Funding Alert — New Form D Filing Watch

Watch SEC Form D filings by company name or sector keyword and get only the filings that are NEW since your last check — not the same filings you already saw last week. Runs on our own live SEC EDGAR search engine under the hood.

What you get

• A named "watch" (company name or sector keyword) that remembers what it has already shown you.
• Every scheduled run reports ONLY filings that were not there last time — company name, CIK, filing date, form type and a direct link to the filing.
• Run several independent watches from one Actor (e.g. one for AI startups, one for biotech seed rounds) — each has its own memory.
• The very first run for a new watch tells you honestly that it's establishing a baseline, not hiding a "no new filings" run as if it found something.
• 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 company name or sector keyword into Search queries, optionally a name for this watch.
3. Hit Start. The first run creates the baseline; schedule it to run again (daily or weekly) to get a stream of only the NEW filings each time.

Pricing

Pay-per-event: $0.005 per run start + $0.01 per NEW filing / baseline created. The "no new filings" notice on a quiet run and any error/notice row are never charged. 10 new filings cost about $0.11 (the one-time start fee plus 10 billed rows).

Input

Field	Required	What it does
queries	yes	Company names or sector keywords, e.g. "artificial intelligence". Up to 25.
sinceDays	no	Lookback window for the underlying search, in days (1-730, default 90).
watch_name	no	Name for this watch, so you can run several independent watches.
limit	no	Filings requested per query, per run (1-100, default 20).
max_items	no	Max NEW-filing rows delivered (and charged) per run (1-200, default 20).

Migration: older API clients and saved Tasks may still send baseline_key. Runtime accepts that legacy alias only when watch_name is absent, preserving the same stored baseline. New integrations should use watch_name; an explicitly empty watch_name selects the documented fallback and never revives a legacy value.

{
    "queries": ["artificial intelligence"],
    "sinceDays": 180,
    "watch_name": "ai-startups"
}

Output

Baseline-created row (first run for a watch — real output, live run 30.07.2026):

{
    "baselineKey": "selftest-funding-2",
    "found": true,
    "isNew": false,
    "baselineCreated": true,
    "company": null,
    "ciks": [],
    "filedAt": null,
    "form": null,
    "accessionNumber": null,
    "url": null,
    "currentFilingsCount": 5,
    "newFilingsCount": null,
    "error": "",
    "summary": "First check for \"selftest-funding-2\" — baseline created with 5 current filing(s) matching the filter. Future runs report only NEW filings against this baseline.",
    "checkedAt": "2026-07-30T08:37:17.804Z"
}

New-filing row (real output, live run 30.07.2026):

{
    "baselineKey": "selftest-funding-2",
    "found": true,
    "isNew": true,
    "baselineCreated": false,
    "company": "UserFirst Software, Inc.",
    "ciks": ["0001698513"],
    "filedAt": "2026-06-29",
    "form": "D",
    "accessionNumber": "0001698513-26-000002",
    "url": "https://www.sec.gov/Archives/edgar/data/1698513/000169851326000002/0001698513-26-000002-index.htm",
    "currentFilingsCount": null,
    "newFilingsCount": 1,
    "error": "",
    "summary": "New filing for \"selftest-funding-2\": UserFirst Software, Inc. — Form D filed 2026-06-29.",
    "checkedAt": "2026-07-30T08:37:47.546Z"
}

Field	Meaning
baselineKey	Which watch this row belongs to.
found	true for a real result row (new filing or baseline-created); false for a notice/error row.
isNew	true only for an actual new-filing row.
baselineCreated	true only on the first-ever run for this watch.
company, ciks, filedAt, form, accessionNumber, url	The filing's own fields, straight from SEC EDGAR.
currentFilingsCount	Only set on the baseline-created row: how many filings matched at that moment.
newFilingsCount	Total new filings detected this run (may exceed the rows actually delivered if capped by max_items or your remaining budget).
error	Empty string when the check completed cleanly (including "nothing new"); non-empty only on a real problem.
summary	Human-readable one-liner.

Other tools we built

Related tools

Related tools for adjacent workflows in financial signals and markets.

Actor	What it does
Funding Round Tracker	Pair it in the financial signals and markets workflow: Track recent SEC Form D filings by company name or sector keyword — the notice a company files when it...
SEC Filing Watcher	Pair it in the financial signals and markets workflow: Watch stock tickers for new SEC filings — 10-K, 10-Q, 8-K, S-1 and more
Insider Trading Tracker	Pair it in the financial signals and markets workflow: Track insider stock trades — SEC Form 3/4/5 filings — for any list of tickers
Patent Filing Monitor	Pair it in the financial signals and markets workflow: Watch keywords or technologies for newly granted US patents
13F Portfolio Tracker	Pair it in the financial signals and markets workflow: Track institutional managers (hedge funds, family offices) by SEC CIK or name and get their recent 13F-HR...

FAQ / Limitations

Does this cover funding rounds outside the US? No — SEC Form D covers private placements filed with the US Securities and Exchange Commission only.

What happens if I change my queries for an existing watch_name? The watch keeps comparing against whatever it last saw, so changing the filter can produce a burst of "new" filings that simply weren't checked for under the old filter. Use a new watch_name for an intentionally different watch.

What this is NOT. This is not investment advice, and a Form D filing does not mean a deal has closed or is verified — it's a regulatory notice. Verify independently before acting on it.

Found a bug or need a custom watch? Issues on the Actor's page.

Commercial guide: Funding Alert — Stateful SEC Form D Filing Monitor

Watch company names or market themes for newly observed SEC Form D filing identities, with explicit baselines, issuer-attribution uncertainty, evidence, and retry-safe monitor outcomes.

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 Form D filing identities appeared after the last comparable watch run, and what identity and offering evidence must be verified before treating them as company funding signals?

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
Founder-led sales teams	Monitor a narrow company or category watch and route newly observed filings into a verification-first account-research queue.
B2B marketers	Use a recent filing identity as time-sensitive context without labeling the matched company funded or ready to buy.
Agencies	Operate independent client or theme watches while preserving baseline names, query scope, run URLs, and evidence limitations.
Market researchers	Track changes in a repeatable SEC search slice and distinguish baseline creation, quiet runs, and source failures.
Sales operations	Deduplicate on accession identity, keep monitor events auditable, and block unverified matches from automatic outreach.
Automation builders	Connect a stateful Form D observation stream to Sheets, Airtable, a CRM staging table, or a webhook review queue.

Input contract

Input field	How to use it
queries	Required company-name or sector-keyword list, up to 25 values. These are SEC full-text search terms, not verified issuer-to-domain identities.
sinceDays	Underlying filing lookback from 1 to 730 days. Keep it comfortably wider than the schedule interval so edge timing does not omit candidates.
watch_name	Stable namespace for one comparable baseline. Use a new name whenever the query definition changes intentionally.
limit	Maximum filings requested per query, from 1 to 100. This bounds source collection, not only delivered rows.
max_items	Maximum newly observed filing rows delivered and charged in one run, from 1 to 200.

Recommended first Input

{
  "queries": [
    "artificial intelligence"
  ],
  "sinceDays": 180,
  "watch_name": "ai-form-d-watch",
  "limit": 20,
  "max_items": 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
baselineKey, matchedQuery, inputRef	Watch identity and the submitted search context associated with the monitor outcome.
entityId, eventId, accessionNumber, ciks	Stable filing or watch identity plus an event identity for deduplication and audit.
found, isNew, baselineCreated, changeFlags	Explicit distinction between a new filing, first-run baseline, quiet run, and operational outcome.
company, filedAt, form, url	SEC search-result facts and direct source reference.
observedAt, firstSeenAt, lastSeenAt, previousObservedAt, filingAgeDays	Monitor and filing timing kept separate.
filingExistenceConfidence, issuerMatchConfidence, matchBasis	Strong filing-identity evidence separated from weak name/keyword-to-intended-company attribution.
fundingClaimStatus, materialityBand	Explicit statement that the result is not a verified funding round and that materiality is unknown without verified offering evidence.
confidenceScore, confidenceBand, confidenceRisks	Evidence support for the precise monitor statement, not an investment or conversion probability.
sourceEvidence, dataGaps, negativeSignals	SEC filing reference and unresolved issuer, domain, amount, round-label, and contact evidence.
recommendedAction, actionPriority, safeToAutomate, failureType, retryable	Verification-first routing and operational recovery semantics.

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

• A new-filing row means an accession identity was present in the completed current SEC search and absent from the stored watch baseline.
• The direct SEC archive URL, accession number, CIK values, filing date, and observation time are retained as source evidence.
• The first valid run creates a baseline and is never mislabeled as newly detected funding.
• A quiet completed run means no new filing identity appeared in that bounded watch comparison.
• Name or keyword matching is disclosed separately from filing existence so attribution uncertainty cannot be hidden by a high-confidence filing observation.

What this Actor never claims

• The Actor does not prove that the SEC issuer and the company or domain intended by the buyer are the same entity.
• A Form D filing does not prove a round closed, cash was received, the stated amount was sold, or a conventional seed/Series round occurred.
• The Actor does not provide investment advice, valuation, credit analysis, buyer intent, budget, contact identity, or permission to outreach.
• It does not cover non-US funding activity that is absent from the SEC Form D search scope.
• It does not support comparing incompatible query definitions under one baseline name.

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
VERIFY_ISSUER_IDENTITY_AND_FILING	Resolve legal issuer identity, CIK, company domain, filing document, amount fields, and commercial relevance before labeling or outreach.
SCHEDULE_NEXT_CHECK	Keep the same watch definition and run again; only a later comparable run can identify a newly observed filing.
NO_NEW_FILING_IDENTITY	Record the quiet bounded comparison without converting it into a claim that no financing activity occurred anywhere.
RAISE_BUDGET_AND_RESUME	Adjust the authorized charge cap and resume unfinished work without treating a budget stop as a market result.
REPAIR_BASELINE_STATE	Inspect watch state and restore a comparable baseline before accepting change events.
RETRY_SOURCE_CHECK	Retry a temporary SEC/source failure; never turn it into a no-filing observation.

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. Named-account funding context

Goal. Watch an exact legal-name term, verify any new result through CIK and the filing, then add the sourced event to an account-research brief.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

2. Sector watch

Goal. Monitor a narrow theme such as biotechnology, review each issuer individually, and never attribute a keyword match to a target domain automatically.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

3. Agency client watch

Goal. Give every client and query definition its own watch_name, retain run and Dataset URLs, and deliver verification status with each filing.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

4. Founder weekly review

Goal. Schedule a small watch, route only isNew rows into a research task, and keep baseline and quiet events in the audit log.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

5. CRM trigger staging

Goal. Deduplicate by entityId and eventId, write fundingClaimStatus visibly, and require a human approval field before promotion into campaign context.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

6. Market-monitoring brief

Goal. Report newly observed filing identities and query coverage; avoid describing the count as capital raised or completed rounds.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

7. Baseline change management

Goal. Create a new watch when queries or methodology change so a configuration edit cannot masquerade as a market event.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

8. Failure and budget lane

Goal. Branch on failureType and retryable, preserve the unfinished outcome, and prevent error rows from entering the lead stream.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

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~funding-alert/runs?waitForFinish=60' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data '{"queries":["artificial intelligence"],"sinceDays":180,"watch_name":"ai-form-d-watch","limit":20,"max_items":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 = {
  "queries": [
    "artificial intelligence"
  ],
  "sinceDays": 180,
  "watch_name": "ai-form-d-watch",
  "limit": 20,
  "max_items": 10
};
const run = await client.actor('zinin/funding-alert').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/funding-alert").call(run_input={
    "queries": [
        "artificial intelligence"
    ],
    "sinceDays": 180,
    "watch_name": "ai-form-d-watch",
    "limit": 20,
    "max_items": 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/funding-alert",
    "input": {
      "queries": [
        "artificial intelligence"
      ],
      "sinceDays": 180,
      "watch_name": "ai-form-d-watch",
      "limit": 20,
      "max_items": 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 Form D filing identities appeared after the last comparable watch run, and what identity and offering evidence must be verified before treating them as company funding signals?
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

Create one narrow watch with a small max_items cap, inspect the baseline event, then rerun with the identical watch name and queries. Verify event charges in the live run before scheduling a portfolio. The live Apify pricing panel is authoritative.

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. baselineKey, matchedQuery, inputRef

Contract meaning: Watch identity and the submitted search context associated with the monitor outcome.

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. entityId, eventId, accessionNumber, ciks

Contract meaning: Stable filing or watch identity plus an event identity for deduplication and audit.

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. found, isNew, baselineCreated, changeFlags

Contract meaning: Explicit distinction between a new filing, first-run baseline, quiet run, and operational outcome.

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. company, filedAt, form, url

Contract meaning: SEC search-result facts and direct source reference.

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. observedAt, firstSeenAt, lastSeenAt, previousObservedAt, filingAgeDays

Contract meaning: Monitor and filing timing kept separate.

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. filingExistenceConfidence, issuerMatchConfidence, matchBasis

Contract meaning: Strong filing-identity evidence separated from weak name/keyword-to-intended-company attribution.

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. fundingClaimStatus, materialityBand

Contract meaning: Explicit statement that the result is not a verified funding round and that materiality is unknown without verified offering evidence.

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. confidenceScore, confidenceBand, confidenceRisks

Contract meaning: Evidence support for the precise monitor statement, not an investment or conversion probability.

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, dataGaps, negativeSignals

Contract meaning: SEC filing reference and unresolved issuer, domain, amount, round-label, and contact evidence.

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?

10. recommendedAction, actionPriority, safeToAutomate, failureType, retryable

Contract meaning: Verification-first routing and operational recovery semantics.

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 Form D filing identities appeared after the last comparable watch run, and what identity and offering evidence must be verified before treating them as company funding signals?
• 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

• queries is explicitly reviewed and bounded.
• sinceDays is explicitly reviewed and bounded.
• watch_name is explicitly reviewed and bounded.
• limit is explicitly reviewed and bounded.
• max_items 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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.

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 new row proves a newly observed SEC Form D filing identity within a comparable bounded watch. It does not prove issuer-to-domain attribution, a closed funding round, amount raised, buyer intent, or permission to contact.