Built to HIPAA and GDPR handling standards, with associative retrieval across hundreds of millions of records and papers.
Structure this fifteen-minute follow-up into a SOAP note and code it.
These are exchanges the team published, replayed here. Nothing on this page calls a language model, and nothing claims to.
A prompt library, a node-by-node board, a financial model you can argue with, and the API as it is actually documented.
Each agent below is configured for Healthcare and life sciences work, and carries its own measured throughput, accuracy and latency.
Turns a consultation into a structured note against your own template, codes it, and marks every field it was not confident about.
Searches across the literature and your internal assay data together, and every claim it makes carries the paper it came from.
Every template is a chain of nodes with a trigger at one end and something the business actually needed at the other.
A faxed or emailed referral is read, the record matched, urgency assessed against your own criteria, and the appointment offered — with anything ambiguous sent to a person instead.
Structured notes with the uncertain fields marked, referrals triaged against your own criteria, and literature work that shows what has actually been replicated.
Problem
Notes are written after hours because the day is full of patients.
After
The note is structured from the encounter and waiting for review before the next patient is seen.
Problem
An automated note that quietly fills a gap is worse than no note at all.
After
Low-confidence fields are marked and left for the clinician. Nothing is silently invented.
Problem
A literature review takes a week and still misses the failed replications.
After
Findings are separated into replicated, single-source and contradicted, with the papers attached.
The agent marks what it was unsure of. A confident-looking note that is wrong is the failure mode this design exists to prevent.
Protected health information does not leave the boundary you configure, in any deployment mode.
Replication status stated per finding, so a single striking paper is not mistaken for a settled result.
What sold it internally was that it says when it is not sure. Our clinicians trusted it because it admits the gaps rather than papering over them.
A worked example, not a promise. Change the inputs on the full model and the arithmetic changes with them.
repetitive hours a year across 40 people at 8 hours each per week
hours automated at a 62% automation rate
labour value released a year, at $40 an hour fully loaded
Forty-six working weeks a year, before licence cost. Released hours are hours people spend on something else — whether that becomes cash depends on what you redeploy them to, and this figure does not assume you will.
One REST surface, documented request and response for each route, and a token you can revoke.
Routes
{
"data": [
{
"id": "agt_8fk2",
"name": "DataPulse",
"model": "nexus-dense-70b",
"status": "ready",
"quota": { "used": 4120311, "limit": 120000000 }
}
],
"has_more": false
}
Every tier states its own ceilings. Where a limit is not listed, there is not one.
For a team automating its first few processes.
Billed monthly, cancel at any time
For a department running automation as part of how it works.
Billed monthly, cancel at any time
For an organisation with regulators, auditors and a security review.
Billed monthly, cancel at any time
For deployments that cannot touch a public network at all.
Scoped to your deployment
For a team automating its first few processes.
20% below the monthly rate of $299
For a department running automation as part of how it works.
20% below the monthly rate of $1,290
For an organisation with regulators, auditors and a security review.
20% below the monthly rate of $4,900
For deployments that cannot touch a public network at all.
Scoped to your deployment
It marks what it is unsure about. Clinicians trusted it because it admits the gaps rather than papering over them.
11 minutes returned per encounter
Verified customer, 11 months, deployed in-region.