Medical Chatbots
Grounded, guard-railed assistants for triage, intake, and patient questions — never a hallucinated dose, always a citation, always a handoff when it matters.
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What makes a chatbot safe in a clinical setting?
A generic LLM wrapper answers confidently whether or not it actually knows — which is fine for a marketing FAQ bot and unacceptable the moment a patient asks about a medication interaction. Medical chatbots built for triage, intake, or patient questions need retrieval grounded in your own clinical content, an answer that carries a citation back to that source, and a refusal behaviour that hands off the moment a question needs real clinical judgement.
We design the guardrails before the conversation flow — what the assistant is allowed to answer, what it must escalate, and how a handoff to a human happens without the patient having to repeat themselves.
What the build covers
Grounded retrieval
Answers are retrieved from your actual clinical content and protocols, with a citation attached — not generated from the model's general training.
Guardrails & refusal behaviour
Explicit rules for what the assistant answers versus escalates, so it refuses and hands off rather than guessing on anything requiring clinical judgement.
Triage & intake flows
Structured intake conversations that capture the information a clinician actually needs, formatted for the record rather than a chat transcript.
Human handoff
A handoff to a live person carries the full conversation context, so a patient never has to explain the same thing twice.
Conversation logging
Every conversation is logged and reviewable, so quality and safety review isn't guessing at what patients were actually told.
EHR / system integration
Intake data and triage outcomes flow into your existing record via HL7 or FHIR where applicable.
How the build runs
Define the guardrails
Before any conversation design, we agree with your clinical team exactly what the assistant is allowed to answer and what it must always escalate.
Build the retrieval layer
Your clinical content and protocols are indexed so answers are grounded and citable, not generated from general model knowledge.
Design & test the flows
Triage, intake, and Q&A flows are built and tested against real questions, including deliberately adversarial ones, before any patient sees them.
Pilot with review
A limited pilot with full conversation logging lets your clinical team review real interactions before wider rollout.
Medical Chatbots FAQ
The tools we build with
Grounded retrieval and explicit refusal behaviour are treated as requirements, not tuning — a model that guesses on clinical judgement doesn't ship.
Clinical AI
Anthropic
pgvector
PythonPlatform
React
Node.jsPostgreSQLInteroperability
Compliance & ops
AWS
Terraform
OpenTelemetryRelated work
Related reading
A chatbot nobody on your clinical team trusts yet?
Tell us where triage or intake is eating clinician time. We'll scope an assistant your team would actually sign off on.