Platform

An engagement layer, not an unconstrained chatbot.

The controls are not bolted on after generation. Redaction, retrieval, scoping and validation are stages in the path a question takes, and a response that fails any of them does not reach the patient.

The path a question takes

Six stages, in this order.

  1. Intake

    The question is redacted before anything else happens

    Inbound text is scanned for personal identifiers and redacted at the edge of the system. The platform is built to avoid holding personal health information at all, so the redaction runs before retrieval, before generation, and before anything is written down.

  2. Retrieval

    Evidence is retrieved before an answer exists

    The question is matched against 11,000+ retrievable passages using vector and keyword search together. Retrieval happens first: the model is answering from a shortlist of real sources rather than producing text and looking for support afterwards.

  3. Scoping

    Each tenant sees its own corpus

    A deployment can be scoped to a defined portfolio, a therapeutic area, or its own private content. Scope is enforced during retrieval, so out-of-scope material cannot reach an answer even if it exists in the shared corpus.

  4. Generation

    Answers are assembled with citations attached

    Every substantive claim carries an inline citation pointing back to the passage it came from, with the credibility grade of that passage available alongside it.

  5. Validation

    Output is checked before it is shown

    Generated text passes a post-generation validator that blocks directive language, diagnosis and assessment, unattributed superlatives, and personal health information, and that requires citations to be present. Output that fails is not shown.

  6. Escalation

    What the system should not answer, it hands over

    Questions outside the boundary route to a human-response queue rather than being answered approximately. Potential adverse-event mentions are flagged on a separate path into pharmacovigilance review.

Built for regulated enterprise deployment

What a deployment gives your teams.

Source-grounded responses

Answers are tied to retrievable evidence rather than unsupported free-form generation.

Adverse-event capture

Potential AE mentions are flagged and routed into pharmacovigilance review workflows.

Human escalation

Questions outside the AI boundary move to a human-response queue for source-grounded follow-up.

Training-gap detection

No-result and low-rated questions reveal where content or retrieval needs improvement.

Conversation analytics

Understand what patients ask, where they struggle, and which support topics recur.

Multi-tenant isolation

Separate environments, API keys, webhooks, and feature flags per deployment.

Auditability

Operational logs support review of system activity, usage, and deployment behaviour.

Modular deployment

Ship as a patient experience, a branded support layer, a portal capability, or an integrated service.

See the pipeline run on your own questions.

Bring the questions your patient-services team actually receives, and watch where the system answers, cites, and declines.

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