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Grade BproductionSource date Oct 17, 2026· Last verified Oct 17, 2026

How does Numan use an AI agent for patient support?

Numan · Hospitals & Health Systems

Header image: A doctor checking a patient's blood pressure

Numan, a UK digital health company, uses Fin for patient support. Its team first manually reviewed 100% of Fin interactions, then moved to a 5% monthly audit, while maintaining a 90% CSAT, per Intercom.

Reported outcomes

CSAT
90%
Source [1] · self-reported
quality assurance
100% manual review at launch, then 5% monthly audit
Source [1] · self-reported

Outcomes are reported exactly as stated by the source; we have not independently audited them.

How it works

  1. Fin answers patient support questions.
  2. Every interaction reviewed manually at first; later a 5% monthly audit.

Risks & compliance

  • Incorrect answers to customers; human review and escalation paths needed
  • Vendor-published results; not independently audited
  • Health information; clinical questions must go to clinicians

Healthcare cases cover operational and commercial workflows. Nothing here is medical advice or a claim about a device's or drug's efficacy.

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Tools mentioned & how to build it

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Tools mentioned

  • Fin AI Agent (Intercom)
    Intercom's customer-service AI agent; the most-documented tool in the casefile.

Build a version yourself

  • n8n
    Open-source workflow automation with AI agent nodes; self-host or cloud.
  • HubSpot (Breeze agents)
    SMB-friendly CRM with Breeze AI agents for prospecting and support.
  • Relevance AI
    Build an 'AI workforce' of agents, e.g. BDR agents that research and book meetings.

All tools by category →

Sources

[1]Intercom (Fin)· vendor customer story· Undated (accessed Oct 17, 2026)
“The team began by manually reviewing 100% of Fin interactions to ensure quality, accuracy, and safety.”
Numan and Fin

Grade rationale: Named organization with a quantified outcome from the vendor or the organization itself (self-reported). Automated check on Oct 17, 2026: 1/1 quotes and 2/2 outcome claims matched the source. Compiled by the Agent Casefile research desk (AI-assisted; see methodology). Spot an error? Request a correction.

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