Relevance AI

What it is

Relevance AI is a no-code platform for building and running teams of AI agents — what it calls an “AI workforce” — over the tools an ops team already runs. You assemble agents in a visual builder by writing natural-language instructions, picking an underlying model (Claude, Gemini, or OpenAI), and granting each agent a set of actions it is allowed to take. Agents can call each other: a “BDR” job is really a research agent, a scoring agent, and an outreach agent working as a team under one playbook. The platform leans on a four-level autonomy ladder — Assisted, Copilot, Autopilot, Self-Driving — so a process can start with a human approving every step and graduate to running on its own once you trust it. It connects to 1,000+ tools natively and supports MCP for the rest. The company is Sydney-founded, raised a $24M Series B led by Bessemer Venture Partners in May 2025 (about $37M total), and lists Qualified, SafetyCulture, Canva, KPMG, and Autodesk among its customers; it reported 40,000 agents created on the platform in January 2025 alone.

Where it fits across ops

The builder is general-purpose, but the center of gravity is GTM. The pre-built agents Relevance markets are sales-shaped:

• Bosh — an AI BDR/SDR that researches leads, enriches contacts, scores against ICP, writes outreach, handles replies, books meetings, and updates the CRM. Bosh is itself a multi-agent system, custom-onboarded for enterprise rather than self-serve.
• Apla — an account-research agent that preps AEs with a per-account brief before calls.
• Back-office processes — outside sales, teams build their own agents: a legal-ops intake triager that routes contract requests, or a recruiting agent that summarizes a candidate against the scorecard. These are builds, not templates you switch on.

Because every agent runs through the same connector graph, model layer, and approval gates, a RevOps agent and a recruiting agent are governed under one policy instead of two disconnected point tools.

Pricing

Relevance AI is usage-based, metered in two units: Actions (each step an agent takes) and Vendor Credits (the model cost behind those steps). Seats are not the meter — every plan includes unlimited agents.

• Free — $0, ~200 Actions/month, one builder. Enough to prototype one agent end to end.
• Pro — about $19/month, for individuals building and testing agents often.
• Team — about $234/month, adds calling/meeting agents, more builders plus end users, and analytics.
• Enterprise — custom; this is where Bosh and SSO/data-residency controls live. Annual billing runs roughly a third cheaper than monthly.

The headline seat price is not the budget. Connect your own Anthropic or OpenAI keys to pay provider rates directly and skip the credit markup, then size the real number off Action volume once agents run at production cadence.

Best for

• RevOps and GTM teams that want to stand up multi-step agent processes — research, score, outreach, follow-up — with human approval gates, and that have someone to own agent design.
• Ops teams of any function that prefer a no-code builder over an engineering project, and that want model choice and MCP-native tool calling rather than a single hardwired model.

Alternatives and when to pick them

• Dust — pick when the job is a knowledge-grounded assistant over your SaaS (“where’s our position on uncapped indemnity?”) more than a process automation that takes actions. Dust leads on retrieval and transparent flat pricing; Relevance leads on autonomous multi-agent processes and pre-built GTM agents.
• Microsoft Copilot Studio — pick when your team lives in Microsoft 365 and you want agents inside that estate with the largest default install base, not a separate platform.
• Lindy — the fastest-growing no-code agent-automation entrant buyers weigh against Relevance. Pick it for lighter individual-to-team automations; Relevance pulls ahead on enterprise governance and the autonomy ladder.
• n8n — pick when you want to own the orchestration yourself and wire models into a DIY workflow rather than buy a managed agent layer.

Watch-outs

• Actions plus Vendor Credits are the real budget, not the $19 seat. A chatty multi-agent process burns Actions fast, and model credits scale with retrieval and tool calls. Guard: run a 30-day pilot on your single highest-volume use case, read Action and credit consumption off the dashboard before signing Enterprise, and connect your own model keys to pay provider rates.
• The marquee agents are GTM; the “AI workforce” claim is broader than the out-of-box templates. Bosh and Apla are sales-shaped. For a legal-ops or recruiting process you are assembling agents in the builder, not buying a finished worker. Guard: scope a build sprint with a named owner before assuming an off-sales process ships in a week.
• Autonomy without governance is silent bad output at scale. A self-driving agent that writes to Salesforce or sends email can degrade deliverability or data quality before anyone notices. Guard: keep write-capable agents on the Assisted/Copilot rungs behind approval until reply-rate and accuracy metrics hold, and promote to Autopilot one process at a time rather than flipping the whole workforce to L3 on day one.