Relevance AI

Relevance AI is a no-code workforce builder where businesses assemble AI agents that use tools, memory, and human hand-offs to run real processes.

Overview

Relevance AI frames agents as digital workers rather than scripts. Its builder combines a tool library, structured memory, and multi-agent teams so that a hiring screen, a sales research pass, or a support triage flow can be delegated end to end. Agents can be given roles, goals, and escalation rules, then embedded in Slack, HubSpot, or any system reachable through HTTP and its integration catalog. A template marketplace accelerates common go-to-market use cases, and an observability layer shows every step an agent took for audit and iteration. The platform is model-agnostic, letting teams swap between hosted LLMs or connect their own endpoints without rebuilding workflows. Recent additions include a workforce planner that suggests which processes to delegate first, plus evaluation dashboards that compare agent quality before and after prompt or model changes.

Key Features

• Role-based agent teams with defined goals and escalation paths
• Visual tool builder mixing integrations, code steps, and LLM calls
• Long-term memory slots for persistent facts about customers and work
• Model-agnostic runtime supporting hosted and custom LLM endpoints
• Step-by-step run logs for auditing and debugging agent behavior
• Evaluation dashboards that compare agent quality across prompt and model changes

Pricing

A free trial includes a limited agent run allowance. Paid plans are priced by monthly agent runs and workspace seats, with enterprise tiers adding SSO, private cloud deployment, and custom tool support.

Who Is It For

Operations, revenue, and support leaders who want to delegate whole processes to AI workers without a data-science team, and platform engineers who need a governed, model-agnostic agent runtime. It fits mid-market companies that want AI leverage now, with the option to bring engineers in later for deeper customization.