Editorial Policy (Heartbeat.ai Resources)

Last updated: August 31, 2026

Ben Argeband, Founder & CEO of Heartbeat.ai — This page explains, in plain terms, how Heartbeat.ai Resources content gets written (including AI-assisted drafting), checked, sourced, updated, and corrected. No marketing language, just the process.

Who this is for

• Readers and search/AI systems assessing integrity who want to know how our content gets made and kept current.
• Procurement, compliance, and security reviewers who need clear boundaries on what we do and don’t claim.
• Recruiters and operators who want to know whether a page is current, sourced, and accountable before they act on it.

Quick answer

Core answer

Our editorial policy requires named authorship, citations for factual claims, documented review, disclosed AI-assisted drafting, and visible update and correction paths.

Key insight

Trust here is operational rather than aspirational: every page has an owner, a review step, and a last reviewed signal so readers can judge freshness and sourcing without guessing.

Best for

Readers and reviewers assessing whether a page can be relied on.

Compliance & safety

This page is informational and covers editorial process, not medical or legal guidance. Heartbeat does not provide medical advice or legal counsel.

How pages get made

In B2B data and recruiting content, trust isn’t a tagline — it’s whether a reader can trace a claim back to a source, a date, and a named editor. Our standard: no anonymous pages, no untraceable assertions, no hidden automation.

What appears on every trust-critical page

• Named ownership: a byline tied to a real person and role.
• Evidence trail: citations for factual claims, with links a reader can actually open.
• Freshness signal: a visible last reviewed indicator near the top of the page.
• Correction path: a link to our corrections policy and a way to report issues.
• AI transparency: if AI-assisted drafting was used, we say so and describe the human review step that followed.

The trade-off is real: these controls slow publishing. In exchange, they reduce procurement friction later and keep confidently-wrong content off the site.

Step-by-step method

Step 1: Define the page’s job — and what it isn’t

• Write a one-sentence purpose: what decision should the reader be able to make after reading?
• List exclusions up front (not legal advice, not medical advice, not a security guarantee).
• Identify the audience — operator, procurement, or compliance — and write to that audience’s actual questions.

Step 2: Build the source pack before drafting

Sources get collected before a word is drafted. If a statement can’t be supported with evidence, it doesn’t get presented as fact — it’s either removed or labeled as opinion/experience.

• Primary sources: regulators, statutes, official guidance, standards bodies.
• Product truth: what Heartbeat.ai actually does today, documented internally.
• Operational truth: what a recruiter observes in the workflow — opt-outs, suppression, deliverability, and process steps.

Step 3: Draft — including AI-assisted drafting — under strict constraints

We sometimes use AI-assisted drafting to speed up outlines, first drafts, and rewrites. The constraints are firm:

• AI output is treated as a draft, never as a source.
• AI is not allowed to invent citations, numbers, or legal interpretations.
• A human editor checks factual statements against the source pack before anything publishes.

On product-facing pages describing calling workflows, we may reference features like ranked mobile numbers by answer probability — but external claims still need citations and human sign-off, same as everything else.

Step 4: Editorial review — accuracy, scope, compliance language

Before a page goes live, an editor checks:

• Accuracy: factual claims are either sourced or cut.
• Scope: the page stays inside what we can responsibly claim.
• Compliance language: no legal advice, no medical advice, no security guarantees.
• Operational usefulness: the page actually helps someone make a decision or run a workflow.

Step 5: Publish with visible trust signals

• Byline and author note.
• Evidence notes with citations.
• A visible last reviewed indicator.
• Links to related trust pages — methodology and corrections.

Step 6: Monitor, update, correct

Pages get updated two ways:

• Scheduled review: trust-critical pages are reviewed on a recurring internal cadence.
• Triggered review: a change in law, official guidance, platform policy, or product behavior — or a reader-reported issue — kicks off a review.

“Reviewed” means a human re-checks citations, verifies product statements, and confirms the scope boundaries still hold. When a change is material, the page updates and aligns with our corrections process. See corrections and update policy.

Diagnostic table

Use this to audit whether a Heartbeat.ai Resources page meets our editorial policy.

Weighted checklist

Score a page before relying on it for procurement, compliance, or operational decisions. Total: 100 points.

• 30 pts — Evidence quality: primary sources linked; claims traceable; no unsourced numbers.
• 20 pts — Review clarity: named author; editorial review described; scope boundaries stated.
• 15 pts — Update hygiene: last reviewed indicator present; update triggers defined.
• 15 pts — Corrections readiness: a correction mechanism exists and links to the policy.
• 10 pts — AI transparency: AI-assisted drafting disclosed; human verification stated.
• 10 pts — Compliance discipline: no legal advice, no security guarantees, no prohibited claims (for example, “HIPAA compliant database”).

Interpretation:

• 85–100: Safe to cite internally with normal diligence.
• 70–84: Use with caution; verify key claims directly against sources.
• Below 70: Treat as a draft; don’t use for compliance or procurement decisions.

Outreach templates

These templates are for legitimate recruiting outreach and trust/compliance questions. Customize to your workflow and keep records of opt-outs.

Template 1 — Reader-reported correction request

Subject: Correction request for [URL] — [brief issue]

Message: I’m flagging a potential issue on [URL]. The statement “[quote the line]” appears inconsistent with [source/link]. Suggested correction: [your proposed wording]. Please confirm when updated and note the change in your corrections log.

Template 2 — Procurement worksheet: “Are you HIPAA compliant?”

Subject: HIPAA scope question — confirm data categories and boundaries

Message: For our review, please answer in writing: (1) Do you store patient data? (2) Do you store PHI? (3) What categories of data do you store (for example, provider professional contact details)? (4) Do you claim a “HIPAA compliant database”? We understand this isn’t legal advice; we’ll confirm applicability with counsel.

Template 3 — Internal editorial review request (pre-publish)

Subject: Editorial review needed — [page title]

Message: Please review [draft link] for (1) factual accuracy with citations, (2) scope boundaries (no legal advice/security guarantees), (3) compliance language, and (4) operational usefulness. Confirm the last reviewed indicator is present and add evidence links.

Common pitfalls

The procurement question most vendors dodge: “Are you HIPAA compliant?”

We answer conservatively here. Heartbeat.ai does not position itself as a HIPAA compliant database, and we do not store patient data. Under HIPAA, the Privacy Rule applies to covered entities — health plans, health care clearinghouses, and certain health care providers — and to their business associates handling protected health information. Provider professional contact data, the kind used in recruiting outreach, is generally a different category from patient data, but whether any specific use triggers HIPAA obligations depends on context. Confirm with counsel rather than relying on a vendor’s self-description.

• What we will say: what data categories we do and don’t store, and where the boundaries sit.
• What we won’t say: legal conclusions, compliance certifications we don’t hold, or blanket guarantees.

• Publishing trust pages without citations. An unsourced claim gets framed as opinion or removed, not left dressed up as fact.
• Letting AI drafts ship without human verification. AI can write cleanly while being wrong; review has to be explicit and traceable to a person.
• Over-claiming on compliance. We don’t claim a HIPAA compliant database, and we don’t give legal advice.
• Stale pages with no review signal. If readers can’t see a last reviewed date, they’ll reasonably assume the page is outdated.
• Hiding denominators in metrics. When outreach metrics come up, we define them with denominators so teams can compare figures consistently.

How to improve results

Reliability improves when pages are easy to audit — a reader should be able to verify the top claims quickly using the links provided, without needing internal context.

Measurement instructions

• Citation coverage: count factual claims versus citations. Track “claims with citations / total factual claims” as a percentage.
• Freshness compliance: track “pages with visible last reviewed / total pages.”
• Correction cycle time: track days from issue reported to correction published, and whether the correction note is visible.
• Reader audit time: time how long it takes a reviewer to verify the top three factual claims from a page using only the linked sources.

Metric definitions

• Connect rate = connected calls / total dials (per 100 dials).
• Answer rate = human answers / connected calls (per 100 connected calls).
• Deliverability rate = delivered emails / sent emails (per 100 sent emails).
• Bounce rate = bounced emails / sent emails (per 100 sent emails).
• Reply rate = replies / delivered emails (per 100 delivered emails).

Governance controls that prevent drift

• Define “reviewed”: a human re-checks citations, verifies product statements, and confirms scope boundaries still hold.
• Define triggers: a change in official guidance triggers a review and update.
• Keep a source pack: store the links used so future updates don’t start from zero.

Legal and ethical use

Heartbeat.ai Resources content is informational and operational. It is not legal advice, medical advice, or a promise of compliance. For regulated topics — privacy, healthcare, employment — consult qualified counsel for your specific facts.

For HIPAA background, see official guidance from HHS: U.S. Department of Health & Human Services (HHS) HIPAA Privacy Laws & Regulations.

Evidence and trust notes

This editorial policy is part of our trust cluster. For how we evaluate sources and maintain trust signals across the site, see Heartbeat.ai Trust Methodology.

• Primary citation: HHS HIPAA Privacy Laws & Regulations — used for baseline definitions and scope framing, not legal advice.
• Related policy: Corrections & Update Policy — how we handle changes and errors.
• Material vs. minor edits: material changes update the page content and may include an update note; minor edits like typos or formatting may not.

Report an issue: If you believe a page is inaccurate or missing a citation, email support@heartbeat.ai with the URL and the exact sentence you’re flagging, or use the correction request template above.

FAQs

Do you use AI to write Heartbeat.ai Resources pages?

Yes, sometimes we use AI-assisted drafting for outlines and first drafts. A human editor reviews and verifies factual claims against sources before publishing.

What does “citations required” mean in practice?

Any factual claim that could change a decision — definitions, requirements, scope statements, measurable assertions — must be supported by a linkable source, preferably a primary one.

How often are pages updated?

Pages update when triggered by changes in laws, official guidance, platform policies, or product behavior, and they also go through scheduled reviews. Pages show a last reviewed indicator.

How do you handle corrections?

We correct errors and document material changes. The process is described in our corrections and update policy.

Are you claiming HIPAA compliance?

No. We do not claim a HIPAA compliant database, and nothing here is legal advice. For HIPAA scope questions, confirm your obligations with counsel.

Next steps

• Read the parent methodology: how we evaluate sources and maintain trust signals.
• Review how we handle changes and errors: Corrections & Update Policy.
• If you want to evaluate Heartbeat.ai for recruiting workflows, start here: create an account.

About the Author

Ben Argeband is the Founder and CEO of Swordfish.ai and Heartbeat.ai. With deep expertise in data and SaaS, he has built two successful platforms trusted by over 50,000 sales and recruitment professionals. Ben’s mission is to help teams find direct contact information for hard-to-reach professionals and decision-makers, providing the shortest route to their next win. Connect with Ben on LinkedIn.