AllocateAI that answers patients,
on infrastructure that protects them.
Member support, triage, scribing, and scheduling agents that run inside an isolated boundary, trained on your own clinical and operational history.
Member support on WhatsApp
Answer, book, and escalate around the clock, with every conversation staying inside your boundary.
Clinical scribing and notes
Turn consultations into structured documentation in your templates, reviewed and filed automatically.
Intake and triage
Read referrals, classify urgency on your own protocols, and route to the right clinician with context attached.
A member support fleet answering in 6 seconds median, booking 41 appointments a day, and escalating with full context.
Models teams route here.
Start on frontier, fine-tune the open ones on your own data.
Common questions
Where does patient data live?
Inside a single-tenant isolation boundary provisioned for your organization. Conversations, documents, and any fine-tuned weights stay in it; nothing is used to train anyone else’s models, and egress is off by default.
Can the agents escalate to our clinical team?
Yes, and they should: escalation paths are part of the agent configuration, with full conversation context attached. The agent handles the routine volume; your team receives the cases that need judgment.
Do we have to start with our own trained model?
No. Most teams start on frontier models through a route name, then fine-tune an open-weights base once real conversations have accumulated. Your product code does not change when that swap happens.
How is spend controlled?
Every token is metered per route and per agent, with hard caps you set, so the bill is a function of usage you can forecast, not seats.
Does this work for South African healthcare?
That is where we started. See the South Africa page for the POPIA analysis and the in-country serving story.