Use Cases

Multi-tenant support agents that handle inquiries, route tickets, and escalate to humans when needed.

What you get:

• Each customer gets isolated agents with their own knowledge base
• Per-tenant memory (conversations don't leak between customers)
• Webhooks to integrate with existing ticketing systems
• Observability to track resolution rates and agent performance

Example: A SaaS company embeds MUXI-powered agents into their product. Each of their customers gets a white-labeled support agent trained on their specific docs.

Agents that help users draft, edit, and research content with context from their documents.

What you get:

• Knowledge ingestion from user-uploaded docs
• Multi-agent workflows (researcher → writer → editor)
• Streaming responses for real-time typing feel
• User credential storage for integrations (Google Docs, Notion, etc.)

Example: A content platform lets users upload their brand guidelines. Agents write drafts that match the user's voice and style.

Industry-specific agents for legal, healthcare, finance, real estate, etc.

What you get:

• Domain knowledge from specialized documents
• Compliance-friendly (self-hosted, data stays on your infra)
• SOPs to enforce industry-specific workflows
• Human-in-the-loop for high-stakes decisions

Example: A legal tech startup runs contract review agents. Each law firm client gets isolated agents trained on their precedent library.

Code assistants, documentation bots, PR reviewers for developer products.

What you get:

• Knowledge from codebases and docs
• Tool access (GitHub, Jira, Slack, databases)
• Multi-agent teams (code reviewer + security scanner + docs writer)
• API-first for IDE and CLI integrations

Example: A DevTools company offers an AI assistant that understands their SDK. Agents answer questions, generate code samples, and debug issues.

Agents that answer questions from company wikis, policies, and documentation.

What you get:

• Knowledge ingestion from Confluence, Notion, SharePoint, or local files
• Answers grounded in your actual docs (not hallucinations)
• Access controls (different agents for different teams)
• Incremental indexing (docs update automatically)

Example: A 500-person company deploys an agent that answers HR policy questions, onboarding procedures, and IT help desk queries.

Agents that execute multi-step processes across internal systems.

What you get:

• Tool integrations (Slack, Jira, Salesforce, internal APIs)
• SOPs to define step-by-step procedures
• Triggers to kick off workflows from events
• Approval gates for sensitive actions

Example: An ops team builds an agent that handles employee offboarding - disables accounts, revokes access, updates HR systems, notifies managers.

Agents that query databases and generate reports in natural language.

What you get:

• Database tools (PostgreSQL, MySQL, BigQuery, etc.)
• Natural language to SQL
• Scheduled reports via triggers
• Charts and artifacts in responses

Example: A sales team asks "What were our top 10 deals last quarter?" and gets a formatted report without touching a dashboard.

Run your own support agents without sending customer data to third parties.

What you get:

• Complete data control (runs on your infrastructure)
• Integration with existing CRM/ticketing
• Custom knowledge from your support docs
• Escalation workflows to human agents

Example: A fintech company needs AI support but can't use cloud AI services due to compliance. They self-host MUXI behind their firewall.