Can it access patient records?

Only if you scope it to, and most deployments deliberately do not. A copilot over policies and procedures delivers most of the value with a far smaller compliance footprint, which is usually the right starting point.

How do different roles see different content?

Retrieval is scoped by role, so clinical, administrative, and management content is separated at the retrieval layer rather than filtered afterwards.

Does our data get used to train a model?

No. Your documents sit in your vector store and are retrieved at query time. We use enterprise API tiers with training disabled, and for stricter requirements we can run open-weight models entirely inside your own infrastructure.

How do you handle documents people should not see?

Permissions are applied during retrieval, so restricted documents are never candidates for a given user's query. Filtering after generation is not good enough — by then the content has already reached the model.

Internal AI Copilots · Healthcare

Internal AI Copilots for Healthcare

Staff should not need to ask a colleague where the policy lives.

Clinical and administrative staff lose real time to questions with documented answers: referral criteria, consent requirements, escalation procedures, which form applies. A copilot over your policy library answers those in seconds with the source attached.

Get a Free Assessment See Proof

To an answer: SecondsTo an answer
Every response: CitedEvery response
Per-user scoping: RBACPer-user scoping

Typical stack

RAG
Pinecone
LlamaIndex
Next.js
SSO

Use cases

What this handles for clinics and healthcare providers

Answer policy, procedure, and escalation questions with the source document cited
Give new staff a single place to ask instead of interrupting senior colleagues
Retrieve the correct form, pathway, or referral criteria for a given situation
Search training material and induction content on demand

Procedural questions answered instantly, with senior staff interrupted far less often.

Context

The healthcare problem underneath it

Healthcare administration consumes an extraordinary share of clinical capacity. Booking, rescheduling, intake forms, referral letters, insurance verification, recall lists — none of it needs clinical judgment, and most of it can run without a person in the middle. We build the administrative layer and stay firmly out of diagnosis.

Reception phones jam at opening while patients wait on hold or hang up
No-shows go unfilled because nobody has time to work a waiting list
Intake forms are completed on paper and then retyped into the practice system
Referral letters and reports arrive as PDFs that someone has to read and file

What we build

Inside an internal AI copilot

Retrieval quality is the whole game. We invest in how documents get chunked, indexed, and ranked before touching the answer layer, because a copilot that retrieves the wrong passage will confidently explain the wrong thing. Permissions are enforced at retrieval time, not filtered afterwards — the model never sees a document the user is not cleared for.

Ingestion pipelines for Drive, SharePoint, Notion, Confluence, PDFs, and email
Hybrid semantic and keyword retrieval tuned against real staff questions
Answers with inline citations linking to the exact source passage
Role-based access control enforced at the retrieval layer
Slack and Teams surfaces so people ask where they already work
Feedback capture that flags weak answers for retrieval tuning

Deliverables

What ships with the engagement

Connected document sources with scheduled re-indexing
Retrieval evaluation set built from questions your team actually asks
Chat interface on web plus Slack or Teams integration
Permission model mapped to your existing identity provider
Usage analytics showing what people ask and where answers fall short

FAQ