Agents & teams
Profiles, skills, collaboration, tools and escalation patterns for agent-assisted work.
AI & Automation
AI that understands context and moves work forward.
Connect messages, documents, business data, models, agents and workflows so AI can help route, extract, search, summarize, recommend and act inside governed business processes.
Smart Relay
From incoming information to operational action.
Receive email, chat, documents, forms, scans or API data. Use rules and AI to understand context, then route the result into records, people, workflows, notifications or analytics.
Email & messagesDocumentsOCRSearchClassificationRoutingApprovalsWebhooks
AI operating layer
Context, models, tools and budgets in one governed path.
XTND AI capabilities are designed around real tasks: build the context, choose the right model, enforce policy and budget, use tools where allowed, and keep evidence of what happened.
Context, memory & RAG
Build task-specific context from business data, documents, retrieval and reusable context packs.
Model routing
Route by capability, cost, privacy, quality and runtime instead of hard-coding one provider.
Workflow automation
Use AI as one step in a controlled process.
Combine extraction, classification, model calls, tools, human approvals, retries and scheduled execution rather than treating every task as an open-ended chat.
Trigger
Context
Model
Tool
Approval
Audit
AI governance
Treat AI as a metered, policy-controlled platform resource.
XTND's AI architecture separates model/provider selection, budgets, usage logging, fallbacks, tool permissions and audit so each workflow can be governed according to its cost, risk and business responsibility.
Budgets & usage
Track consumption by tenant, user, module or capability and protect against uncontrolled spend.
Tool scopes
Limit which systems and actions an AI workflow can access.
Fallbacks & health
Choose alternate providers or runtimes when the preferred path is unavailable or unsuitable.
FAQ
AI and automation questions.
No. XTND is provider-agnostic at the architecture level. Workloads can be routed to different providers or runtimes based on capability, privacy, cost and availability.
Yes. Approval gates are important for business-critical, financial or otherwise consequential workflows.
AI inference, GPU/compute and provider-specific consumption are metered separately from the core platform footprint. See Pricing for the reference calculation model.
Yes. Routing and automation can use deterministic rules, and AI can be added only where it improves understanding or decision quality.