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Your protocols, searchable the moment they matter.

A governed knowledge assistant built on your own clinical documentation, surfacing the relevant guideline, protocol or prior finding in seconds, with a citation to the exact source it came from.

Your clinical team already has the answers. They are in protocol binders, institutional guidelines, prior case notes and the memory of your most senior staff. We make that knowledge retrievable at the point of work, without the system ever making a clinical judgment.

The knowledge exists. Retrieving it is the bottleneck.

A technician, a nurse, a care coordinator or an audiologist hits something unfamiliar mid-shift. The protocol covering it exists: in a binder, a shared drive, a policy portal nobody opens, or the head of a senior colleague who is with a patient.

So they page someone senior, search for it themselves, or proceed on best judgment. All three cost time. Two of them produce inconsistency.

  1. 01

    Escalation as the default

    Frontline staff page a supervisor for questions the documentation already answers. The supervisor is mid-consultation. Twenty minutes disappear, twice, and the senior clinician’s time is the most expensive resource in the building.

  2. 02

    Inconsistency at the edges

    Two staff members handle the same unusual situation differently because each learned it from a different colleague. Neither is exactly wrong, but the variance shows up in quality review.

  3. 03

    Knowledge that walks out the door

    Your longest-tenured clinician holds the institutional memory of why protocols exist and how edge cases were handled. When they retire or leave, that knowledge is not in any system. It was in them.

A bounded corpus. A governed retrieval layer. A citation on every answer.

01

Bounded to your own documentation

We build on a defined corpus: your clinical protocols, institutional guidelines, approved reference materials and prior case documentation. Not the open internet. Not a model’s training data. If it is not in the corpus you approved, the system does not have it and will say so.

02

It admits what it does not know

When the corpus does not contain grounding for a question, the system says so and routes to escalation rather than generating a plausible-sounding answer. We test this explicitly: ambiguous scenarios are deliberately included in the evaluation set to verify the fallback fires when it should.

03

Every response cites its source

Each answer names the specific document, section and version it drew from, with a link to the source. A clinician can verify the answer against the original in one click. Unsourced assertions are a failure condition, not an acceptable output.

04

Scope guardrails in the architecture

Out-of-scope questions are refused structurally, not by prompt instruction. Clinical judgment questions, dosing decisions and diagnostic queries return a defined refusal and an escalation path. This is tested in every evaluation set.

The difference between this and a general-purpose AI assistant is what it is allowed to know and what it is required to show.

Every response has the same shape.

Predictable structure means frontline staff know where to look, and reviewers know what to evaluate. This is the response format we validated in production.

BlockWhat it containsPurpose
1 · Status lineWhat was asked, restated in operator terms, in 3 to 6 wordsConfirms the system understood the question before the reader invests time
2 · Plain-language summaryWhat the documentation says, in the language the staff member uses, not the language the policy usesRemoves the translation burden between policy prose and operational reality
3 · Why it mattersThe operational or regulatory context behind the guidanceTurns compliance from arbitrary into understandable, which drives adherence
4 · What to do nextConcrete numbered steps where the documentation specifies themActionable rather than informational: the difference between reference and support
5 · Edge case branchThe “if you cannot do that” path for the most common blocked scenarioPrevents the escalation that happens when step 3 turns out to be impossible
6 · ProvenanceSource document, section, version and linkVerifiability. A clinician can check the original in one click.
7 · Escalation pathWhen the question exceeds scope or the corpus lacks groundingEnsures the system routes to a human rather than filling the gap itself
Luveo HealthEnterprise pharmacy operations platform

Validated in a regulated production environment.

For Luveo Health we built a governed copilot that translated compliance rule outcomes into cited, plain-language guidance for pharmacy technicians. Different domain, regulatory rather than clinical, but the same architecture, the same guardrails and the same non-negotiable requirement: every response traceable to a source, and no fabricated citations under any conditions.

The evaluation set deliberately included ambiguous scenarios where the correct behavior was to decline. The system produced the calibrated fallback in every one of those cases. That is the behavior that matters most in a clinical context: not accuracy on easy questions, but honesty on hard ones.

Read the case study →
  • 97%Explanation accuracy across 30 scenarios
  • 100%Chat accuracy
  • 0Fabricated citations
  • 0Guardrail violations

For organizations where clinical consistency is an operational problem.

  • Telehealth platforms with employed clinical staff

    Audiologists, pharmacists, nurses or care coordinators handling patient-facing workflows at volume, often without real-time access to a senior clinician. Consistency across a distributed team is the recurring challenge.

  • Pharmacy and infusion operations

    Technicians navigating handling, storage, compounding and documentation requirements, where the protocol exists but retrieving the right one under time pressure is the bottleneck.

  • Research and clinical operations teams

    Teams that need to answer “have we encountered this before, and what did we do” across prior program documentation, assay records and internal reports.

One corpus. One workflow. Four weeks.

We scope each cycle to a single bounded corpus and one clinical workflow family. Narrow enough to validate properly, which in a clinical context matters more than breadth.

  1. Week 1

    Corpus + scope

    We define the document corpus with your clinical leads, establish scope boundaries and refusal conditions, and stand up the environment.

    You getAn approved corpus definition and a written scope boundary document.
  2. Week 2

    Retrieval + grounding

    We build the governed retrieval layer with provenance tracking and shape the response format around your workflow.

    You getA working assistant answering questions on your real documentation, with citations.
  3. Week 3

    Evaluation

    We build the scenario evaluation set, including out-of-scope and ambiguous cases, and your clinical reviewer scores the outputs.

    You getA scored report on accuracy, refusal behavior and citation integrity.
  4. Week 4

    Production

    Deployment into your environment, staff walkthrough, documentation and the expansion roadmap.

    You getA production system your team owns, plus a written scale roadmap.

Your clinical reviewer signs off on the evaluation before the system reaches staff. We do not deploy a clinical knowledge assistant your own clinicians have not validated. How we work →

Reasonable questions.

Is this a medical device? Do we need FDA clearance?
No, and that is by design. The system does not diagnose, recommend treatment or analyze physiological signals. It surfaces reference material from your own documentation with a citation and requires a qualified clinician to review before acting. We document this positioning at engagement start and design to hold it. If a use case you want would cross into SaMD territory, we will tell you that rather than build it.
How is this different from ChatGPT with our documents uploaded?
Three structural differences. The corpus is bounded and approved, so the system cannot draw on model training data. Every response carries verifiable provenance, and unsourced output is a failure condition rather than a normal one. And refusal behavior is tested: we build an evaluation set including out-of-scope and ambiguous cases, and your clinician scores whether the system declined when it should have.
What if it gives a clinician wrong information?
Every response cites the specific source document and section, so a clinician can verify against the original immediately; that is the primary control. Beyond that, the week-three evaluation is scored by your clinical reviewer before the system reaches staff, and the corpus is versioned so you can see exactly which document version produced a given answer. The system is reference material subject to clinical review, and it says so in every response.
Our protocols change. How does the system stay current?
The corpus is versioned. When a protocol is updated, the new version is ingested and the prior version is retained for audit, so you can demonstrate which guidance was in force at a given time. Ongoing corpus maintenance is available as a managed operations engagement after the first cycle.
Does this touch PHI?
It depends on the corpus. Protocol and guideline documentation typically contains none. If the corpus includes case documentation, it is built for that. We execute a BAA before any data access, work in isolated environments in your cloud, and tokenize PHI at the ingestion boundary so protected data never reaches the AI layer directly. More on security & PHI →

Bring us one workflow and one binder.

If we can scope it into a four-week cycle, we will tell you exactly what that includes and what it costs. If it belongs outside our scope, we will tell you that too.

Book a 30-minute scoping call →
  1. We’ll ask three questions
  2. 01Which workflow generates the most escalations?
  3. 02Where does the documentation live today?
  4. 03What would leadership need to see to trust it?