For pharma
Reach every prescriber with a rep that can't go off-label.
Drug promotion, PAAB- and OPDP-bound. Every answer a zenrep sends on web and Telegram is grounded in your MLR-approved corpus, paired with fair balance and ISI, and logged for your compliance team — before it reaches the HCP.
Three teams, one page — each sees the part they own.
Reach without growing the field force.
Reach every HCP on-label, at launch scale, without growing the field force — including the rural and low-volume prescribers a human team never gets to. Share of voice and coverage stop being a headcount problem.
Never off-label. Fair balance every time. Refuses rather than guesses.
Off-label and out-of-indication claims are refused at the fail-closed pre-send verifier, with deterministic per-class refusal templates — a reply that cannot be grounded on-label is never improvised. Efficacy is paired with the required safety information and ISI on every turn. When a claim can't be grounded in your approved corpus, a zenrep declines and routes to a human rather than guessing — and every turn lands in an inspection-ready audit trail. PAAB and OPDP framing, enforced in code.
The same trust spine as the rest of the platform.
Data residency in ca-central-1, a no-PHI-by-design text path, HIPAA/BAA posture activating at our first paying US customer, a SOC 2 roadmap stated honestly, and a published responsible-AI posture. The full detail — with subprocessors and a security contact — lives on the trust page.
Read the trust & security postureThe gate, in drug-promotion terms
On the gated text channels — web chat and Telegram — every reply is constructed and checked before it reaches the HCP. For a drug, that means:
Corpus-grounding
A zenrep answers only from the MLR-approved materials you load — product monograph, ISI, approved claims. There is no general-model answer underneath.
Off-label refusal, fail-closed
Structural refusals (pediatric-on-adult, cross-tenant) fire before any model runs. Off-label and out-of-indication claims are refused at the fail-closed pre-send verifier, with deterministic per-class refusal templates — a reply that cannot be grounded on-label is never improvised.
Fair balance & ISI
Efficacy statements are automatically paired with the required safety information; a reply cannot send benefit without the balancing risk.
Adverse-event detection & PV routing
A two-layer detector — a deterministic pattern floor plus an LLM backstop — flags adverse-event reports, records the regulated intake, and pages pharmacovigilance. The deterministic layer is live-verified; the LLM backstop has shipped and is validated offline, with live verification in progress.
Inspectable audit trail
Every turn is written to an append-only record with database-level tamper protection; reconstruct any conversation and export CSV for an inspector.
Watch the gate work
Meet a drug rep and put the gate to the test — ask an off-label question, drop a claim your data does not support, and read the receipt. The video preview is a managed-LLM demo; the pre-send gate runs on the web and Telegram text channels (detail on the compliance page).
Bring your MLR team to the table.
A demo runs against a corpus that looks like yours, with your PAAB and OPDP questions front and center.