AI for healthcare

No, and not by instruction but by construction. Clinical practice guidelines do not enter the index, so there is nowhere for that answer to come from; a query asking for medical judgement is classified before retrieval and routed to the institution’s line; and the evaluation set carries clinical questions whose only correct answer is an escalation, measured and reported separately. That boundary is defined in writing with the institution before anything is built.

Health information is sensitive data and the design assumes it from the start. The index holds administrative and process documentation, not clinical records. When an answer needs a specific person’s data — the status of an authorization, say — it is fetched over API at that moment and not persisted. Where identifiable information is in play we deploy on Azure OpenAI or Amazon Bedrock inside the institution’s subscription, and filter personal data before inference.

No, and pitching it that way is where the projects that end badly come from. It takes the repetitive, documented part off the queue — requirements, hours, sites, administrative routes — which is usually most of the volume, and leaves the human team what needs judgement or context. The success measure we recommend is not "how many conversations it resolved" but "how many it escalated correctly": an assistant that never escalates is inventing somewhere.

Yes, over API, and that is what separates a useful assistant from an informational one. Without integration it can explain how an authorization works; with integration it can say where that person’s authorization is. We work against whatever exists — HIS, scheduling, patient portal — through MCP servers or direct integrations, with whatever permissions and access logging the institution requires.

It depends on volume and on the state of the documentation, not on size. If administrative queries run in the dozens per day and the instruction sheets fit in a few pages, a good FAQ handles nearly all of it and this is unnecessary. The conversation changes with several sites, services with different requirements, agreements that change, and documentation scattered across departments — that is when finding the right answer is already work.

Six to eight weeks to production for a defined scope, with something running from week one. Two things are needed from your side and neither is technical: somebody who can say which version of each document is in force, and somebody from the service area to validate the correct answers in the evaluation set. Without that the system can be built but not measured, and without measurement we do not recommend opening it to patients.

Almost always, and it is worth measuring before deciding. In one week we build a reference set from real questions off the channel, measure groundedness and escalation, and hand back the failures ranked by impact with each fix and its estimated effort. The usual cause is not the model: it is that the corpus mixes old and new versions of the same instruction sheet and the system has no way to know which one governs. If the diagnostic does not reach three actionable findings, it is not billed.