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How to evaluate an AI rep for regulated promotion.
Most AI sales agents are judged on pipeline. An AI rep for regulated medicine gets judged on something that comes first: what happens to each sentence before an HCP reads it. This page is the evaluation frame we would put to any vendor — including us — followed by a factual matrix of what each vendor publishes against it. Vendor cells quote or closely paraphrase public materials, cited in the footnotes; where a vendor has not published an answer, the cell says so plainly.
Six questions to put to any vendor.
One per row of the matrix below. They are the questions we would want a buyer to put to us, so we put them to everyone — ourselves included.
Does the system verify each individual claim against an MLR-approved corpus before the message is sent — or does it constrain generation to approved content and review afterward?
When verification cannot complete or a claim cannot be supported, does the system fail closed (refuse) — and is the refusal itself logged?
Are PAAB (Canada) and OPDP (US) promotional rules encoded as distinct rulesets, or configured by the customer in natural language?
Is the audit trail append-only (can anyone edit or delete a turn after the fact?), and can it be exported per-turn for an inspection or MLR review?
If an HCP mentions a possible adverse event mid-conversation, what detects it, what record is created, and who is notified — within what timeframe?
Was the compliance layer designed for regulated pharmaceutical promotion specifically, or is it a general-purpose guardrail framework applied to pharma?
What each vendor publishes, dimension by dimension
How to read the two kinds of column: the Zenreps column is written from our own code and internal capability ledger, scoped to what is live today and rounded down — the same discipline as the rest of this site. The vendor columns are drawn from each vendor's public materials as of July 2026 and claim nothing beyond what those materials say.
| Dimension | Zenreps | Salesforce Agentforce | Qualified (Piper) | AiSDR | Doceree RepTwin |
|---|---|---|---|---|---|
| Pre-send, per-claim verification against an MLR-approved corpus | On the gated text channels — web chat and Telegram — every reply passes a per-claim gate before send: ordered deterministic checks plus an LLM verifier, run against a corpus that serves approved-status documents and nothing else. The video avatar is a preview, audited after the fact, not pre-send gated. | Publishes topic classification ("map user inputs to specific topics") and toxicity flagging ("detected and flagged"). Per-claim verification against an MLR-approved corpus: not published.4 | Publishes knowledge-base curation ("stays on message") and after-the-fact response tracking with feedback. Pre-send per-claim verification: not published.8 | Publishes deliverability controls — mailbox warmup, sending caps, and reputation monitoring. Pre-send claim verification: not published.9 | Publishes a constrain-to-approved-content model: "Every response the system generates draws from MLR-reviewed, approved content" and "There are no dynamically generated claims." A per-claim verification step on each outgoing message: not published.1 |
| Fail-closed refusal behavior | The verifier is fail-closed in production: when a claim cannot be supported against the approved corpus, the reply is refused rather than delivered, structural refusals run before the model is asked at all, and the refusal is written to the same audit record as any other turn. | Publishes customer-authored escalation instructions ("clearly describe any keywords, language, or requests that should trigger escalation") and human-handoff oversight patterns. Fail-closed refusal semantics: not published.3,4 | Publishes goals, guardrails, and rules-of-engagement configuration. Refusal behavior: not published.8 | Not published in the materials reviewed (July 2026). | Publishes escalate-on-out-of-scope behavior: it "escalates to a human rather than generating an unapproved answer." Fail-closed wording: not published.1 |
| Jurisdiction-aware rulesets (PAAB / OPDP) | Canada and United States rulesets are encoded in the gate itself for both drug and device — PAAB (CA) and OPDP (US) rules on the drug side, with distinct device rulesets. EU and UK rulesets exist in provisional, drafted form and are not live — no EU or UK tenant onboards today. | Published guardrails are authored by the customer in natural language ("Using natural language, you can specify guidelines for the agent's behavior"). PAAB or OPDP rulesets: not published.3 | Not published in the materials reviewed (July 2026). | Not published in the materials reviewed (July 2026). | Publishes configurable regional rules: "Regional rule sets are configurable for brands operating across markets with different promotional guidelines." PAAB or OPDP by name: not published.1 |
| Append-only per-turn audit trail, exportable for inspection | The audit log is append-only at the database level — updates, deletes, and truncation are blocked by database triggers, not policy — and every conversation exports per-turn as CSV for MLR or inspection review. | Publishes an Einstein Trust Layer audit trail: "timestamped metadata" covering the prompt, the original unfiltered response, toxicity scores, and collected feedback. Append-only property and inspection-export format: not published.5 | Publishes tracking of agent responses with human feedback. An audit trail: not published.8 | Publishes CRM activity logging ("logs all outreach activity automatically"). An append-only, exportable audit trail: not published.10 | Publishes "Every interaction is logged with a full audit trail accessible to MLR and legal teams." Append-only property and export format: not published.1 |
| Pharma-specific adverse-event detection and PV routing | A deterministic adverse-event capture layer is live: it writes a redacted adverse-event record and triggers pharmacovigilance escalation. A second, LLM-based detection backstop has shipped and been validated offline against an adversarial test set; its live verification is still in progress, so we count it as shipped, not proven. | Not published in the Agentforce and Einstein Trust Layer materials reviewed. Salesforce separately publishes life-sciences agent skills described in general compliance terms ("respecting regulatory guardrails"). Salesforce's news and product pages could not be retrieved for this review.6 | Not published in the materials reviewed (July 2026). | Not published in the materials reviewed (July 2026). | Names pharmacovigilance among its pharma agent areas ("medical information, clinical trial recruitment, access and reimbursement, and pharmacovigilance"). Detection and routing mechanics: not published.2 |
| Regulated-promotion design vs general-purpose guardrails | The gate is built around promotional-compliance targets: an approved-claims corpus, ISI and fair-balance pairing drawn from that corpus, and PAAB and OPDP rulesets in the pipeline itself. | Publishes general-purpose, cross-industry trust controls — topic classification, toxicity detection, zero data retention, an audit trail — plus separate life-sciences agent skills described as "respecting regulatory guardrails."4,5,6 | Publishes a general-purpose B2B pipeline agent: an inbound SDR guided by goals, guardrails, and rules-of-engagement.7,8 | Publishes a general-purpose outbound sales agent — email, LinkedIn, and calls — with deliverability guardrails.9 | Publishes "purpose-built for pharma," with a constrain-to-MLR-reviewed-content model and pre-deployment sandbox sign-off ("MLR teams test and validate responses in a dedicated sandbox").1,2 |
1. Doceree blog — How RepTwin's MLR-Ready AI Solves Pharma's Biggest Content Compliance Bottleneck (vendor blog, accessed July 2026): blog.doceree.com/how-reptwins-mlr-ready-ai-solves-pharmas-bi…
2. Doceree blog — RepTwin: AI HCP Engagement Platform (vendor blog, accessed July 2026): blog.doceree.com/reptwin-ai-hcp-engagement-platform
3. Salesforce Trailhead — Agentforce Agent Planning: Define the Agent Guardrails (vendor documentation, accessed July 2026): trailhead.salesforce.com/content/learn/modules/agentforce-ag…
4. Salesforce Trailhead — Trusted Agentic AI: Explore Agentforce Guardrails and Trust Patterns (vendor documentation, accessed July 2026): trailhead.salesforce.com/content/learn/modules/trusted-agent…
5. Salesforce Trailhead — The Einstein Trust Layer: Follow the Response Journey (vendor documentation, accessed July 2026): trailhead.salesforce.com/content/learn/modules/the-einstein-…
6. Salesforce Trailhead — Agentforce for Life Sciences Cloud: Explore Agentforce for Life Sciences (vendor documentation, accessed July 2026): trailhead.salesforce.com/content/learn/modules/agentforce-fo…
7. Qualified — AI SDR (vendor product page, accessed July 2026): qualified.com/ai-sdr
8. Qualified — Platform (vendor product page, accessed July 2026): qualified.com/platform
9. AiSDR — Platform (vendor product page, accessed July 2026): aisdr.com/platform
10. AiSDR — homepage (vendor site, accessed July 2026): aisdr.com
How this page was assembled
Put the same questions to us.
The matrix is the frame; the demo is the evidence. Ask a live gated rep the six questions above, watch the gate decide, and read the receipt it writes — Zenreps is pre-launch, and this page is held to the same round-down rule as the product.