Last updated 08-10-2026
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Relevance AI
Relevance AI lets enterprise teams build specialist agents that each own one narrow job, like enriching CRM records, prepping sales calls, or drafting proposals. You compose agents on a visual canvas, connect them to 1,000+ apps, and route each task to the cheapest model that passes your eval bar. The platform bundles triggers, queues, tracing, and governance so you are not stitching together five separate tools.
Most agent builders stop at a demo chatbot. Relevance focuses on production metrics: it samples live runs, charts pass rates, and blocks versions that fail eval suites before they ship. A built-in LLM router swaps between providers such as Gemini, Claude, and GPT based on cost and score, which is how customers report cutting per-task spend while holding a 90%+ quality bar.
Sales, customer success, marketing, and HR teams use prebuilt agent templates, while engineers can drive the same runtime through MCP from Cursor or Claude Code. Enterprise plans add SSO, RBAC, audit logs, data residency, and a dedicated account manager for six-week deployment sprints.
Connect 1,000+ apps plus MCP gateways for governed tool access
LLM router picks the lowest-cost model that clears eval thresholds per agent
Built-in evals sample live runs and block failing agent versions before publish
Visual drag-and-drop builder for multi-agent orchestration with approval nodes
SOC 2 Type II and GDPR compliance with PII masking and audit logs
Managed job queue handles bursts so downstream rate limits do not drop tasks
All-in-one stack replaces separate routers, queues, eval tools, and tracers
Per-agent model routing cuts cost while evals hold quality above a set bar
Visual builder and MCP paths let business and engineering teams share one runtime
Enterprise security includes SOC 2, GDPR, SSO, and human-in-the-loop approvals
Public pricing is enterprise-only, so small teams must talk to sales for rates
Six-week deployment model targets larger orgs more than solo builders
Deep customization still needs time to map workflows and write eval suites
What does Relevance AI do?
Relevance AI is an enterprise agent platform for building, running, and monitoring specialist AI agents. Teams design agents on a canvas, connect CRM and support tools, route tasks across LLM providers, and enforce quality with built-in evals and tracing.
Who is Relevance AI for?
Relevance AI targets enterprise sales, customer success, marketing, and operations teams that need governed agents in production. Customers named on the site include Canva, Autodesk, and KPMG Australia.
How does Relevance AI pricing work?
Relevance AI publishes an Enterprise plan with custom pricing. Enterprise includes unlimited agents, users, and workforces, 2,000+ integrations, agent evaluations, SSO, RBAC, audit logs, and a dedicated account manager. Contact sales for a quote.
Does Relevance AI support multiple LLM providers?
Yes. Relevance AI routes each agent to major model providers and supports bring-your-own keys. The platform runs evals to pick the cheapest model that meets your quality bar rather than locking you to one vendor.
How does Relevance AI handle security?
Relevance AI offers SOC 2 Type II and GDPR compliance with data residency options, PII masking, audit logs, SSO and SAML, role-based access control, and a policy that customer data is not used to train models.
Can engineers build on Relevance AI with code?
Yes. Relevance AI exposes APIs and MCP support so engineers can create agents, link knowledge, and run evals from Claude Code, Codex, or Cursor while business users work in the visual builder.