AI Assistants & Copilots with LLMs
Types, features, workflows, and enterprise use cases.
Types, features, workflows, and enterprise use cases.
AI assistants and copilots built on LLMs support automation, reasoning, and workflow execution across business environments. They improve efficiency through natural language interfaces and system integrations.
Chat-based, task-based, multimodal, agentic, embedded in apps, or fully autonomous.
Reasoning, planning, retrieval, tool use, workflow orchestration, and system integration.
Summaries, insights, report generation, code support, decision assistance, and automation.
User provides a query, task, or file.
LLM interprets intent and generates a plan.
Assistant uses tools, APIs, or retrieval to complete steps.
Delivers results with reasoning, summaries, or completed tasks.
Automated agents, triage, case summarization, and resolution suggestions.
Email drafting, meeting notes, document creation, and research helpers.
Ticket resolution, tool orchestration, monitoring, and troubleshooting.
Conversational, general-purpose, limited autonomy.
Embedded in workflows, context-aware, specialized.
Tool-using, autonomous, capable of multi-step planning.
Yes, with governance, access controls, and auditability.
They enhance productivity rather than replace roles.
Yes, via APIs, RPA tools, or enterprise connectors.
Use LLM-powered copilots to transform workflows and accelerate productivity.
Get Started