Kylon vs Relevance AI

Relevance AI builds an AI workforce. Kylon puts agents in the room with the human one.

Kylon is a company harness where agents join your team, understand your business context, and execute real work across the tools you already use.

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01 Which one is the right answer

Not every team should pick the same one.

Relevance AI is the right answer when

You are staffing a repeatable function, sales development or support triage, want it drawn as a workforce with explicit handoffs and per step approval, and need a completed SOC 2 Type II with a choice of US, EU or AU data residency today.

Kylon is the right answer when

The work is the team's daily work rather than one modelled process, everyone should be able to ask for it without being added as a builder, and the result belongs in the thread where the decision gets made.

02 Operating model

How each one works

Every statement about Relevance AI below cites Relevance AI's own documentation or pricing page. Checked 2026-09-09.

Where a run happens
Relevance AIA task is one run by an agent or a workforce. You watch it in the Run tab of the builder, or on a Tasks page that collects every task across all agents so you can triage escalations, errors and approvals in bulk.Source: Tasks, Relevance AI docs
KylonA run happens in the room where the team asked for it. The request, the work and the result are one thread that people and agents both read.
How you set one up
Relevance AIAgents are created by describing what you want with Invent, cloning from the marketplace, or building from scratch, then given tools and knowledge. A workforce is a team of agents connected on a drag and drop canvas, with handoffs the agent decides or you fix as a next step.Source: Workforces, Relevance AI docs
KylonYou ask for the work in the room in plain language. It becomes a saved workflow when you want it to repeat, and the agents that carry it are workspace members with their own permissions.
Company data and its limits
Relevance AIKnowledge is a retrieval system fed by uploads and syncs from Drive, SharePoint and Notion. Published quotas are 100 MB of upload space, 50,000 rows per CSV and 10 MB of raw text as knowledge.Source: System limits, Relevance AI docs
KylonConnected services stay where they are, and the records the work runs on live in the workspace next to the thread, readable and writable by the agent without an upload step.
Approval before a write
Relevance AIApproval is configured per edge on the workforce canvas, with auto run, approval required and escalation modes, and reviewers act from the workforce task view. In Super GTM the default is to ask before writing to any integration.Source: Approvals and escalations, Relevance AI docs
KylonOutbound and sensitive actions wait for a person in the room where the work is being discussed, not in a separate review console. Email goes out as a draft a person sends.

Where Relevance AI is the better tool

Relevance AI is SOC 2 Type II today, lets you pin data to a US, EU or AU region, and its workforce canvas makes a multi step process explicit in a way a conversation does not. We do not offer regional data residency. If that is a condition of purchase, that is a real reason to choose them right now.

Questions people ask

Is Kylon a replacement for Relevance AI?
Not if what you need is a modelled workforce for one repeatable function with an explicit canvas behind it. Kylon is the answer when the work is spread across a team's ordinary days and the agents have to work where that team already talks.
Can everyone on the team use it?
Yes. Pricing is workspace-based rather than per user, so the people who need the work done can ask for it directly in the room.
Does anything get sent automatically?
Email is drafted for a person to send, and sensitive actions wait for approval before they execute.

Keep reading

A workforce you have to draw is still a workforce someone has to run.

Put agents in the room where the work is already discussed, give them the records and tools the job needs, and review what they hand back.

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