Clinical documentation · Production

SynthioLabs builds clinical-grade voice agents for Pharma field teams using ElevenLabs

The problem

Global pharmaceutical companies struggle to scale field team coverage across large territories, and manual CRM entry reduces the time reps can spend engaging with healthcare professionals.

Workflow diagram · grounded in source
1
Pre-call HCP prioritization
Ai action
Users ask Jarvis to identify priority HCPs in a territory, surface recent activity, and retrieve trial summaries or clinical evidence.
▾ source quote
“users ask Jarvis to identify priority HCPs in a territory, surface recent activity, and retrieve trial summaries or clinical evidence. This replaces manual navigation through fragmented datasets.”
2
Scientific support summary
Ai action
Before an HCP visit, users request concise, on-label summaries of efficacy, safety, and comparative trial outcomes.
▾ source quote
“before an HCP visit, users request concise, on-label summaries of efficacy, safety, and comparative trial outcomes”
3
Post-call voice engagement
Trigger
After a meeting, users engage in a real-time voice conversation with Jarvis.
▾ source quote
“after a meeting, users engage in a real-time voice conversation with Jarvis”
4
Contextual capture and gap identification
Ai action
Jarvis listens contextually, captures key discussion points, and identifies gaps as the interaction unfolds.
▾ source quote
“The agent listens contextually, captures key discussion points, and identifies gaps as the interaction unfolds”
5
Structured CRM note generation
Output
Jarvis generates structured, compliant CRM notes and follow-up actions for review and submission.
▾ source quote
“Jarvis generates structured, compliant CRM notes and follow-up actions for review and submission - reducing administrative burden while improving completeness and consistency of documentation”
Reported outcome

Since adopting ElevenLabs, SynthioLabs' Jarvis agent delivered a 40% reduction in admin time, a 30% increase in HCP touchpoints, a 6x improvement in note and insight quality, and 2 hours of average daily usage among reps and MSLs.

Reported metrics
Admin time reduction40%
HCP touchpoints increase30%
Note and insight quality improvement6x
average daily usage among reps and MSLs2 hours
Reported stack
ElevenLabsJarvisCRM
◆ Does this fit your context?

Compare to your context

Tell us your scale, team, and constraints. We'll show what changes at your size, what fails at your scale, and whether this case is a fit, needs adaptation, or won't scale to you. Free demo, no signup.

Compare to your context →
~30 seconds · free
Source
https://elevenlabs.io/blog/synthio-labs
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Since adopting ElevenLabs, SynthioLabs' Jarvis agent delivered a 40% reduction in admin time, a 30% increase in HCP touchpoints, a 6x improvement in note and insight quality, and 2 hours of average daily usage among r…

What tools did this team use?

ElevenLabs, Jarvis, CRM.

What results were reported?

Admin time reduction: 40%; HCP touchpoints increase: 30%; Note and insight quality improvement: 6x; average daily usage among reps and MSLs: 2 hours (source-reported, not independently verified).

How is this clinical documentation AI workflow structured?

Pre-call HCP prioritization → Scientific support summary → Post-call voice engagement → Contextual capture and gap identification → Structured CRM note generation.

Related clinical documentation cases

WHAT TO DO WITH THIS

Now compare it to your context

This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.

Browse the Clinical documentation cases →