Email Verifier

Check a list of email addresses by syntax and by actually resolving the domain's mail servers (MX records) — with an honest, tiered verdict instead of a single misleading "valid/invalid". No live mailbox probe pretending to confirm delivery.

What you get

• Syntax check, domain resolution and MX lookup for every address — a real DNS answer, not a guess.
• A tiered verdict (valid_syntax_and_mx, invalid_syntax, domain_not_found, no_mx, unknown) instead of a flat true/false that hides WHY an address failed.
• Mail provider detection (Google Workspace, Microsoft 365, Yahoo, Yandex, Zoho, ProtonMail, Amazon SES, Fastmail, iCloud and more) from the MX records themselves.
• Disposable-domain and role-account (info@, admin@, support@...) flags — useful signals for cleaning a list before outreach.
• A known "catch-all" warning where the mail PROVIDER itself is documented to accept any address regardless of whether the mailbox exists (currently: Yahoo) — a static fact, not a live probe.
• Runs on Apify: schedule it, monitor it, call it from the API, export to JSON/CSV/Excel or push straight into your own pipeline.

How to run it

1. Click Try for free — no card needed on the free plan.
2. Paste your list of email addresses into Emails.
3. Hit Start and pull the results from the dataset (UI, API or webhook).

Pricing

Pay-per-event: $0.005 per run start + $0.001 per email verified. Every verdict — including a confirmed "this address is invalid" — is a real, delivered answer and is billed the same way; only a genuine DNS infrastructure hiccup (not a verdict on the address) is returned for free. 1,000 emails = $1.00 + the one-time start fee.

Input

Field	Required	What it does
emails	yes	Email addresses to verify. Max 100 per run.
maxConcurrency	no	How many emails to check in parallel (default 10).

{
    "emails": ["test@gmail.com", "not-a-real-email", "someone@this-domain-zzq123.example"]
}

Output

{
    "email": "test@gmail.com",
    "syntaxValid": true,
    "domainResolves": true,
    "mxFound": true,
    "mxRecords": [
        "gmail-smtp-in.l.google.com",
        "alt1.gmail-smtp-in.l.google.com",
        "alt2.gmail-smtp-in.l.google.com"
    ],
    "provider": "Google Workspace",
    "disposable": false,
    "roleAccount": false,
    "catchAllRisk": false,
    "verdict": "valid_syntax_and_mx",
    "mailboxChecked": false,
    "checkedAt": "2026-07-30T07:51:36.835Z"
}

Field	Meaning
verdict	valid_syntax_and_mx (domain can receive mail), invalid_syntax, domain_not_found (confirmed non-existent domain), no_mx (domain exists but cannot receive mail — including RFC 7505 "null MX" domains that explicitly opt out), or unknown (DNS infrastructure hiccup — not a verdict on the address).
mxRecords	Mail server hostnames, sorted by priority.
provider	Mail provider detected from the MX records, or null when none was found or matched.
disposable	Domain matches a static list of known temp-mail providers. Not exhaustive.
roleAccount	Local part looks like a shared inbox (info@, support@, ...) rather than a person.
catchAllRisk	true only when the detected PROVIDER is documented to accept mail for any address on its servers regardless of mailbox existence — a static, known-behavior flag, not a per-domain live test.
mailboxChecked	Always false. See FAQ.
error	null for every real verdict, including "invalid" ones; only set when a DNS infrastructure error prevented a verdict at all.

Related tools

Related tools for adjacent workflows in B2B lead generation and data enrichment.

Actor	What it does
Company Profile Lookup	Pair it in the B2B lead generation and data enrichment workflow: Turn a domain or company name into one unified company card: website tech stack (CMS, ecommerce, key tech)...
URL to Markdown Converter	Pair it in the B2B lead generation and data enrichment workflow: Fetch any URL and convert it into clean, LLM-ready Markdown — headings, links, lists and emphasis...
Company Registry Enricher	Pair it in the B2B lead generation and data enrichment workflow: Turn a company name, LEI or UK company number into an official registry card: legal name, status,...
Counterparty Risk Rollup — Sanctions, Courts, Registry, Hiring	Pair it in the B2B lead generation and data enrichment workflow: One call, one row per counterparty: sanctions screening (OFAC + EU), legal-entity registry (GLEIF),...
Intent Signal Aggregator	Pair it in the B2B lead generation and data enrichment workflow: Is this company in-market right now? Combines public hiring activity (Greenhouse, Lever, Ashby) and recent...

FAQ / Limitations

Does a valid_syntax_and_mx verdict mean the mailbox exists? No — it means the DOMAIN is correctly configured to receive mail. Confirming one specific mailbox exists requires an SMTP conversation with the mail server, which this Actor does not perform (mailboxChecked is always false). Cloud platforms — this one included — commonly cannot open outbound port 25 at all, which is why a live per-mailbox check is not offered as a false promise here.

What this is NOT. Not a deliverability guarantee, not a spam-score check, and not proof a specific person still reads that inbox. catchAllRisk flags a PROVIDER-level known behavior (some mail providers accept any address on their servers), not a per-domain catch-all test — that would require the same live SMTP conversation this Actor deliberately does not perform.

Found an address this Actor should classify differently, or need a bulk mailbox-check add-on for your case? Open an issue on this Actor's page.

Commercial guide: Email Verifier — Honest Syntax, Domain, and MX Intelligence

Classify email inputs with deterministic syntax and DNS/MX evidence while clearly stating that mailbox existence and final deliverability are not checked.

This guide is written for buyers, operators, analysts, and automation builders. It explains what the Actor observes, how to turn the Dataset into a controlled workflow, and where human verification remains mandatory.

The decision this product supports

Which addresses have deterministic structural or mail-domain problems, and which still require mailbox-level verification before use?

The Actor reduces collection and first-pass triage work. It does not remove responsibility for source verification or authorize an external business action. The commercial value comes from a structured, repeatable evidence layer: stable identity, observation time, source evidence, confidence, gaps, recommended action, and failure semantics travel with the raw facts.

Who uses it

User	Value
Small-business marketers	Remove confirmed syntax and mail-domain failures before spending campaign capacity.
Lead-generation agencies	Add explainable DNS/MX evidence and risk flags to a client-supplied list.
Sales operations	Separate safe suppression decisions from addresses that merely have a mail-capable domain.
CRM administrators	Audit imported addresses and route unknown infrastructure errors for retry instead of deleting them.
Data enrichment developers	Use a stable, source-aware row contract for bulk email hygiene workflows.
Compliance-conscious teams	Avoid presenting domain-level checks as proof that a person or mailbox exists.

Input contract

Input field	How to use it
emails	Required list of up to 100 email strings. Each item receives its own explicit result or run advisory.
maxConcurrency	Parallel DNS/MX checks from 1 to 50. A moderate value is appropriate for ordinary batches.

Recommended first Input

{
  "emails": [
    "test@gmail.com",
    "not-a-real-email",
    "someone@this-domain-zzq123.example"
  ],
  "maxConcurrency": 10
}

Start with this bounded example, inspect every Dataset field, and only then expand the scope. Input limits are product controls, not inconveniences: they make cost, completeness, and error handling visible.

Field dictionary

Field or group	Meaning
entityId, email, domain, inputRef, observedAt	Stable address identity and observation context.
syntaxValid, domainResolves, mxFound, mxRecords	Deterministic syntax and DNS/MX evidence used for the verdict.
provider, disposable, roleAccount, catchAllRisk	Provider and risk classifications that inform review but do not prove mailbox existence.
verdict, deliverabilityStatus, verificationLevel	Tiered result and the exact evidence level reached.
mailboxChecked	Always false in this Actor; it prevents domain-level evidence from being misread as mailbox verification.
confidenceScore, confidenceBand, confidenceReasons, confidenceRisks	Confidence in the stated DNS/syntax classification, not likelihood of engagement.
recommendedAction, actionPriority, actionReason, safeToAutomate	Separate deterministic suppression from manual mailbox verification.
failureType, retryable, recommendation	Operational failure handling, including DNS outages and budget advisories.
sourceEvidence, negativeSignals	Input and MX evidence plus explicit risk codes for downstream policy.

Common decision fields

Field	Operational meaning
recordType	The semantic row family. Use it to distinguish a business result from an advisory or terminal record.
schemaVersion	Version of the additive decision-intelligence contract. Pin or validate it in strict consumers.
entityId	Stable entity identity for deduplication and joins. It is not necessarily a legal identifier.
inputRef	The relevant submitted input reference after normalization.
observedAt	When the Actor observed or finalized the evidence. It is not necessarily the source publication time.
firstSeenAt and lastSeenAt	Always-emitted observation boundaries. Stateful monitors use the compatible baseline/current boundary. Stateless rows set both equal to observedAt for the current run; that equality does not establish historical tenure.
freshness	A structured statement about evidence age or availability, not a prediction. Its basis and age unit follow the source-specific field definition.
eventId	For monitors, the stable identity of one observed transition or monitor outcome. It is distinct from entityId.
before and after	For monitors, the bounded comparable snapshots used for the decision. Null means that side of a comparison was not honestly available.
changedFields and changeFlags	Machine-readable monitor deltas and normalized change labels. Empty arrays mean no supported changed field was established, not that every possible real-world fact stayed constant.
materialityScore and materialityBand	Magnitude of an observed monitor change when the Actor can calculate it. Materiality is separate from evidence confidence and may be unknown when the source lacks the required facts.
confidenceScore	Evidence support on a 0–100 scale. It is separate from materiality, lead score, or business value.
confidenceBand	Readable high/medium/low/unknown grouping of evidence support.
confidenceReasons	Observed facts that raise confidence.
confidenceRisks	Missing, partial, ambiguous, inferred, or conflicting aspects that reduce confidence.
confidenceConflict	Explicit consistency warning when structured evidence does not reconcile.
sourceEvidence	Source-linked observations supporting the row. Preserve this during export.
dataGaps	Important evidence the Actor did not observe or cannot establish. Keep these gaps visible in CRM, spreadsheet, and automation exports.
negativeSignals	Machine-readable risks or gaps. A negative signal is not automatically a negative business outcome.
recommendedAction	Bounded review label produced from the available evidence.
actionPriority	Suggested queue priority, not urgency guaranteed by the source.
actionReason	Plain-language explanation for the recommended action.
safeToAutomate	Whether the narrow recommended action is deterministic enough for automation. Organizational policy still applies.
failureType	Normalized terminal or partial failure classification. Null means no classified failure.
retryable	Whether a later retry may legitimately change an operationally incomplete result.
recommendation	Human-readable handling guidance, especially for terminal rows.

Evidence, confidence, and honest boundaries

What the evidence supports

• invalid_syntax is a deterministic formatting verdict under the Actor policy.
• domain_not_found means DNS confirmed non-existence at observation time.
• no_mx means no usable mail exchanger was found, including an explicit null-MX policy.
• valid_syntax_and_mx means the domain publishes usable mail infrastructure; it says nothing conclusive about the named mailbox.
• DNS infrastructure failures become unknown and retryable rather than false invalid addresses.

What this Actor never claims

• The Actor does not open an SMTP conversation and does not prove mailbox existence.
• It does not guarantee delivery, inbox placement, sender reputation, reply likelihood, identity, or consent.
• It does not identify a role address as a real person.
• It does not treat provider-level catch-all risk as a live per-domain catch-all test.
• It does not authorize outreach or replace suppression, privacy, and consent policy.

Reading data gaps correctly

A data gap is part of the result. Nulls, partial flags, confidence risks, source failures, and unavailable fields must survive export. Removing these fields makes the remaining facts look more complete than they are. When two sources conflict or a required identity cannot be proven, lower confidence and keep safeToAutomate=false.

Source evidence is not permission

A public source proves only that a value or statement was observable at the recorded time and URL. It does not establish consent, contractual rights, legal status, accuracy after observation, or authorization for a downstream action. Your organization remains responsible for source terms, privacy rules, outreach policy, retention, and human review.

Decision policy and action routing

Action	How to use it
SUPPRESS_INVALID_ADDRESS	A deterministic syntax failure can be removed from the active send queue.
SUPPRESS_UNROUTABLE_DOMAIN	A confirmed non-existent domain can be suppressed, with observation time retained for audit.
SUPPRESS_NO_MAIL_SERVER	A domain with no usable MX can be suppressed from email delivery workflows.
VERIFY_MAILBOX_BEFORE_USE	The domain can receive mail, but the named mailbox still requires an appropriate verification method.
REVIEW_RISK_AND_VERIFY_MAILBOX	Review disposable, role, or accept-all risk before any campaign decision.
RETRY_OR_REVIEW	Retry DNS infrastructure failures; do not delete or approve the address based on an unknown result.

Confidence is not attractiveness

confidenceScore answers “how strongly does the available evidence support this factual classification?” It does not answer “how valuable is this lead, property, account, or address?” A high-confidence negative fact may be commercially uninteresting; a low-confidence positive signal may deserve research but not action. Keep the concepts separate in dashboards, exports, and CRM fields.

Why safeToAutomate is conservative

safeToAutomate is intentionally false whenever the next step could amplify an uncertain inference. It may be true only for narrow deterministic actions explicitly supported by the row, such as suppressing an email with invalid syntax. A true value does not waive legal, privacy, consent, contractual, or organizational rules.

Retry policy

• Retry when retryable=true and the failure is operational, such as a temporary source or DNS problem.
• Do not endlessly retry deterministic invalid input, policy refusal, or confirmed absence.
• A retry must preserve the original input reference and must not create duplicate downstream actions.
• Budget exhaustion is not negative evidence about the entity. Resume only the unprocessed scope with an authorized budget.
• A failed Actor run is an operational event. Never transform it into “no listing,” “no contact,” “bad lead,” or “invalid email.”

Commercial use-case playbooks

1. Pre-campaign hygiene

Goal. Submit the approved address list, suppress deterministic failures, and send mail-capable-domain rows through a separate mailbox and consent review.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

2. CRM import audit

Goal. Classify new records before activation. Keep raw email, verdict, observation time, and action reason together for reversible decisions.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

3. Agency delivery report

Goal. Report syntax/domain/MX outcomes and unknown infrastructure failures separately. Do not market the service as mailbox verification.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

4. Role-account segmentation

Goal. Use roleAccount to route shared inboxes into a different messaging policy, not to infer lower quality or individual identity.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

5. Disposable-domain review

Goal. Flag bundled-list matches for human policy review because static lists can age and context matters.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

6. Provider migration observation

Goal. Compare provider/MX observations over time in your own stateful system, while remembering each run is a current DNS snapshot.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

7. Suppression-list maintenance

Goal. Record safeToAutomate only for deterministic negative verdicts. Preserve the reason and timestamp so decisions remain auditable.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

8. Retry queue

Goal. Retry only rows marked retryable after resolver failure or budget interruption; a retry is not needed for confirmed structural verdicts.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

Integration recipes

All examples use placeholders. Keep the Apify token in a secret manager and never write it into a Dataset, README, screenshot, or client-side application.

cURL: start a run and wait briefly

curl -sS -X POST 'https://api.apify.com/v2/acts/zinin~email-verifier/runs?waitForFinish=60' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data '{"emails":["test@gmail.com","not-a-real-email","someone@this-domain-zzq123.example"],"maxConcurrency":10}'

The run response includes defaultDatasetId. Read clean JSON rows with:

curl -sS "https://api.apify.com/v2/datasets/$DEFAULT_DATASET_ID/items?clean=true&format=json" \
  -H "Authorization: Bearer $APIFY_TOKEN"

JavaScript with apify-client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const input = {
  "emails": [
    "test@gmail.com",
    "not-a-real-email",
    "someone@this-domain-zzq123.example"
  ],
  "maxConcurrency": 10
};
const run = await client.actor('zinin/email-verifier').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems({ clean: true });

for (const row of items) {
    console.log({
        entityId: row.entityId,
        confidenceBand: row.confidenceBand,
        recommendedAction: row.recommendedAction,
        safeToAutomate: row.safeToAutomate,
        failureType: row.failureType,
    });
}

Python with apify-client

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("zinin/email-verifier").call(run_input={
    "emails": [
        "test@gmail.com",
        "not-a-real-email",
        "someone@this-domain-zzq123.example"
    ],
    "maxConcurrency": 10
})

for row in client.dataset(run["defaultDatasetId"]).iterate_items(clean=True):
    print({
        "entityId": row.get("entityId"),
        "confidenceBand": row.get("confidenceBand"),
        "recommendedAction": row.get("recommendedAction"),
        "safeToAutomate": row.get("safeToAutomate"),
        "failureType": row.get("failureType"),
    })

Apify MCP call

{
  "name": "call-actor",
  "arguments": {
    "actor": "zinin/email-verifier",
    "input": {
      "emails": [
        "test@gmail.com",
        "not-a-real-email",
        "someone@this-domain-zzq123.example"
      ],
      "maxConcurrency": 10
    }
  }
}

Generic webhook consumer policy

1. Trigger on a terminal Actor run event.
2. Confirm the run status is SUCCEEDED before reading business rows.
3. Retrieve rows from defaultDatasetId.
4. Reject or quarantine rows whose failureType is non-null unless your policy explicitly handles that failure.
5. Send safeToAutomate=false rows to a human-review queue.
6. Store entityId, observedAt, sourceEvidence, confidence, action, and the Apify run ID together.
7. Make retries idempotent by keying the destination on the stable entity ID plus the intended observation or event identity.

Where this fits in a practical stack

Destination	Recommended pattern
Apify Console	Use the visual Input form, start the run, then open the default Dataset overview. This is the fastest path for a one-off review and the best place to inspect evidence before automating anything.
Apify API	POST JSON input to the Actor run endpoint, wait or poll for completion, then read the default Dataset through the URL returned by the run object.
JavaScript client	Use apify-client from a Node.js service, pass the same JSON object as the Console Input, and preserve the returned run and Dataset IDs in your own audit log.
Python client	Use apify-client in a Python enrichment job, iterate Dataset items, and route rows by recommendedAction, confidenceBand, failureType, and retryable.
Make	Start the Actor from a scenario, wait for the run, retrieve Dataset items, filter unsafe or low-confidence rows, then insert review-ready rows into the destination application.
Zapier	Use an Apify run action or webhook trigger, fetch Dataset items, apply a Filter step, and send only review-approved fields into the next sales or operations step.
n8n	Use HTTP Request or Apify nodes, branch on failureType and retryable, keep a manual-review lane for safeToAutomate=false, and write sourceEvidence together with the business fields.
Google Sheets	Export the Dataset directly or append rows from an automation. Keep stable entityId as a hidden key so reruns update the correct record instead of creating ambiguous duplicates.
Airtable	Map entityId to a primary or deduplication field, store confidence and evidence in separate columns, and expose recommendedAction as the triage view.
Webhook	Configure an Apify webhook for terminal run states, retrieve the Dataset after SUCCEEDED, and treat FAILED or TIMED-OUT runs as operational events rather than negative business evidence.

A safe automation shape

The Actor is a collection and decision-support component. A production workflow should keep raw evidence, decision metadata, and business action in distinct layers:

1. Collect: run the Actor with explicit bounded input.
2. Validate: require a successful run and schema-valid Dataset rows.
3. Triage: branch on failureType, retryable, confidenceBand, and safeToAutomate.
4. Review: open source evidence for rows that may affect a person, campaign, investment, compliance decision, or customer record.
5. Act: execute only the action approved by your own policy and authorized operator.
6. Audit: retain run ID, Dataset ID, observation time, input reference, source evidence, and the final human decision.

This separation prevents a common automation error: turning “data was observed” into “a business action is justified.”

Operating guide

Before the first production run

1. Write the business question in one sentence: Which addresses have deterministic structural or mail-domain problems, and which still require mailbox-level verification before use?
2. Confirm every submitted input is within your authorized scope.
3. Use the prefilled small example and review all returned row types.
4. Map stable identifiers, confidence, evidence, actions, gaps, failure, and retry fields into the destination.
5. Establish a human owner for review exceptions.
6. Set a run budget and output bound appropriate to the test.
7. Verify that secrets are stored only in the platform or workflow secret manager.

After every scheduled run

1. Check terminal run status and logs.
2. Compare the number of submitted entities, produced business rows, and advisory rows.
3. Review partial, unknown, conflict, and low-confidence buckets.
4. Inspect a sample of source evidence, including at least one positive and one negative result.
5. Confirm the destination deduplicated on the intended stable key.
6. Verify that no downstream action was triggered from an error row.
7. Track cost per useful reviewed row rather than cost per raw request alone.

Production monitoring signals

Monitor source-unavailable rate, partial-row rate, low-confidence share, missing evidence, retry volume, run duration, Dataset row count, and spend. A sudden shift may indicate source drift, input drift, or an upstream outage. Stop automation and investigate before accepting a new pattern as business truth.

Cost control

Use a small mixed fixture containing a known mail domain, malformed input, and a reserved non-resolving domain. Confirm that your downstream policy distinguishes deterministic failures from unknowns before processing a real list. Current prices are shown in the live Apify panel.

Use maxTotalChargeUsd when calling a monetized Actor if your workflow supports it. Treat a buyer-set cap as a hard safety boundary. If the cap stops work, the unfinished items remain unprocessed; they do not become negative results.

Review templates and quality reporting

Row-review worksheet

For every material row, an analyst should be able to answer the following without relying on memory or an unstated assumption:

1. What submitted entity or query does this row refer to?
2. Is it a business result, a baseline/advisory row, a partial observation, or a failure?
3. Which exact source evidence supports the headline fact?
4. When was the evidence observed, and is there a different source publication time?
5. Which fields are direct observations, which are normalized, and which are deterministic derivations?
6. What important evidence is null, missing, partial, ambiguous, or conflicting?
7. Does confidence describe evidence support only, or has someone incorrectly treated it as business value?
8. What recommended action is present, and what additional verification does its reason require?
9. Is the narrow action marked safe to automate? If yes, does organizational policy also permit it?
10. What final human disposition was made, by whom, and from which run and Dataset item?

Field-group review prompts

1. entityId, email, domain, inputRef, observedAt

Contract meaning: Stable address identity and observation context.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

2. syntaxValid, domainResolves, mxFound, mxRecords

Contract meaning: Deterministic syntax and DNS/MX evidence used for the verdict.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

3. provider, disposable, roleAccount, catchAllRisk

Contract meaning: Provider and risk classifications that inform review but do not prove mailbox existence.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

4. verdict, deliverabilityStatus, verificationLevel

Contract meaning: Tiered result and the exact evidence level reached.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

5. mailboxChecked

Contract meaning: Always false in this Actor; it prevents domain-level evidence from being misread as mailbox verification.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

6. confidenceScore, confidenceBand, confidenceReasons, confidenceRisks

Contract meaning: Confidence in the stated DNS/syntax classification, not likelihood of engagement.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

7. recommendedAction, actionPriority, actionReason, safeToAutomate

Contract meaning: Separate deterministic suppression from manual mailbox verification.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

8. failureType, retryable, recommendation

Contract meaning: Operational failure handling, including DNS outages and budget advisories.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

9. sourceEvidence, negativeSignals

Contract meaning: Input and MX evidence plus explicit risk codes for downstream policy.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

Weekly quality report

Create a recurring internal report with these measures. The report is about pipeline health, not market demand unless the source contract explicitly measures demand.

Metric	Why it matters	Investigate when
Submitted inputs	Defines the actual denominator and scope of the run.	The count differs from the approved batch or schedule.
Business result rows	Shows how many usable observations were produced.	The rate changes sharply without an input explanation.
Advisory/failure rows	Prevents operational failures from disappearing in a results-only dashboard.	Any terminal class grows or is unmapped.
Partial-result rate	Measures incomplete source coverage or configured truncation.	It rises, or analysts stop seeing the partial warning.
Low-confidence rate	Shows the share of rows requiring more evidence.	It rises by source, cohort, or input pattern.
Retryable failure rate	Distinguishes temporary operational issues from deterministic outcomes.	Retries repeat without improving evidence.
Evidence-link coverage	Confirms material facts remain traceable after export.	Links or evidence objects are missing from delivered records.
Safe-automation share	Shows how little or much of the workflow can be deterministic.	A mapping change makes unsafe actions appear safe.
Manual-review backlog	Measures whether human verification capacity matches collection volume.	Rows age beyond the campaign or decision window.
Duplicate destination writes	Tests idempotency and stable identity mapping.	The same entity/run creates multiple external actions.
Cost per reviewed useful row	Relates platform spend to approved, decision-useful output.	Raw volume rises but reviewed utility falls.
Source-drift exceptions	Detects changed markup, response shape, policy, or source availability.	A new unknown pattern survives more than one bounded check.

Client-facing delivery note template

Use a note like this when delivering exports to a client or another team:

This Dataset contains bounded public-source observations produced by the Apify Actor for the submitted Input. Each row includes observation time, evidence confidence, recommended review action, and explicit gaps where available. A positive row is not proof of buyer intent, permission, legal status, future outcome, or any fact listed in the Actor's “never claims” section. Partial and failure rows are included so coverage is not overstated. Validate material rows at their source before acting.

Add the Actor URL, run URL, Dataset URL, build/version, exact Input scope, observation window, pricing model observed for the run, reviewer name, and date of approval.

CRM disposition vocabulary

Keep collection results and sales dispositions separate. A practical downstream vocabulary is:

• needs_evidence_review: useful signal exists but a reviewer has not approved it.
• needs_identity_review: entity or ownership association is not sufficiently proven.
• needs_policy_review: contact, privacy, suppression, legal, or contractual policy must be checked.
• approved_for_research: an analyst may perform more research; this is not approval for outreach.
• approved_for_authorized_action: a named operator approved one specific action under the organization's policy.
• retry_operational_failure: the source or infrastructure failed and a bounded retry is appropriate.
• closed_no_supported_signal: the completed bounded check found no supported signal; this is not a universal negative fact.
• closed_out_of_scope: the input should not have entered this workflow.

Never overwrite recommendedAction with the CRM disposition. The first is Actor-produced decision support; the second is your organization's accountable decision.

Sampling plan

For a new workflow, review every row in the first small run. When the contract is understood, sample all failure and partial rows plus a representative set of high-, medium-, and low-confidence results. Re-expand to full review whenever the source changes, the schema version changes, a new input cohort is introduced, the error distribution shifts, or a downstream user reports an unexplained result.

Change-management record

When you change field mappings or automation policy, record:

1. Previous mapping or rule.
2. New mapping or rule.
3. Actor build/version and schemaVersion used for validation.
4. Test run and Dataset URLs.
5. Positive, negative, partial, retry, and budget fixtures inspected.
6. Security and privacy review outcome.
7. Approver and activation time.
8. Rollback condition and responsible operator.

This makes a commercial data workflow supportable. Without the record, a later operator cannot distinguish a real source change from an undocumented mapping change.

Delivery patterns for marketing and small-business teams

One-off research

Run the Actor in Console, inspect the overview table, open evidence for each material row, and export only the approved subset. Record the run URL in the client or campaign notes.

Recurring watch or hygiene job

Use an Apify schedule. Write rows into a staging table keyed by entityId. Compare current and previous observations only when the Actor supplies valid state or your own pipeline implements an explicit comparable baseline. Never infer a change from a failed run.

Agency client delivery

Deliver three views: business results, evidence/quality exceptions, and operational failures. Include the run URL, observation time, configured scope, and a plain-language statement of what the Actor does not prove. This makes the deliverable auditable and reduces disputes caused by overclaiming.

CRM enrichment

Write into staging fields first. A human or approved policy promotes values into canonical CRM fields. Keep raw source values separate from normalized and decision fields, and do not replace a verified value with a lower-confidence observation.

AI-assisted review

An LLM can summarize rows, but it must receive the evidence, confidence risks, negative signals, and limitations. Require citations to sourceEvidence and prohibit invented identity, intent, legal, funding, mailbox, valuation, or availability facts.

Buyer and operator acceptance checklist

Use this checklist before calling the workflow production-ready.

Product fit

• The business question matches: Which addresses have deterministic structural or mail-domain problems, and which still require mailbox-level verification before use?
• The submitted entities were selected through an authorized process.
• A human owner understands the positive, negative, partial, and failure row types.
• The team accepts the boundaries listed in “What this Actor never claims.”
• The destination keeps evidence confidence separate from business scoring.

Input and run controls

• emails is explicitly reviewed and bounded.
• maxConcurrency is explicitly reviewed and bounded.
• The first production-like run uses a small representative sample.
• A maximum charge or internal spend alert is configured where appropriate.
• The workflow records Actor ID, build/version, run ID, Dataset ID, and input hash.

Data handling

• entityId is mapped to an idempotent destination key.
• observedAt and source-specific time fields remain distinct.
• sourceEvidence, gaps, and nulls are preserved.
• Advisory and failure rows cannot enter the positive-results lane.
• Low-confidence and partial rows have a visible manual-review view.
• Retention and deletion rules match the type of data collected.

Action safety

• recommendedAction is treated as a review label.
• safeToAutomate=false blocks automatic external action.
• Consent, suppression, legal, contractual, and platform rules are evaluated downstream.
• A reviewer can trace a material action back to source evidence and run metadata.
• Retry logic cannot duplicate a downstream action.

Ongoing quality

• The team monitors failure, retry, partial, low-confidence, and empty-result rates.
• A source-drift threshold pauses the workflow for inspection.
• Sample evidence is manually reviewed on a recurring basis.
• Cost per useful reviewed row is measured.
• Documentation and field mappings are updated when schemaVersion changes.

Frequently asked questions

Is this a database?

No. It is an on-demand observation tool. Each run collects or evaluates the submitted scope and records evidence at that time.

Does a found row prove commercial interest?

No. A found row proves only the factual observation described by its fields. Buyer intent is never inferred.

Can I automatically contact every result?

No. Use recommendedAction as triage, verify the evidence and identity, and apply your own consent, privacy, suppression, and outreach rules.

Why is safeToAutomate often false?

Because a useful observation can still require identity, context, legal, or source verification before action. Conservative routing prevents false certainty from scaling.

What should I do with low confidence?

Open confidenceRisks and sourceEvidence, close the important gap, or keep the row in a manual queue. Do not hide the confidence field.

What does partial mean?

The Actor obtained some usable evidence but could not support a complete observation of the configured scope. Partial is not the same as empty.

What is a confirmed zero?

Only an explicit source or deterministic rule can support a confirmed absence. An outage, truncation, or unreadable response is not a zero.

Should I retry every failure?

No. Retry only when retryable is true. Invalid input, policy refusal, or deterministic classification should be corrected or handled, not looped.

Can I delete failure rows?

You can exclude them from a business-results view, but retain them in operational logs so Dataset completeness and retry decisions stay explainable.

How should I deduplicate?

Use entityId for the entity and, for stateful monitors, eventId for the observed transition. Also retain the Apify run ID.

Can I treat confidence as conversion probability?

No. Confidence measures evidence support, not purchase probability, revenue, suitability, or expected return.

Can I change the recommended action?

Yes. It is an explainable default. Your downstream policy can be stricter, and should encode organization-specific authorization and risk tolerance.

How do I estimate cost?

Run the smallest representative input, inspect live event prices and run usage in Apify, then model the number of billable result events. The live pricing panel is authoritative.

Why use a small prefill?

It produces a cheap, fast, inspectable first run and reduces the chance of scaling a wrong input or workflow assumption.

Can I schedule it?

Yes. Use an Apify schedule, but make the destination idempotent and review changes in failure, partial, and confidence rates.

Can I export CSV or Excel?

Yes. Apify Datasets support common export formats. JSON is recommended when you need nested evidence and decision fields.

Can I send results to Sheets or Airtable?

Yes. Preserve entityId, confidence, evidence, gaps, actions, and failure fields instead of mapping only the headline value.

Can I use it from Make, Zapier, or n8n?

Yes. Start the Actor, wait for a successful terminal state, read Dataset items, then branch on decision and failure fields.

Can an LLM consume the output?

Yes, but pass the structured evidence and limitations together. Instruct the model not to invent missing facts and to cite sourceEvidence.

What happens when a source changes?

The run may become partial, unavailable, or fail validation. Monitor these rates and inspect logs before treating changed output as a real-world shift.

Does public mean unrestricted?

No. Public visibility does not remove source terms, privacy obligations, retention rules, or the need for a legitimate downstream purpose.

Is a source URL permanent?

Not necessarily. Store observation time and material facts because web content can change or disappear.

Can I rely on one row for a high-stakes decision?

No. High-stakes legal, financial, employment, compliance, safety, or personal decisions require appropriate primary evidence and qualified review.

How do I report a suspected parsing issue?

Provide the Actor run ID, a redacted input, affected field, expected source evidence, and whether the issue reproduces. Never include tokens or private data.

What does success mean?

A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.

Support information to include with an issue

Provide the public Actor name, Apify run ID, Dataset item index or stable entity ID, a redacted Input, the relevant source URL, expected behavior, observed behavior, and whether retrying produced the same result. Do not include an Apify token, API key, private customer record, or unnecessary personal data.

Final interpretation rule

A positive MX result proves only that the domain publishes usable mail infrastructure. The mailbox, person, consent, and final deliverability remain unverified.