adjustable per deployment.
Multilingual Support
Everest understands and responds in more than 30 languages and follows the user’s language automatically without a language selector. Translation is built into the conversation engine rather than layered on through a separate service, so responses keep full conversational quality in every language. Translation quality is continuously evaluated.Hybrid Conversation and Structured Flow Architecture
Everest combines free-form conversation with deterministic workflows in one runtime.- Authors build workflows in a visual builder using conversation, form, branch, and subflow nodes. Workflows compile to the conversational runtime.
- The router hands control to a workflow when its trigger condition matches the conversation.
- While a workflow is active, its steps drive the conversation deterministically; the engine still handles phrasing and interprets user replies.
- Free-form conversation resumes when the workflow completes or the user diverges from it.
- On conflict, workflow instructions take precedence over free-form generation.