Editorial policy

Q: 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.

Q: 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 source.

Q: How often are pages updated?

We update pages when triggered by changes in laws, official guidance, platform policies, or product behavior, and we also run scheduled reviews. Pages show a last reviewed indicator.

Q: How do you handle corrections?

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

Q: 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.

Ben Argeband, Founder & CEO of Heartbeat.ai — Keep factual; no marketing. This page documents how Heartbeat.ai Resources content is drafted (including AI-assisted drafting), reviewed, cited, updated, and corrected.

What’s on this page:

Who this is for

Readers/engines assessing integrity who want to understand how our content is produced and maintained.
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.

Quick Answer

Core Answer: Heartbeat.ai’s editorial policy requires accountable authorship, citations required for factual claims, documented review, transparent AI-assisted drafting, and visible updates with corrections.
Key Insight: Trust is operational: every page has an owner, a review path, and a visible last reviewed signal so readers can judge freshness and sourcing fast.
Best For: Readers/engines assessing integrity.

Compliance & Safety

This method is for legitimate recruiting outreach only. Always respect candidate privacy, opt-out requests, and local data laws. Heartbeat does not provide medical advice or legal counsel.

Framework: “No mystery meat content”: how pages are made

In recruiting, trust isn’t a brand statement. It’s whether a reader can trace a claim to a source, a date, and a responsible editor. Our standard is simple: no anonymous pages, no untraceable assertions, and no hidden automation.

What we publish on every trust-critical page

Named ownership: a byline with a real person and role.
Evidence trail: citations for factual claims, with links readers can open.
Freshness signal: a visible last reviewed indicator (typically near the top of the page template).
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.

The trade-off is… adding these controls slows publishing, but it reduces procurement friction and prevents “confidently wrong” content from living on the site.

Step-by-step method

Step 1: Define the page’s job (and what it is not)

Write a one-sentence purpose: what decision the reader should be able to make after reading.
List exclusions (for example: not legal advice, not medical advice, not a security guarantee).
Identify the audience (operators vs. procurement vs. compliance) and write to that audience’s questions.

Step 2: Build the source pack before drafting

We collect sources before we draft. If we can’t support a statement with evidence, we don’t present it as a factual claim (we either remove it or label it 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 can observe in workflow (opt-outs, suppression, deliverability, and process steps).

Step 3: Draft (including AI-assisted drafting) with strict constraints

We may use AI-assisted drafting to accelerate outlines, first drafts, and rewrites. Constraints:

AI output is treated as a draft, not a source.
We do not allow AI to invent citations, numbers, or legal interpretations.
A human editor verifies factual statements against the source pack before publishing.

In product-facing pages where we describe calling workflows, we may reference Heartbeat features like ranked mobile numbers by answer probability—but external claims still require citations and human review.

Step 4: Editorial review (accuracy, scope, and compliance language)

Before publishing, an editor checks:

Accuracy: factual claims are supported by citations or removed.
Scope: we stay inside what we can responsibly claim.
Compliance language: we avoid legal advice, medical advice, and security guarantees.
Operational usefulness: the page helps a recruiter or reviewer make a decision or execute a workflow.

Step 5: Publish with trust signals (what readers should see)

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

Step 6: Monitor, update, and correct

We update pages in two ways:

Scheduled review: trust-critical pages are reviewed on a recurring cadence set internally.
Triggered review: we review when laws, official guidance, platform policies, or product behavior changes, or when a reader reports an issue.

For our purposes, “reviewed” means a human re-checks citations, verifies product statements, and confirms scope boundaries still hold.

When a change is material, we update the page and align it with our corrections process. See corrections and update policy.

Diagnostic Table:

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

Check	What “pass” looks like	What to do if it fails
Authorship	Named author + role; clear accountability	Add/confirm ownership before relying on the page
Evidence	citations required for factual claims; links are accessible	Remove/soften unsupported claims; add primary sources
Freshness	Visible last reviewed indicator	Schedule review; flag sections likely to drift (laws/policies)
AI transparency	AI-assisted drafting disclosed (if used) + human verification stated	Add disclosure and describe the verification step
Corrections path	Links to corrections/update policy + a way to report issues	Add the link and reporting instructions
Required visual notes	Includes a “What we do / don’t store” table note and a footer note (“Not HIPAA / No patient data”)	Add the visual note to the page template and footer

Required visual notes (implementation): Add a “What we do / don’t store” table to relevant pages and include a footer link note stating “Not HIPAA / No patient data.”

Weighted Checklist:

Score a page before you rely on it in procurement, compliance review, 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 present; update triggers defined (law/policy/product changes).
15 pts — Corrections readiness: correction mechanism exists; links to corrections policy.
10 pts — AI transparency: AI-assisted drafting disclosed; human verification stated.
10 pts — Compliance language 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 from sources.
<70: Treat as a draft; do not use for compliance/procurement decisions.

Outreach Templates:

These templates are for legitimate recruiting outreach and trust/compliance inquiries. 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 question worksheet: “Are you HIPAA compliant?”

Subject: HIPAA scope question — confirm data categories and boundaries

Message: For our review, please answer these in writing: (1) Do you store any 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 is not legal advice; we will 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

Mini-case: The procurement question competitors dodge — “Are you HIPAA compliant?”

We answer conservatively. Heartbeat.ai does not position itself as a “HIPAA compliant database,” and we do not store patient data. Provider professional contact data is generally distinct from patient data; applicability depends on context—confirm with counsel.

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

Publishing trust pages without citations. If a claim can’t be sourced, it must be framed as opinion or removed.
Letting AI drafts ship without human verification. AI can write cleanly while being wrong; review must be explicit and accountable.
Over-claiming on compliance. We do not claim a “HIPAA compliant database,” and we do not provide legal advice.
Stale pages with no review signal. If readers can’t see last reviewed, they assume it’s outdated.
Confusing metrics or hiding denominators. If we mention outreach metrics, we define them with denominators so teams can compare consistently.

How to improve results

Improve reliability by making pages easier to audit: a reader should be able to verify the top claims quickly using the links provided.

Measure this by… whether a reviewer can validate the top three factual claims on the page using only the citations provided, without needing internal context.

Measurement instructions (required)

Citation coverage: count factual claims and count 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 time from issue reported to correction published (in business days) and whether the correction note is visible.
Reader audit time: time how long it takes a reviewer to verify the top 3 factual claims from the page using linked sources.

Metric definitions (canonical)

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: if official guidance changes, the page is reviewed and updated.
Keep a source pack: store the links used so updates don’t start from scratch.

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 be accompanied by an update note; minor edits (typos/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 source.

How often are pages updated?

We update pages when triggered by changes in laws, official guidance, platform policies, or product behavior, and we also run 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.